Looking for someone is very specific with information and gives great detail within their writing. Need good quality work. No plagiarism, honesty, and A++ work. Someone who will take their time to understand and follow given instructions carefully. Deliver work ahead of time and not have me asking and looking for expected assignment. If you have any questions about the assignment or unsure about something please ask. Instructions attached.
I have provided the sources that need to be used in the attached PDF files. Please use those sources. If you have any questions, please ask. I’ve also included a sample of how the work is to be done.
Assignment Instructions
Instructions: Submit an Annotated Bibliography of 5 sources. First, put the source in the correct citation format for your particular curricular division, and then write a brief annotation of that source.
The annotation should describe the main ideas covered in the source as well as an evaluation by you for the source’s usefulness for your project.
The Project is a Research Paper. The topic is Value of Reverse Logistics.
Remember that an online source can be a number of things. It can be a book, journal article, blog, podcast script, website, government report, newspaper article or editorial, or something else. Be sure to analyze each source carefully and follow the style guide in presenting the needed info. One goal in your annotation is to help your readers find the source if they want more information, so make it easy for them to do so.
APA Style Guide
Sample Annotation.
Sally Student
COLL 300
Date
Annotated Bibliography- MLA
Calkins, Lucy. Raising Lifelong Learners: A Parent’s Guide. Reading: Addison-Wesley
Longman. 1997. Print.
Lucy Calkins is a noted teacher and researcher in reading and writing. Her book is a guide for parents, helping them to work with their children’s schools to create a positive learning environment and a lifelong love of learning in their children. Topics covered include fostering learning and curiosity in mathematics, science, social studies, reading, and writing. Calkins’ work also offers advice on school curriculum and testing. By providing specific examples of parental involvement, this book will help support my assertion that parents need to play a strong role in their children’s education.
Reverse logistics disposition
decision-making
Developing a decision framework
via content analysis
Benjamin T. Hazen, Dianne J. Hall and Joe B. Hanna
Department of Supply Chain and Information System Management,
College of Business, Auburn University, Auburn, Alabama, USA
Abstract
Purpose – The purpose of this study is to identify the critical components of the reverse logistics
(RL) disposition decision-making process and suggest a decision framework that may guide future
investigation and practice.
Design/methodology/approach – The authors utilized a problem-driven content analysis
methodology. RL literature from 2000 through 2010 was content analyzed to determine which
components may impact a firm’s RL disposition decision.
Findings – The authors extrapolated seven RL disposition decision components from a compilation
of 60 variables identified in the literature. Practical implications and suggestions for future research
are offered, and a RL disposition decision-making framework is presented.
Research limitations/implications – Although methodological techniques were carefully
followed, the nature of a content analysis may be subject to author bias. Future investigation and
use of the framework presented will verify the findings presented here.
Practical implications – This study identifies seven components that should be considered when
deciding which RL disposition alternative should be adopted and integrates these components into a
decision-making framework. Supply chain professionals who refer to this framework during the decision
process will benefit from a more comprehensive analysis of potential RL disposition alternatives.
Originality/value – Congruent with recent assertions suggesting that RL research is evolving from
an operational-level focus to a holistic business process approach for maximizing value recovery, this
study synthesizes operational-level research to develop a practical framework for RL disposition
decision-making.
Keywords Reverse logistics, Returns, Disposition, Content analysis, Decision making
Paper type Research paper
Introduction
Managing return product flow is becoming increasingly important to the success of
supply chain firms, particularly as the volume of return flow substantially increases
(Guide Jr et al., 2006). Because $100 billion worth of products are returned in the USA
each year (Stock et al., 2002), the returns management process can be an integral part of
a firm’s supply chain (Rogers et al., 2002). Accordingly, returned product disposition
should not only happen quickly (Blackburn et al., 2004), but disposition
decision-makers must consider a variety of decision parameters to ensure that the
chosen disposition policy is the most advantageous for the organization. To date, no
study has investigated what components comprise the disposition decision-making
process. Thus, the purpose of this study is to identify relevant decision parameters
The current issue and full text archive of this journal is available at
www.emeraldinsight.com/0960-0035.htm
IJPDLM
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Received 14 October 2010
Revised 9 March 2011,
16 August 2011,
22 October 2011
Accepted 24 October 2011
International Journal of Physical
Distribution & Logistics Management
Vol. 42 No. 3, 2012
pp. 244-
274
q Emerald Group Publishing Limited
0960-0035
DOI 10.1108/096000312112
259
54
and create a framework that will help guide business decisions-makers and future
research regarding which disposition option to choose.
Much of the extant reverse logistics (RL) disposition literature seeks to optimize
operational processes. For example, much of the literature discusses various aspects of
managing returns for remanufacturing (Atasu and Cetinkaya, 2006; Inderfurth, 2005;
Lu and Bostel, 2007; Teunter et al., 2006; Webster and Mitra, 2007). However, Guide Jr and
Van Wassenhove (2009) posit that research in closed-loop supply chains (CLSCs) is
evolving from a technical focus on operational-level activities to a holistic business
process approach for maximizing value recovery. Additional research has noted the
importance of understanding the factors involved in carefully examining the impact of
the disposition decision on the rest of the firm (Blumberg, 1999). However, the literature
is sparse in the area of RL disposition decision-making and is therefore an area in need of
further study (Stock and Mulki, 2009). The current study capitalizes on the abundance of
operational-level research to lay the groundwork for future decision-making research.
Background
Literature on RL and the nature of decisions regarding RL processes is becoming more
abundant as the area matures from primarily a step-retracing supply chain process to
one that stands on its own as a necessary process to which management should pay close
attention. While some research begins to investigate considerations for pursuing returns
management activities in general (Rogers et al., 2002), a comprehensive understanding
of the elements inherent in the disposition decision has not been developed. Without
such an understanding, practitioners struggle to develop best practices and researchers
cannot provide support to them. Research is needed to adequately provide an
understanding of this area. The current study provides the foundation for that
understanding by identifying the key components of the RL disposition decision and
providing a framework for practice and future research.
We define the RL disposition decision as leading to the establishment of an
organizational policy regarding which recovery option to pursue for a specific product
or line of products. Because this decision should be made in accordance with a firm’s
current policies, market position and objectives, we propose that the RL disposition
decision requires great consideration. RL is comprised of all functions that begin with
acquiring a returned product and end when the owning firm has extracted all possible
value from the item through proper disposition. This disposition process includes
options ranging from simply reusing the product to properly disposing of the product.
Much literature has been devoted to identifying and describing the actual disposition
alternatives, such as reuse, recycling, and remanufacturing (Blackburn et al., 2004;
Carter and Ellram, 1998; Krikke et al., 2004; Thierry et al., 1995). Some of this research
has also addressed a variety of design considerations for reverse channels, such as
investigating the tradeoff between efficient and responsive reverse supply chains
(Blackburn et al., 2004) and modularity (Krikke et al., 2004). However, research regarding
which RL disposition alternative should be employed by a given firm for a given product
line is markedly absent in the literature. When determining a policy regarding how to
handle returns, it would behoove decision-makers to follow an established
decision-making process that takes into account all relevant considerations. To date,
no study has assimilated all of these considerations or attempted to create such a
decision-making framework. This lack of understanding makes it difficult to fully
RL disposition
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245
comprehend which issues are important when making these increasingly important RL
decisions. To determine a foundation for such an understanding, this research uses
content analysis of RL literature to identify RL disposition decision criteria.
The remainder of this article is structured as follows. First, we briefly
review foundational RL literature, where we describe how numerous factors
have been acknowledged in extant literature to affect the RL process. We then offer
a brief background of the four disposition alternatives that are generally described in
the literature. The methodology of the content analysis is then described, followed by a
discussion of the findings. Next we present a validity check of our findings, where
components derived from our analysis are compared with factors addressed in existing
RL frameworks. We then offer a discussion of the practical implications of our results,
which also describes ideas for future research in RL decision-making. Finally, we
integrate our findings with extant decision-making literature to create a RL disposition
decision-making framework.
RL defined
A review of supply chain management (SCM) literature reveals that some terms often
encompass numerous definitions. Notably, the terms “logistics” and “SCM” lack
universal definitions as multiple conceptual perspectives exist (Larson et al., 2007;
Stock and Boyer, 2009). Similarly, there is not a consensus in the literature regarding
the terms used to describe the reverse processes within the supply chain (Lambert,
2008). Therefore, the term “RL” will be used in this research to encompass all returns
processes and is synonymous with terms such as “closed-loop supply chain” or
“returns management”kopic. For the purpose of this paper, we adopt Stock’s (1998,
pp. 20-1) comprehensive definition of RL:
[. . .] from a business logistics perspective, the term refers to the role of logistics in product
returns, source reduction, recycling, materials substitution, reuse of materials, waste disposal,
and refurbishing, repair, and remanufacturing; from an engineering logistics perspective, it is
referred to as reverse logistics management (RLM) and is a systematic business model that
applies best logistics engineering and management methodologies across the enterprise in
order to profitably close the loop on the supply chain.
Foundational literature
A variety of internal and external forces affect a firm’s RL processes. Building upon
previous marketing research (Achrol et al., 1983; Stern and Reve, 1980) and their review
of the logistics literature that specifically addresses external marketing factors
(Barry et al., 1993; Bronstad and Evans-Correia, 1992; Cairncross, 1992; Kopicki et al.,
1993; Livingstone and Sparks, 1994; Murphy et al., 1995; Pohlen and Farris II, 1992;
Stock, 1992), Carter and Ellram (1998) developed a framework that describes the forces
that affect RL. Their framework posits that the task environment consists of four
distinct organizational entities that affect the firm’s RL operations. They are:
(1) suppliers (input);
(2) buyers (output);
(3) government (regulatory); and
(4) competitors (competitive).
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The task environment is embedded within the overall market environment consisting
of legal, economic, political, and social variables.
Carter and Ellram’s (1998) conceptual model is widely regarded as the first
comprehensive RL framework. It takes into account factors that are beyond the normal
scope of logisticians and illustrates the holistic nature of RL. Their work provided the
foundation for further investigation, as demonstrated by Knemeyer et al. (2002) when
they updated the model to account for factors recognized in more current research.
Considering the theory-building work of Dowlatshahi (2000) and a review of the
contemporary literature at the time, Knemeyer et al.’s (2002) model accounts for concerns
at multiple levels of an organization. Similarly, Skinner et al.’s (2008) research suggests
that cross-functional integration is critical to the continued success of the returns
management process. Additionally, Jayaraman and Luo (2007) recognized the
system-level effects of a firm’s RL policies. Their framework describes the
interdisciplinary nature of the RL disposition decision and demonstrates how a firm
may derive value from its returned products, thus promoting the idea that all of a firm’s
activities should seek to increase profits.
Considerations regarding RL encompass issues beyond that of many other
business processes. For example, De Brito and Dekker’s (2003) model emphasizes
corporate citizenship, legislation, and economics as the driving forces behind RL
practices. Furthermore, Rogers and Tibben-Lembke (2001) highlight the overlapping
considerations between green logistics and RL, suggesting the impact that green
principles may have on RL decision-making. Specifically, they describe the
activities of recycling, remanufacturing, and use of reusable packing as overlapping
between green logistics and RL. Conversely, Wolf and Seuring (2010) suggest that,
although environmental concerns are often considered when organizations contract
with third party logistics providers, those concerns are given cursory examination at
best. Research also suggests that understanding customer needs regarding returns may
enable organizations to develop better product placement strategies (Ofek et al., 2011).
In that same vein, Jack et al. (2010) found that RL may be examined from both the
viewpoint of front-end customer relationship strategies and back-end RL processes.
Their research suggests that back-end processes have a positive impact on RL
capabilities, which in turn increases cost savings.
The above research highlights the dynamic nature of RL and underscores the
importance of identifying factors that impact the general RL process. Not specifically
addressed, however, is the disposition decision. Whether or not to implement RL
processes is not an issue; RL will endeavor to exist for organizations that produce or
move materials through the forward supply chain. While there is some need to align
the RL process with the rest of the organization’s supply chain objectives, only the
disposition decision has the ability to add value to the organization when the decision
is based on appropriate information (Tan and Kumar, 2006). The research reviewed
thus far describes the nature of RL. Our focus now turns to describing the common RL
disposition alternatives that a firm may employ.
RL disposition alternatives
In this paper, the term disposition alternative is synonymous with what some authors
have referred to as recovery option (Krikke et al., 2004). Deciding upon the most
advantageous disposition alternative can bolster a firm’s success (Croxton et al., 2001).
RL disposition
decision-making
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However, not only do decision-makers need to understand the laws and regulations
that govern proper material handling and disposal, they must also be able to recognize
potential financial gains that may be realized by capitalizing on opportunities to reuse
operational products, recondition damaged or used products, or recover valuable
materials from products that are beyond their useful life.
Early work in regard to disposition decision models sought to identify and
stratify disposition alternatives (Kopicki et al., 1993; Stock, 1992). Thierry et al.’s (1995)
integrated supply chain model depicts a standard return process. Their model illustrates
three separate disposition alternatives. First, a firm may employ direct reuse, which
entails reusing or reselling the returned product in an as-is condition. Next, a firm may
employ product recovery management, which entails processes such as repairing,
refurbishing, remanufacturing, cannibalizing useable materials, and recycling materials
of value. Finally, a firm may employ waste management, which entails incinerating
waste or land filling.
Since the work of Thierry et al. (1995), others such as Carter and Ellram (1998),
Krikke et al. (2004) and Rogers et al. (2002) have modified and stratified possible
disposition alternatives. Although each study emphasized slightly differing alternatives
and definitions, four common RL disposition categories seemingly emerge as
comprising the core taxonomy in recent literature. In consideration of the work cited
above, we propose that the following four disposition alternatives encompass the
recovery options available for RL. In hierarchical order in regard to the potential residual
value that can be recovered by a firm, the four alternatives are:
(1) reuse;
(2) product upgrade;
(3) material recovery; and
(4) waste management.
Reuse allows for the most value to be recovered while waste management allows for
the least amount of value recovery. Although some decompose these four alternatives
even further (Krikke et al., 2004), this general hierarchy is often utilized in the current
literature (Blackburn et al., 2004; Prahinski and Kocabasoglu, 2006; Rogers et al., 2002;
Staikos and Rahimifard, 2007). The following briefly defines each alternative and gives
an example of research within each area.
Reuse. Direct reuse is an option that presents itself when a customer returns an
unused product back to the place of purchase, thus inserting the product back into the
supply chain for use. At the retailer level, once the product is no longer serviceable or
requires some sort of upgrade (e.g. cleaning, replacing accessories, remanufacturing,
repackaging, etc.) direct reuse is no longer an option. Generally, this option exists only
if the location in which the product resides in the supply chain possesses the capability
to return the product to retail condition. This process includes products that are
completely unused and products that are returned after such light use that upgrade is
not required in order to return the product to new status.
Assuming that the returned product is in new condition, a variety of options exist.
The product can be again offered for sale by the retailer, shipped laterally to another
retailer, shipped back to the distributor, or shipped to any other place within the forward
or reverse supply chain where stock levels require such an item. Logisticians and retail
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managers are primarily concerned with accounting for the quantity and quality of these
returns. These unknown quantities provide even more variability to a process that is
already susceptible to forecasting error. This additional variability increases the
bullwhip effect within the supply chain and can lead to increased inventory (Vlachos and
Dekker, 2003). If returns can be adequately forecasted and properly managed, the
returns that are available for direct reuse can reduce transportation, procurement, and
storage costs while improving productivity as each item returned to the end of the
supply chain offsets the need for another item to be pulled through the forward supply
chain (Giuntini and Andel, 1995b; Mollenkopf and Closs, 2005).
Product upgrade. The product upgrade alternative is concerned with repairing,
refurbishing, or remanufacturing an item in order to extend the life of and derive value
from the original core unit (Krikke et al., 2004). Product upgrade becomes an option
when the possibility of direct reuse is either no longer available (e.g. the product is in
used condition) or not economical (e.g. there is no longer a market requirement for the
product). If executed properly, product upgrade can create profitable business
opportunities through recapturing value that would otherwise have been lost
(Clendenin, 1997; Giuntini and Andel, 1995a). The term “upgrade” implies improving
the product from its end-of-life condition to that of a condition acceptable for future use
or sale. The condition and quality of upgraded products can vary greatly, depending
on the upgrade technique chosen and the purpose of the upgrade.
The definitions of repair, refurbish, and remanufacture are debatable and the usage
of such terms differs within the literature. However, Majumder and Groenevelt (2001)
suggest that remanufacturing is the primary means of product upgrade and is the term
usually assigned to any upgrade function. Remanufacturing is defined as:
[. . .] the process of disassembling used items, inspecting and repairing/reworking the
components, and using these in a new product manufacture. A product is considered
remanufactured if its primary components come from a used product (Majumder and
Groenevelt, 2001, p. 125).
The current study adopts this definition.
Material recovery. Material recovery involves recovering any portion of a returned
product that may contain value. Material recovery can entail cannibalizing entire pieces
not requiring upgrade that can be reused (Krikke et al., 2004), recovering parts or pieces
that may be reused (Blackburn et al., 2004), or extracting recyclable materials for reuse or
to sell as a commodity. Early RL literature often focused on recycling (Guiltinan and
Nwokoye, 1975; Pohlen and Farris II, 1992); thus, some scholars posit that RL has been
most closely associated with recycling and environmental matters (Daugherty et al.,
2002). Although the topic of sustainability is becoming popular in recent literature,
determine how to extract value from returned products and ensure regulatory
compliance are still prominent topics in this area (Roy et al., 2006).
Waste management. Once a firm has decided that it is no longer of value to reuse,
upgrade, or recover materials from a specific product, the product then becomes waste.
Lyons (2005, p. 71) defines waste as:
[. . .] something that is perceived to have either no inherent value to its owner, or the amount
of effort required to access that value is greater than the expected return [. . .] waste is a
residual that is discarded.
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249
In regard to the disposition decision, relegating a product to waste entails deriving no
more value from that product. Subsequently, this alternative is the least desirable
disposition alternative as the business implications end at this juncture. However, waste
management has become an important topic in recent years as economic and
environmental forces demand environmentally-friendly and cost-effective handling
of waste.
The preceding four disposition alternatives and their operational definitions
assimilate the extant literature in this area for the purpose of bringing understanding
to the options available to a firm when making the disposition decision. As noted
above, the exact terminology and definitions of the alternatives differ within the
literature. Readers interested in the disposition alternatives (or recovery options) are
encouraged to Blackburn et al. (2004), Carter and Ellram (1998), Kopicki et al. (1993),
Krikke et al. (2004), Prahinski and Kocabasoglu (2006), Rogers et al. (2002), Staikos and
Rahimifard (2007), Stock (1992) and Thierry et al. (1995).
While much literature exists in the general area of RL, and a literature base is being
built in disposition alternatives, no comprehensive analysis of extant literature for the
purposes of extracting or deriving decision considerations or creating a disposition
decision-making framework has been conducted. Because of the relative lack of
literature specifically in the area of dispositions, our study analyzes literature in RL
that addresses decision-making in general to determine those variables that
appear throughout the literature. Then, we synthesize those findings into higher-level,
decision-making components, which are applied to the disposition decision. The method
used for identifying and assimilating these components is described next.
Methodology
The purpose of this study is to identify the components of the RL disposition decision
and suggest a decision-making framework. To serve this purpose, the authors required a
method that would uncover the variables considered in extant RL decision-making
literature. In short, we needed to derive meaningful content to address our specific
purpose from a large amount of textual literature. Berelson (1952) suggests that
revealing the focus of attention is one of the primary uses of content analysis. In addition,
Neuman (2006) asserts that content analysis is useful for three primary types of research
problems:
(1) problems involving a large amount of text;
(2) problems that must be studied from afar because of either necessity or to attain
the proper scope; and
(3) problems where casual observation may not reveal the proper solution.
In the case of this research, we needed to assimilate a large amount of text to
thoroughly investigate relevant variables. When viewed from afar and via conscious
examination, this text may reveal a solution to our problem. Accordingly, a content
analysis method was adopted for this study.
Content analysis is “a research technique for making replicable and valid inferences
from texts (or other meaningful matter) to the contexts of their use” (Krippendorff, 2004,
p. 18). Content analysis can be strictly quantitative when used to objectively and
systematically count and record symbolic content from text. Content analysis can also
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be utilized for qualitative purposes to interpret meaning from text. Indeed, many content
analysis procedures employ both qualitative and quantitative elements (Holsti, 1968).
As with many research methods, content analysis encompasses a wide variety of
procedures and techniques that may be employed for use in a variety of settings to
solve a multitude of problems. As such, there is no one systematic checklist to follow
when conducting content analysis. This lack of standardization requires that great care
be taken to develop a specific method that will yield appropriate results to answer the
given research question at hand. Regardless of the specific procedure employed, the
techniques must result in findings that are replicable in order to attain sufficient levels
of reliability. In addition, measures must be taken to enhance validity whenever
possible. Although discussion of the many ways in which to bolster reliability and
validity are beyond the scope of this manuscript, we will discuss the specific
techniques we employed in our research later in this article.
In this research, we adapted procedures for problem-driven content analysis
suggested by Krippendorff (2004) to locate relevant materials for analysis, define the
units of analysis, develop recording procedures, present the findings, infer results, and
demonstrate empirical validity. These steps were carefully chosen and meticulously
performed to efficaciously derive meaning from the textual content while enhancing
reliability and validity to the fullest extent. The procedures and outcomes of each of
these steps will be discussed throughout the remainder of this article in the order in
which they were completed.
Relevant materials
As discussed previously, the vast majority of extant RL literature addresses
operational-level concerns of an organization. With a focus on optimizing specific
operational efficiencies, much of this research is aimed toward clarifying various
aspects of decisions that pertain to RL. As an example, Bhattacharya et al.’s (2006)
research develops a mathematical model to determine optimal order quantities;
Ferguson and Toktay’s (2006) research aims toward facilitating remanufacturing
decisions. As such, this literature represents a rich body of content that is relevant to
our problem, and thus useful for our analysis.
The scope of the literature search was limited to articles that designed, developed,
tested, or otherwise utilized a decision support system (DSS) or simulation in regard to
facilitating a decision within RL. Authors of these articles go to great lengths to
identify and describe any possible variable or consideration that may be used in regard
to their specific RL problem in order to enhance the relevance and validity of their
research. To find articles that meet our criteria, the literature from the top eight
journals in SCM, management information systems (MIS), and operations management
(OM), as identified by Menachof et al. (2007, p. 151), Rainer and Miller (2005) and
Gorman and Kanet (2005), respectively, was first considered. In alphabetical order by
discipline, these journals are shown in Table I.
These journal listings yielded a total of 21 unique journals because of the
interdisciplinary nature of both Management Science and Harvard Business Review.
Logistics research spans a multitude of disciplines (Stock, 1997). However, the three
selected disciplines encompass the vast majority of literature on the specific topic of RL
decision-making and are therefore thought to appropriately limit the scope of the
search. Although searching only the top journals in a field may not render exhaustive
RL disposition
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results (Webster and Watson, 2002), a comprehensive interdisciplinary analysis of this
nature requires a limited scope in the preliminary search of literature. Furthermore,
this listing served more as a beginning reference than as a definitive boundary.
Investigation into the literature revealed additional journal titles that pertained to this
topic and were subsequently explored.
This review examined all applicable literature from 2000 through 2010. In their
review of RL literature, Carter and Ellram (1998) propose that the first academic work
in the field was not published until the early 1990s (Kopicki et al., 1993; Stock, 1992).
Their review also notes that the majority of literature throughout the 1990s was
exploratory in nature, offering little theoretical grounding. Accordingly, the vast
majority of RL literature is published after the year 2000, thus presenting a logical limit
to the scope of this review. The authors’ objective was to determine which variables are
currently being utilized in the literature. This dictated that the review reach back far
enough to provide an appropriate number of articles, but not so far as to lose
contemporary relevance. The year 2010 was chosen as an upper limit so as to facilitate
a comprehensive search of a selected period, thus limiting the possibility of
inadvertently omitting newly published literature within the stated scope of the review.
All selected journals are searchable via electronic format and were thus accessed
electronically. Broad keyword searches of each journal were used to generate a large
number of search results that the authors were then able to evaluate more closely. The
keywords used were: CLSC, end-of-life, return, disposition, RL, decision, model, and
simulation. Titles and abstracts were then reviewed to find any literature that developed
a DSS or simulation within RL. Specific articles not available in electronic format were
requested and received through inter-library loan. The research process yielded
73 articles that the authors were able to read and analyze for adherence to the established
criteria of utilizing a DSS or simulation within RL for the purpose of decision-making.
Rudimentary citation analysis of the original articles directed the authors toward other
journals which, although not encompassed within the original search, were deemed to be
highly applicable to this study. These additional journals are discussed below.
Supply chain management
Management information
systems Operations management
Harvard Business Review Communications of the ACM IIE Transactions
International Journal of Logistics
Management
Decision Sciences Journal of Scheduling
International Journal of Physical
Dist & Logistics Management
Decision Support Systems Manufacturing and Service
Operations Management
Journal of Business Logistics Harvard Business Review Mathematics of Operations
Research
Journal of Operations
Management
Information Systems Research Management Science
Management Science Journal of Management
Information Systems
Operations Research
Supply Chain Management
Review
Management Science Production and Operations
Management
Transportation Journal MIS Quarterly Transportation Science
Table I.
Top journals by field
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Much of the literature in this area is in the OM field, which prompted further
exploration into more of these top journals. Thus, we searched the additional journals
found in Gorman and Kanet’s (2005) ranked listing of OM journals. The same search
procedure was performed on the additional journals; this yielded a total of 25 additional
articles. In total, 98 articles that potentially met our criteria were found and acquired
via inter-library loan or downloaded in full from an electronic database. However, upon
close examination of the 98 articles, only 62 met the specific criteria and were reviewed.
A listing of the 62 articles used for content analysis can be found in the Appendix.
Unit of analysis and recording
Krippendorff’s (2004, p. 83) first component of content analysis involves unitizing,
which he defined as “the systematic distinguishing of segments of text – images,
voices, and other observables – that are of interest to an analysis”. In this study, we are
interested in the considerations used in RL literature. Accordingly, our unit of analysis
was the individual variable or parameter (we will refer to these as “considerations”)
utilized in the research.
In regard to content analysis, a primary purpose of recording is to transform
original texts into analyzable representations (Krippendorff, 2004). In respect to
maintaining consistency, this stage of the content analysis was conducted by only one
of the authors. The author began the recording process by searching each article for the
considerations used in the simulation. The author investigated model explanations,
discussions of variables, parameters, assumptions, and other applicable areas of the
selected literature to extract and tabulate all considerations addressed in the respective
article. In sum, the author simply recorded the variables considered in each study, as
stated explicitly in the journal article. Each consideration and the article of origin were
then recorded on a spreadsheet. Articles had as few as five and as many as
18 considerations each. A master listing of findings was compiled on a spreadsheet
document such that the authors could analyze the findings.
Findings
The content analysis yielded a total of 60 considerations used by the authors of the
selected literature. These considerations are shown in Table II, in order of most
frequently utilized (top left) to least frequently utilized (bottom right). The number next
to each consideration represents how many different times the consideration was
identified during the analysis of the articles.
Inferred results
Abductively inferring contextual phenomena from textual data bridges the gap
between descriptive accounts of the text and what the data actually mean by pointing
to unobserved phenomena of interest to the analysts (Krippendorff, 2004). At this point
in the content analysis, we had compiled and counted a listing of considerations
without assigning meaning to the data. Subsequently, we next reviewed this data to
extrapolate autocorrelation functions. Upon reaching agreement, seven general factors
were extrapolated from the listing of 60 variables. These factors are: supply chain
capabilities, costs of RL, profit from RL, environmental impact of RL, regulation,
market considerations, and customer behavior.
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In the literature used for analysis, supply chain capabilities encompass a myriad of
variables that address whether or not a firm is prepared to commence various RL
operations from a logistical perspective. Examples of such variables include a firm’s
remanufacturing and inventory capacity. Therefore, we define supply chain
capabilities as a firm’s existing resource capacity available for RL activities.
Cost of RL was represented in the literature in a variety of ways, to include labor and
administrative costs. As such, we define cost of RL as the costs incurred to facilitate
effective RL activities. Similarly, profit from RL was represented with variables such as
revenues from recycling and profit margins from RL activities. We therefore define
profit from RL as any profit realized via the employment of RL activities.
We found several instances where the environmental impact of RL was considered
in the literature (Aksen et al., 2009; Haas et al., 2003; Staikos and Rahimifard, 2007). We
define environmental impact as any consequence (positive or negative) of the practice
of RL activities to the natural environment. Although regulation is often developed to
address environmental concerns, environmental issues are not the only drivers
of regulation. As such, we consider regulation to be separate from environmental
Customer Demand (40) Manufacturing Capacity (13) Outsourcing (3PL/4PL) (6)
Product Return Volume/Rate (38) Wholesale Price (12) Environmental
Considerations (6)
Remanufacturing Costs (32) Number of OEMs (Monopoly,
etc.) (9)
Length of Time Customer
Holds Product (5)
Cost of Acquiring Returned
Product (30)
Existing Logistics Infrastructure
(9)
Cost of Capital (5)
Management Strategy/Policy (23) Salvage Value (12) Safety stock (4)
Inventory Costs (23) Supply of Parts Required for
Remanufacture (12)
Stocking points (4)
Disposal Considerations/Scrap
Costs (23)
Market Size (11) Service Level (3)
Leadtime (22) Quality of Remanufactured Item
(11)
Factory Location (3)
Retail Price (22) Disassembly (Cost/Time) (11) Number of Remanufacturers
(3)
Transportation Costs (21) Remanufactured Item Inventory
Level (11)
RL Administrative Costs (3)
Manufacturing Costs (20) New Item Inventory Level (9) Processing Times (2)
Inspection Costs (20) Delays (9) Packaging (2)
Remanufacturing Capacity (18) Recycling Costs (8) Discontinuation Price (2)
Sales Lost/Backorder Costs (18) Demand for Remanufactured
Part (8)
Customer Segment (2)
Remanufactured Item Sales Price
(17)
Revenue from Recycling (8) Value of Time (2)
Profit Margin of Remanufacturing
(15)
Lot/Batch Size (7) Penalty Costs of Uncollected
Returns (2)
Product Lifecycle (15) Labor Cost (7) Sorting Policy (1)
Fixed Costs (15) Quantity Recycled (7) Total Quantity of Items in
Supply Chain (1)
Return Quality (14) Pattern of Recovery (Collection
Location) (7)
Total Cost of RL (1)
Total Serviceable Item Levels (13) Legal Considerations (7) Forecast (1)
Table II.
Variables used in
quantitative RL literature
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impact and found that regulation is often addressed in the literature in regard to legal
constraints and compliance with environmental law. We define regulation as any law
or directive imposed by a governing body that influences RL.
Market conditions in the literature include the size of the market and number of
competing firms. We narrow the definition of market conditions to entail the
competitive forces realized via the existence and actions of industry competitors. In
respect to Porter’s (1980) five forces, this includes the threat of new entrants, threat of
substitute products, the bargaining power of suppliers, and rivalry among existing
firms. Notably, this does not include the impact of customers. Our definition of market
conditions differs in scope from existing conceptualizations in the supply chain and
marketing literature and represents a fundamental difference between the forward and
RL process. Extant supply chain literature suggests that market conditions are largely
affected by customer demand (Fisher, 1997; Pagh and Cooper, 1998). In contrast, in a
CLSC, we posit that customers encompass a role beyond that of simply placing a
demand on the supply chain or wielding bargaining power. For example, in the CLSC
literature, we found that the length of time a consumer holds a product and the
condition in which the product is returned are important considerations (Geyer et al.,
2007; Toktay et al., 2000; Vorasayan and Ryan, 2006). Thus, we separate customer
behavior into its own category apart from market conditions in the RL context. We
define customer behavior as any action taken by a customer that impacts a firm’s RL
activities. For example, customer demand for reused, recycled, or remanufactured
products and the willingness to return used or unused products to the supply chain
constitute important customer behaviors.
These analytical constructs defined above (which we will refer to as components of
the disposition decision) were extrapolated from the literature and used as the basis for
categorization of variables based upon our knowledge and experience with the
established theories of SCM. The extrapolation of these seven components suggests that
these factors embody the considerations required for RL disposition decision-making.
However, in order to qualify these results, empirical validity was tested.
Empirical validity
In order to measure how much these components converge with and are discriminate
from current RL literature, we evaluated current RL frameworks and compared the
findings of the content analysis with the factors addressed in each framework. In this
section, we discuss our literature search procedures for finding these RL frameworks,
our evaluation of each framework, and the conclusions we drew regarding the validity
of our findings.
Literature search
The criterion for a usable article was simple and explicit: it must contain an RL
framework. Although a rather broad parameter, an exhaustive search for such literature
yielded very few results. Again using listings provided via the research of Menachof et al.
(2007), Rainer and Miller (2005) and Gorman and Kanet (2005) as a starting point, the top
SCM, MIS, and OM journals were searched. Keyword search terms were: closed-loop,
end-of-life, return, disposition, RL, decision, model, and framework. The scope of this
review in regard to range of dates was limited only by the start date, which was 1998.
This date was chosen as this was the year of Carter and Ellram’s (1998) initial
RL disposition
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255
framework publication. Although numerous articles were retrieved and reviewed for
adherence to criteria, this process yielded just seven usable articles. In an attempt to find
more literature, we conducted a broad search of the Business Source Premier and
ABI/Inform databases. The same keyword searches generated a variety of additional
articles. However, only four additional articles met the criteria of this search.
Evaluating frameworks
The exhaustive search detailed above yielded 11 articles which met our criteria. In
order to validate our results, the frameworks from these articles were evaluated to
determine percentage of agreement with the considerations identified by our content
analysis. Each of the three authors evaluated the frameworks and identified the areas
addressed in each framework. Krippendorf’s a for reliability of this process was
calculated to be 0.85, which is considered sufficient to draw meaningful conclusions
(Krippendorff, 2004). The results of this comparison are shown in Table III, which
indicates the considerations encompassed by each framework.
Correlative validity analysis
Convergent validity is “the extent to which results correlate with variables known to
measure the same phenomena and considered valid” (Krippendorff, 2004, p. 319). In this
study, we were concerned with the validity of the components that we extrapolated from
the content analysis. Subsequently, we tested the convergent validity of our findings by
comparing each framework against the seven components identified by our analysis.
If each component was represented in at least one other framework, then it would follow
that it was relevant to RL. Percent agreement was calculated by dividing the number of
frameworks that utilized each component by the total number of frameworks evaluated
and is shown in the bottom row of Table III. As the results demonstrate, all components
have been utilized in previous frameworks. This suggests adequate convergence, which
indicates that our findings are relevant to the context of RL.
Discriminant validity is “the extent to which correlations are absent between results
and variables known to be valid but measuring phenomena that are distinctly different”
(Krippendorff, 2004, p. 319). If an existing RL framework encompassed all the
components that we found in our analysis, then it would indicate that the disposition
decision problem may not differ much from other RL problems. However, we found that
this was not the case. We compared each framework against the seven components
identified by our analysis to determine levels of divergence. Percent agreement was
calculated by dividing the number of components included in the respective framework
by the total number of components found in our analysis and is shown in the far right
column of Table III. As demonstrated by the level of agreement, no one framework
accounts for all components, suggesting adequate divergence from existing frameworks.
Key components, practical implications, and future research
Due to the potential impact RL can have on customer relations and the considerable
assets/value now consumed by RL related activities, RL has become a managerial
priority for many firms (Daugherty et al., 2005). Given the heightened awareness of RL
by management, our findings are presented as key components of this relatively recent
managerial priority. Our findings suggest seven components which may affect a firm’s
decision as to which RL disposition activity to employ. Each of these seven components
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Evaluation of
RL frameworks
RL disposition
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257
has meaningful implications for practitioners confronted with the RL decision-making
process. As a result, each component will now be presented and discussed.
Cost is the first of the seven key components identified in our research results and
includes any costs incurred to facilitate effective RL activities. Our analysis found that
nearly every article of the DSS and simulation literature used for content analysis
considered costs in some regard. In addition, costs associated with the RL process are
included in eight of the 11 frameworks evaluated. For example, Tan et al.’s (2003) study
of a US-based computer company’s Asia-Pacific operations noted many inefficiencies
and high costs in their RL programs. Consequently, Tan and Kumar (2006) developed a
decision model to aid practitioners in controlling costs and maximizing profits in their
potential RL activities. Some of the costs discussed by Tan and Kumar (2006, p. 335)
include transportation, customs duty, acquisition, handling, repair, reuse, scrap, storage,
and freight costs. Furthermore, Guide Jr and Pentico’s (2003) framework addresses the
expected costs of remanufacturing, logistics costs, and machine and labor costs. Some of
these costs include remanufacturing costs, costs of acquiring returned products, value of
time (e.g. opportunity costs), costs of lost sales, and inspection costs.
These are just a few of the examples of key costs that must be evaluated when
determining which RL disposition to pursue. Indeed, a wide variety of costs associated
with RL must be considered when deciding which RL disposition option to adopt. These
costs associated with disposition may deter some firms from choosing certain disposition
alternatives. As such, future research could serve to identify and investigate additional
costs associated with each disposition alternative and determine whether or not lower
cost alternatives are necessarily preferred by firms making the disposition decision:
Practical implication 1.
Costs will endure to be a primary consideration in business decision-making. As
noted in our content analysis, a wide variety of different costs are associated with
each RL alternative and with RL in general. Thus, those in practice should carefully
consider the second- and third-order ramifications of the selected alternative,
realizing that unanticipated costs may surface.
Another key component identified by evaluating RL frameworks is profit, which
encompasses any profits realized from the employment of RL activities. The profit
component was included in seven of the 11 frameworks evaluated. In fact, several of
the frameworks evaluated explicitly address the ability of RL activities to generate
profits (Guide Jr and Pentico, 2003; Tan and Kumar, 2006). Accordingly, potential
profitability is a component that must be weighed whenever deciding which RL
disposition option to employ.
Although some potential profits may be relatively obvious and easy for a firm to
account for (e.g. profit margins from remanufacturing a certain item), future research
should explore further the profit potential of each RL disposition alternative in hopes of
uncovering additional means in which to generate profit. For example, the content
analysis suggests that additional revenues realized from recycling efforts may bolster
profits associated with some RL activities. While profit typically has a clear relationship
with RL costs, each RL disposition alternative may also present a unique opportunity to
realize more obscure sources of profit, such as tax breaks for environmentally-friendly
activities or the ability to charge premium prices for products made via “green”
techniques.
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If done correctly, oftentimes a consequence of aggressively pursuing and
implementing RL processes is an enhanced reputation with potential customers,
especially those with an orientation towards “green” organizations. The indirect result
of this enhanced reputation can be improved sales performance, potentially generating
additional profits for the organization. Furthermore, acting in a manner that is
interpreted by potential customers as being socially responsible may lead to increased
profits.
Friedman (1970) asserts that the social responsibility of business is to increase
profits. Upon a thorough review of the profit potential of each disposition alternative,
all else being equal, firms will most likely choose the alternative that will generate the
most profit. Although this seems obvious, empirical research has not yet thoroughly
investigated this assertion or the many avenues in which RL may drive or enhance
profitability. Thus, we suggest further research in this area to determine the extent to
which perceived profitability drives the disposition decision:
Practical implication 2.
Business organizations exist for the benefit of shareholders and stakeholders. When
making the disposition decision, one must always consider the bottom line. Firms
should take the time to thoroughly consider each alternative’s ability to generate
profits.
A third key component identified by our RL framework review is market conditions.
Market conditions are considered in eight of the 11 frameworks evaluated. In the RL
context, we posit that market conditions are generally concerned with the competitive
forces in the marketplace. As described in our definition of market conditions earlier in
this manuscript, this includes the threat of new entrants, threat of substitute products,
the bargaining power of suppliers, and rivalry among existing firms (Porter, 1980).
Our content analysis reveals that variables such as market size and number of
competing firms are considerations often used in the literature.
Market conditions represent an important component because commencing
with an RL disposition practice may often entail entrance into a new market. For
example, the decision to pursue remanufacturing means that the firm will now be in the
remanufactured products business. Depending on the industry, this market may be
substantially different from the new products market for the same item, creating new
and unique challenges for the organization.
Another example can be found when a firm decides to recover recyclable materials as
part of their RL program. Assuming that the firm is an original equipment
manufacturer, the firm will then enter a completely different market when trying to sell
its recyclable materials. Accordingly, SWOT analysis, gap analysis, and other market
measures must be considered when making the RL disposition decision (Porter, 1980).
If these evaluations are not done prior to setting RL disposition policy, the organization
may subsequently find itself in unfamiliar territory and faced with building a new
business model to handle the consequences of an RL disposition decision involving the
recovery of recyclable materials.
Carter and Ellram (1998) and Knemeyer et al. (2002) emphasize the importance of
market forces in their conceptual framework and research into RL. Similarly, our
research suggests that considering components such as overall market size, market
position, and number of competing remanufacturers may be important when making
RL disposition
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259
the RL disposition decision. For example, let us assume that a firm is choosing between
product upgrade or material recovery for disposition of a certain line of mechanical
gearboxes. If the market is saturated from competing firms that offer the same or
substitutable remanufactured gearboxes, then this fierce competition may dissuade the
firm from choosing the product upgrade alternative. Using the same example, let us
assume that the gearboxes are made with a certain trace metal that is not being supplied
by other competitors in the marketplace. It may then be in the firm’s best interest to
choose the material recovery activity for disposition and sell the raw materials in the
marketplace. Competitive forces in the marketplace may significantly hinder a firm’s
ability to implement certain RL disposition alternatives. Alternatively, a lack of
competition may facilitate a smooth entry into a new marketplace, thereby encouraging
the selection of a given disposition alternative. Therefore, a thorough evaluation of the
market-based ramifications of the RL decision must be part of the RL disposition
analysis:
Practical implication 3.
Firms should understand that pursuit of a new RL disposition alternative will likely
involve entrance into a new market environment. Even if the market is similar
(e.g. selling the remanufactured version of an item that the firm already manufactures
new), the competitive landscape may be vastly different. Firms are advised to use the
same market analysis approaches that it would for entrance into any new business.
A fourth key component of the RL disposition decision is customer behavior. Customer
behavior is considered in nine of the 11 frameworks evaluated above and is defined as any
action taken by a customer that impacts a firm’s RL activities. For example, customer
demand for reused, recycled, or remanufactured products and the willingness to return
used or unused products to the supply chain constitute important customer behaviors that
affect a firm’s RL functions. As emphasis toward initiatives such as lean manufacturing
and just-in-time continue to diffuse within supply chain organizations, the focus on
customers and their role in the supply chain is receiving greater attention (Shah and Ward,
2007). To this end, our study found that customer demand was considered more often than
any other factor in the DSS and simulation articles analyzed, suggesting that the role of
customers is critical to determining which RL disposition option to employ.
Additionally, our study found other customer-related behaviors that have received
much less attention, such as the length of time a customer is in possession of a product
before return and the customer’s concern for the environment. Research focused toward
identifying additional customer behaviors and enhancing understanding of known
customer behaviors such as these may bolster practitioners’ understanding of how the
RL disposition decision affects customers, and vice versa. Firms should understand who
their customers are, what they value, and what they are willing to pay for in the
marketplace. Continued research in this area will help to foster this understanding. In
sum, although customer demand will always be a key interest in the supply chain, other
customer behaviors also affect the RL disposition decision. More research is required
to uncover additional pertinent behaviors and determine how important each is to
consider when making the disposition decision:
Practical implication 4.
A firm must understand its customer base. Not only do firms need to anticipate and
forecast demand, but they should understand how their customers feel about
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the firm and its products. This knowledge will help the firm to make decisions that
will enhance the firm’s ability to attract, serve, and retain customers.
A fifth key component is supply chain capabilities, which are considered in nine of the
11 frameworks evaluated. This component is defined as a firm’s existing resource
capacity available for RL activities. Skinner et al. (2008) asserts that if adequate
resource support in the supply chain is not present, the only feasible option is to
dispose of or destroy the returned product because all other alternatives require a great
amount of resources. This limited access to necessary resources reduces the number of
alternatives that a firm may consider for disposition. Accordingly, a firm must take
into account transportation, warehousing, information technology, and other
resource-intensive considerations when determining if the logistical infrastructure
exists to pursue the desired RL activity. If not, the cost of additional capacity must be
weighed against potential profits to determine the feasibility of the given RL activity.
For example, if a firm already possesses the resources required to remanufacture a
given product, then it may be more advantageous to choose remanufacturing over
other potential alternatives. Conversely, if a firm does not readily possess the resources
necessary to initiate remanufacturing, then it will likely choose a different method of
disposition for which is does possess the capability.
Our findings suggest that future research should investigate how outsourcing
(i.e. 3PL and 4PL) may offset existing capacity and capability shortfalls; thus assisting
firms in better understanding their options when considering their existing capabilities
in reference to employing a new RL disposition alternative. Additional methods for
adding capacity may also expand the number of disposition alternatives that a firm
may consider. Regardless of whether the necessary supply chain capabilities to pursue
a given disposition alternative are intrinsic to the firm, easily procurable, or otherwise
affordably attainable, a firm is more likely to seriously consider a given disposition
alternative when it has access to the proper resources to implement that
alternative
than if the resources are not as readily available. Thus, understanding exactly what
resources will be required of each disposition alternative is important when making the
disposition decision:
Practical implication 5.
The disposition decision-maker must be aware of his or her firm’s existing supply
chain capabilities as well as the resources required to properly conduct a given
disposition alternative. If gaps exist between existing and required capabilities, then
the decision-maker must understand what is required to fill those gaps and whether
or not the firm is prepared for and/or willing to commit the additional required
resources. Otherwise, other disposition alternatives may provide a better option for
the firm.
A sixth key component of the RL disposition decision is regulation. Regulation is
considered in six of the 11 frameworks evaluated above and is defined as any law
or directive imposed by a governing body that influences RL. The seventh key
component of the RL disposition decision, environmental impact, is considered in four
of the 11 frameworks. Environmental impact is defined as any consequence (positive or
negative) of the practice of RL activities to the natural environment.
Although separate components, both the academic literature and practitioners often
regard the environment and regulation as one and the same because environmental
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261
concerns often drive regulation. Our research found that both concepts are considered
often and are not necessarily dependent upon one another; therefore, we treat them as
two separate concepts. Regulatory requirements can be driven by environmental
concerns; however, there are also many instances where the two concepts do not
appear to be highly related.
Often times, firms have little choice in implementing an RL activity if recovery of
their product (e.g. automobile tires or paint) is required by law (Roy et al., 2006).
However, research suggests that firms may still choose to be reactive, proactive, or
value-seeking in implementing its environmental practices (Kopicki et al., 1993;
Srivastava, 2007; Van Hoek, 1999). A reactive approach entails simply abiding by
regulations as they are implemented whereas a proactive approach entails anticipating
and staying ahead of regulation. A value-seeking approach entails initiating
environmentally-friendly activities (such as recycling) as calculated initiatives.
Determining which of the above approaches to follow can affect a firm’s decision as
to when, how, and why it will implement a given RL disposition activity. As such, the
effect of regulation and environmental impact on the disposition decision may be
vastly different for each individual firm. Although a topic of recent interest, literature
is still relatively limited in the area of green RL and more research is encouraged to
determine how both the environment and regulation affect the RL disposition decision.
Although we uncover that regulation and environmental impact surely affect the
disposition decision, we do not have sufficient evidence at this time to characterize the
specific nature of these relationships:
Practical implication 6.
Firms must continuously be aware of existing and pending regulation that may
affect their businesses. This situational awareness should also transcend into the
disposition decision-making process so that the implications of a desired alternative
are fully understood.
Practical implication 7.
Those in the firm who are charged with making disposition decisions must be
aware of the firm’s policy toward environmental practices to ensure that their
decisions are congruent with existing policies and programs. Furthermore, the
environmental impact of each disposition alternative must be understood and
considered when making the disposition decision.
The seven key components of RL disposition and their practical implications on the RL
decision-making process should be used to guide future research efforts. These efforts
should be focused in part on additional exploration of the RL disposition decision and
how these seven key components combine to impact the RL decision-making process.
Each of these key components can be used as the basis of future research designed to
delve deeper into each of the individual components identified by the current research.
However, it should be noted that the results of our research effort also suggests
interactions or relationships between each of the seven components. While many of the
practical implications above seem to suggest simple if/then scenarios for RL
disposition decision-making, clearly the overall RL disposition decision is not that
simple. While a thorough examination of each key component is warranted, there are
relationships between the various components that must also be considered. Each of
these components may work with and/or against other components.
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This research effort uncovered what are proposed to be the relevant components in
making the disposition decision. Future research should also seek to prioritize the
importance of these components and investigate the relationships between components
in a variety of RL scenarios. For example, should costs be given greater consideration
than environmental impact? If market competition is fierce yet profit potential is high
for a given alternative, should that alternative still be pursued? Does the potential
for high profits offset the lack of existing supply chain capabilities? By investigating
the relationships proposed in this research and establishing the relative importance of
each, future research could help to provide answers to such questions.
Although each individual firm may weigh the importance of each component
differently when making the RL disposition decision, future research in this area may
be able to provide a standard point of departure. For example, in some organizations, it
may be of upmost importance to control costs. In this case, costs would be the most
heavily weighted component in the disposition decision. Alternatively, organizations
may be looking to expand their operations or delve into new businesses. In these cases,
lack of existing supply chain capabilities may not be a highly important consideration.
Instead, adopting a resource-intensive disposition option such as remanufacturing may
be a desirable alternative in spite of the lack of existing resources because it is viewed
as an opportunity to expand. It is apparent that these seven components are not
independent of one another. As a result, we offer one additional practical implication:
Practical implication 8.
One must consider each of the considerations identified in this research when
making the disposition decision. Although we are not certain as to the relative
importance of each consideration to every firm, those charged with making the
disposition decision may be able to rely on corporate goals, values, and objectives to
construct a weighted decision matrix to assist in making disposition decisions
within their own organization. Clearly, the prioritization of the relative importance
of each of the considerations related to the disposition decision will differ depending
on the specific RL application being evaluated, the overall organization, and the
overall RL objectives of the organization.
It is within this last practical implication that the impetus for the decision-making
framework that we present below is derived. Decision-makers in each organization
must not only consider, but also weigh the seven components when deciding upon
which disposition alternative to pursue. The next section presents an RL disposition
decision-making framework, which serves assimilate the fundamental ideas discussed
thus far in this article.
Decision-making for RL disposition
The components discussed above provide the foundation for examining an organization’s
decision as to which disposition alternative to choose. However, identification and
explanation of these components alone is not enough to develop a comprehensive
framework for RL disposition decision-making; all of the pieces must now be put together.
Accordingly, the next step involves integrating these components into a decision-making
framework. Thus, we examined the literature for existing decision-making models that
may be used as a foundation for explaining the RL disposition decision-making process.
Given our review of the foundational RL literature and our study results,
RL disposition
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263
the decision regarding which disposition alternative that an organization should adopt for
a given product line appears to encompass similar components as identified in extant
strategic decision-making frameworks.
Although the RL disposition decision may not necessarily be “strategic” for a
given firm, one interesting finding of our research is that the decision components of the
RL decision-making process appear to be very similar to those addressed when
making strategic decisions. As such, extant strategic decision models may provide a
sound foundation in which to integrate our findings and develop an RL disposition
decision-making framework. A strategic decision is one that involves market positioning, is
highly complex, involves multiple functions, affects firm performance, and represents a
substantial commitment to resources (Arendt et al., 2005; Eisenhardt, 1989). Thus, by
nature, a strategic decision is often rare, consequential, and sets the precedent for lesser
decisions and future actions throughout the organization (Hickson et al., 1986; Hunger and
Wheelen, 2007). As highlighted in our review of the literature, these characteristics may
also, to some degree, describe the RL disposition decision.
Extant literature offers a variety of strategic decision process models (Schwenk,
1995). One such model is presented by Hunger and Wheelen (2007), which is based on
their synthesis of competing and complimentary strategic decision literature
(Eisenhardt and Sull, 2001; Hickson et al., 1986; Mintzberg, 1973; Mintzberg et al.,
1976; Quinn, 1980). Hunger and Wheelen’s (2007, p. 13) model consists of an eight step
strategic decision-making process. These steps are:
(1) evaluate current performance results;
(2) review corporate governance;
(3) scan the external environment;
(4) scan the internal corporate environment;
(5) analyze relevant factors;
(6) generate, evaluate and select the best alternative;
(7) implement selected alternative; and
(8) evaluate implemented alternative.
We integrate our RL disposition decision-making components with Hunger and
Wheelen’s (2007) decision-making process, which results in the proposed RL
disposition decision-making process (Figure 1).
As shown in Figure 1, the components of the RL disposition decision are merged
within an existing decision-making process to create a framework for making the RL
disposition decision. The first step of this process entails evaluating current
organizational performance. This requires a review of the current health and posture
Figure 1.
RL disposition
decision-making process
Evaluate current
performance (mission,
objectives, and
policies)
Review applicable
corporate governance
Scan external environment:
– Customer behavior
– Market conditions
– Environmental impact
– Regulation
Scan internal environment:
– Supply chain capabilities
– Profit from RL
– Cost of RL
Select and
implement
alternative
Review disposition
alternatives:
– Reuse
– Product upgrade
– Material recovery
– Waste management
Evaluate
implemented
alternative
IJPDLM
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264
of the organization, to include a review of the organization’s mission statement, business
objectives, and any current policies regarding logistics and/or product returns.
This purpose of this step is to give decision-makers an overview of the current status of
the organization. The next step involves reviewing corporate governance that may
affect the decision at hand. This may entail gaining the support of the board of directors
for investigating new RL options or reviewing the corporate charter to determine if
adopting a new RL function is tenable.
After gaining insight regarding current corporate performance and applicable
governance, the decision-maker(s) should then gain an understanding of the RL
disposition alternatives of reuse, product upgrade, material recovery, and waste
management. This involves understanding what each alternative entails and how each
alternative may align with the first two steps of the RL disposition decision-making
process. This step takes place earlier in the RL disposition decision-making process
than it does in Hunger and Wheelen’s (2007) generic process because, unlike some other
decisions, the RL alternatives are already known in advance to decision-makers.
The decision process then branches into two related steps, which involve reviewing
both the external and internal environment. This is where the components identified in
our study are integrated within the decision-making process. Regarding external
environment considerations, the decision-maker(s) should examine customer behavior,
market conditions, existing regulation, and the environmental impact presented by
each alternative. Regarding internal environment considerations, the decision-maker(s)
should examine the organization’s existing supply chain capabilities as well as the
potential costs and profits associated with each disposition alternative.
The next step involves making the actual decision as to which RL disposition
alternative to adopt. This may be accomplished via use of a weighted decision matrix.
The matrix could be populated with the four RL alternatives, the considerations
identified by our content analysis, and a weighting scheme that would be particular to
each individual organization and motivated by the considerations for current
performance and corporate governance examined in steps 1 and 2. The final step of the
RL disposition decision-making process involves a periodic review of the implemented
RL practice to ensure desired results.
The resulting RL disposition decision-making process offers both a framework for
business leaders posed with making such decisions and a foundation for future
decision-making research in RL. Decision-makers may use this framework to create a
decision matrix to help facilitate their decision as to which RL disposition alternative
would be most advantageous to employ. Future research may further examine the
weighting scheme adopted by firms to determine if some of these considerations are
generally more important to most firms than other considerations.
Content analysis: future ideas and limitations
The content analysis approach used in this research may be valuable in studying
additional SCM topics. As the amount of published research in the expanding field of SCM
continues to increase, so does the amount of content. Content analysis can aid in
comparing content across many sources and help to reveal aspects of the content that may
be difficult to see when viewing phenomena in the context and scope of other research
methods. For example, content analysis has been used in the SCM field to discover various
research trends (Frankel et al., 2005; Stock, 2001). However, recent literature in the MIS
RL disposition
decision-making
265
field has employed elements of content analysis to help uncover the latest technology
trends within industry so as to better align research with practice and report timely
research results to practitioners regarding the use of such technology (Bakersville and
Myers, 2009; Wang, 2010). We encourage similar use of content analysis in the SCM field.
The limitations of our study are inherent to most research endeavors employing a
content analysis methodology. First, relevant data in the literature may have been
inadvertently overlooked by the recorder. However, the sufficient number of articles
analyzed and the large number of variables uncovered suggest that this limitation may
not greatly affect the findings of this study. Second, although validated via comparison
with current RL frameworks, the findings may be subject to author bias. While we
minimized the potential for bias by repeatedly involving multiple authors in the
evaluation process, this remains a risk of the method used. Therefore, future research
designed to test the concepts presented here can serve to further qualify these results and
expand overall understanding of the implications of the RL disposition decision. For
example, Delphi methods or survey-based research can be employed to determine the
relative impact of each of these components. Regardless of the methodology employed in
further studies, our study lays a foundation for further investigation in this area.
Conclusion
A content analysis of 62 technical articles published in top OM, SCM, and MIS journals
between 2000 and 2010 uncovered the use of 60 RL decision considerations used in the
literature. Seven individual components were extrapolated from this listing and
compared with existing RL considerations. These components are: supply chain
capabilities, costs of RL, profit from RL, environmental impact of RL, regulation,
market considerations, and customer behavior. In addition to identifying and
discussing these seven key components of the RL disposition decision, we also offer
practical implications. These implications and supporting discussion should enhance
understanding of each of the seven key components and aid in the identification of key
issues worthy of further investigation into the integration of the returns management
process with existing business processes. Our resulting decision-making framework is
the first of its kind for RL disposition and lays the groundwork for future research
while also providing a practical guide for business decision-makers.
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Appendix
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Corresponding author
Benjamin T. Hazen can be contacted at: benjamin.hazen@auburn.edu
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Strategic orientations,
sustainable supply chain
initiatives
, and reverse logistic
s
Empirical evidence from an emerging market
Chin-Chun Hsu and Keah-Choon Tan
Lee Business School, University of Nevada, Las Vegas, Nevada, USA, and
Suhaiza Hanim Mohamad Zailani
University of Malaya, Kuala Lumpur, Malays
ia
Abstract
Purpose – Global outsourcing shifts manufacturing jobs to emerging countries, which provides new
opportunities for improving their economic development. The authors develop and test a theoretical
model to predict first, how sustainable supply chain initiatives might influence reverse logistics
outcomes and second, the impact of eco-reputation and eco-innovation orientation strategies on the
deployment of sustainable supply chain initiatives. The paper aims to discuss these issues.
Design/methodology/approach – The proposed new model of antecedents and outcomes of
sustainable supply chain initiatives underwent a rigorous empirical test through structural equation
modeling with samples from an emerging market.
Findings – The results show that firms that implement sustainable supply chain initiatives can realize
positive reverse logistics outcomes; the study also provides new insights into eco-innovation and
eco-reputation strategic orientations as theoretically important antecedents of sustainable supply
chain
initiatives.
Research limitations/implications – Though the authors identify three components of sustainable
supply chain initiatives, other components could exist, and ongoing research should investigate them.
Practical implications – The findings have important implications for managers in emerging
markets seeking to initiate ecologically friendly business practices. The authors offer strong evidence of
the benefits obtained from reverse logistics in sustainable supply chain initiatives. Policy makers and
firms attempting to nurture sustainable supply chain initiatives should not overlook the important role of
eco-reputation and eco-innovation strategic orientations, which the results identify as important enablers.
Originality/value – This study offers evidence of the critical role of eco-reputation and
eco-innovation strategic orientations in deploying sustainable supply chain initiative programs, as well
as of their mutual effects. This study also offers empirical evidence that implementing sustainable
supply chain initiatives leads to reverse logistics, creating value, and a new source of competitive
advantages.
Keywords Eco-innovation, Emerging market, Strategic orientation, Eco-reputation,
Reverse logistics, Sustainable supply chain initiatives
Paper type Research paper
1. Introduction
Outsourcing trends since the early 1990s have transformed emerging countries into
significant players in the global economy. Global outsourcing thus has reshaped global
supply chain systems in significant ways, such that the globalized manufacturing
network has shifted manufacturing jobs to emerging countries, which providing
new opportunities for improving the economic development of emerging markets. But a
globalized manufacturing network also poses significant risks to individual health
and safety, national economies, and local, regional, and global environments
International Journal of Operatio
ns
& Production Management
Vol. 36 No. 1, 2016
pp. 86-
110
©EmeraldGroup Publishing Limited
0144-3577
DOI 10.1108/IJOPM-06-2014-0252
Received 20 July 2014
Revised 10 December 2014
29 January 2015
24 February 2015
Accepted 26 February 2015
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/0144-3577.htm
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(O’Rourke, 2005). Thus the question of whether manufacturing firms in emerging
countries can manage their profit growth and environmental sustainability goals
effectively has important implications at both national and global levels.
Sustainable business practices can help create wealth for firms and raise the standard
of living in emerging markets; unsustainable economic activities lead to environmental
degradation that can threaten an emerging country’s long-term prosperity and economic
competiveness (Schmidheiny, 1992). Firms in emerging countries might adapt ecologically
friendly strategies and guidelines from their business clients or competitors in more
advanced economies, though rapid business development and continuous environmental
deterioration also have increased the emphasis on environmental sustainability.
In particular, environmental concerns have prompted the governments of some
emerging economies to regulate business practices and set broad environmental
improvement objectives (Child and Tsai, 2005). On the flip side, profit pressures and weak
ecological traditions can decrease firms’ incentives to address the broader range of
stakeholder interests associated with sustainable practices.
In this context, we note three pertinent knowledge gaps. First, many studies have
focussed on green business approaches in advanced economies, but much less research
has addressed the antecedents or outcomes of ecologically friendly business practices in
emerging markets (Blome et al., 2014; Fabbe-Costes et al., 2014). Second, despite the
ongoing debate about the potential outcomes of ecologically friendly supply chain
activities (Prajogo et al., 2014), the benefits or outcomes of sustainable supply
chain initiatives are poorly understood (Roehrich et al., 2014), though outcome measures
are essential for managing and navigating competitive global markets. In a related sense,
surprisingly few empirical studies examine the impacts on reverse logistics (Aitken and
Harrison, 2013), despite their promise for creating new value and providing competitive
advantages ( Jayaraman and Luo, 2007). Third, even when ecologically friendly supply
chain commitments make sense, managers lack guidelines for how to start greening their
firms’ supply chain efforts. A few prior studies identify external “enablers,” derived from
institutional or stakeholder theory (Zailani et al., 2012), but relatively few cite strategically
relevant factors. That is, research into sustainable supply chain initiatives tends to
pertain to organizational capabilities, not the strategic orientation antecedents that
precede the adoption of sustainable supply chain initiatives. By focussing on
sustainability practices, definitions, and decision frameworks, these studies ignore the
need for insights into how to develop sustainability strategies from an organizational
perspective (Zhu and Sarkis, 2007). The fragmented, incomplete knowledge in this area
thus fails to address adequately which key strategic orientation forces will drive
sustainable supply chain initiatives.
In attempting to fill these knowledge gaps, this study makes three primary
contributions. First, we study emerging economies. Some manufacturing firms identify
and target segments of ecologically conscious buyers, in an effort to position themselves
as favorable green suppliers, but most companies refuse to abandon their existing
operations and production processes, regardless of the growing interest in sustainability
(Größler et al., 2013). Thus, manufacturing firms in emerging countries must find ways to
execute existing supply chain strategies through sustainable initiatives that implement
more ecologically friendly programs than appeared in their past supply chain efforts.
In particular, we study Malaysia, which is a member of Association of Southeastern
Asian Nations and an integral part of the global economy; Malaysian suppliers have
critical roles in global supply chains. The country represents an important
manufacturing hub for global firms that seek to outsource the manufacture of
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component parts. The United Nations Conference on Trade and Development (UNCTAD
)
reports that foreign direct investment (FDI) inflows to Malaysia increased from
US$9.1 billion in 2010 to US$11.9 billion in 2011, an increase of 30.8 percent
(World Investment Report, 2013), which also raised Malaysia’s rank to 13 from 16 in the
list of Top Prospective Host Economies for 2013-2015 (World Investment Report, 2013).
Thus, the UNCTAD report affirms Malaysia’s attractiveness as a FDI destination. In this
emerging economy, sustainable development remains at an early stage, whereas profit
maximization is the priority for most manufacturing firms.
Second, we examine the reverse logistic effects of ecologically friendly purchasing,
manufacturing, and packaging programs (De Leeuw et al., 2013; Hsu et al., 2013).
Sustainable supply chain initiatives can deliver reverse logistic benefits; our empirical
evidence even shows that firms can create competitive advantages for new value
creation (Chavez et al., 2013). Reverse logistics refers to returns of products or
packaging, after their use, for reuse, recycling, or reclamation of materials
(Kapetanopoulou and Tagaras, 2011). By engaging in reverse logistics, firms can
recycle remanufactured parts or components, as well as dispose properly of those
components that cannot undergo remanufacturing or recycling (Lo, 2014). In turn, they
constitute a substantial cost-driving area and may result in greater profitability and
customer satisfaction, as well as benefitting the environment (Hsu et al., 2013).
Third, this study considers specific strategic orientation drivers that engender
success in sustainable supply chain initiatives. Specifically, we identify and empirically
examine two new strategic orientation factors that have been overlooked:
eco-reputation and eco-innovation, both of which integrate environmental concerns
into the firm’s business strategies. This study thus offers evidence of the critical role of
eco-reputation and eco-innovation strategic orientations in deploying sustainable
supply chain initiative programs, as well as of their mutual effects. Both antecedents
may be important for understanding how firms respond to ecological challenges and
derive sustainable supply chain initiatives, but neither has been the subject of prior
research. We show that firms wishing to sustain their firm’s supply chain initiatives
should develop their eco-reputation and eco-innovation strategic orientations first.
In the next section, we present a theoretical framework for the strategic orientation
antecedents and reverse logistics outcomes of sustainable supply chain initiatives.
Our research hypotheses reflect input from a wide array of literature. We discuss the
research methodology and the results of the data analyses. Finally, this paper
concludes by delineating the findings, their managerial implications, and limitations.
2. Literature review
2.1 Strategic orientations
Strategic orientation originally stemmed from the market orientation notion, which
was a popular means to measure firm performance. According to Manu and Sriram
(1996, p. 79), strategic orientation refers to “how an organization uses strategy to adapt
and/or change aspects of its environment for a more favorable alignment.” Extended
versions focus on customer or technology orientations, and Narver and Slater (1990)
argue that strategic orientation is an critical component of profitability for both
manufacturing and service businesses, such that an orientation influences business
decisions through its effects on business profitability (Schniederjans and Cao, 2009).
According to strategic choice theory (Child, 1972), strategic decisions also have a
determining role in a firm’s business survival, and the fundamental issue is the strategic
orientation, with a foundational assumption that firms can enact and actively shape their
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environments. Strategic choice theory centers on decision making in organizations
designed to achieve well-defined goals. Thus, managerial discretion, interpretation,
and perspective have great influence in strategic decision making, over the span of
shared organizational actions. To achieve organizational effectiveness, firms must
make appropriate strategic choices that “represent the competitive strategy implemented
by a firm to create continuing performance improvements” (Morgan and Strong, 1998,
p. 1055). Ultimately a strategic orientation is a firm’s overall direction and objectives,
oriented toward an external business environment and driven by top management
(Voss and Voss, 2000). Strategic choice theory focusses on managers’ strategic choices
when their firms face external challenges (Child, 1972). If they have a strategic orientation,
firms choose to leverage their strategy to adapt or change aspects of their external
environment to ensure more favorable alignment. It also helps explain why firms take
proactive and committed actions to address urgent issues such as sustainability.
Firms do not interact with their operating environments in identical ways. For
example, in the same industry, some firms focus on a narrow, limited, product-market
domains, in an effort to protect their market share. Others search continuously for new
market opportunities through innovation and new product development. Responses to
the operating environment reflect firms’ strategic orientations; strategic orientations
largely their choices, establish their strategic positioning, affect their performance,
involve multiple functions, are highly complex and ambiguous, and demand
substantial resource commitments. In addition, a strategic orientation choice refers
to the process of choosing one course of action rather than another. Thus a strategic
orientation offers a means to comprehend the actions that firms take to enhance their
profitability and competitive advantage. This pattern of past, or intended, decisions
guides a firm’s ongoing alignment with its external environment and shapes strategic
policies and procedures (Hill and Cuthbertson, 2011; Minarro-Viseras et al., 2005).
From a sustainable supply chain perspective, firms’ strategic orientations are
critical, because sustainable business practices demand substantial firm resources and
are technically complex, such that they require diverse skills contributed by technical
experts, organizational experts, and top management (Saeed et al., 2014). From a
strategic choice theory perspective, Sharma (2000) examines how firms use freedom of
choice (discretion, interpretation, and perspective) to create strategies that influence
firms’ orientation toward adopting sustainability initiatives. Ketchen and Hult (2011)
cite strategic choice theory as appropriate for studying strategic supply chain
management. With its focus on the best value, strategic choice theory seeks to identify
supply chain models that can affect organizational outcomes and enact the
environment. Strategic choice theory centers on the intra-organizational level and the
provision of certain strategic capabilities (Ketchen and Hult, 2011). It also seeks to
answers questions and challenges in extant supply chain management research.
Finally, a strategic orientation toward sustainable business practices is influenced by
various external agents, including suppliers, governments, regulatory organizations,
green social groups, and rapidly changing technology (Shrivastava and Grant, 1985).
We examine two particular ecological strategic orientations: eco-reputation and
eco-innovation. An eco-reputation is a stakeholder’s overall perception of a company’s
efforts on environmental protection over time. This evaluation reflects each
stakeholder’s experience of the ecological commitment of the company, as well as
images based on the company’s actions, beyond simple compliance with government
regulations (e.g. Chen, 2010). This definition is consistent with Banerjee (2001),
Banerjee et al. (2003) and Esty and Winston (2009). Eco-innovation instead refers to the
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development of products and processes that explicitly account for concerns about
the natural environment in pursuit of the goal of sustainable development and
ecological improvements (e.g. Menon et al., 1999). Thus eco-innovation constitutes a
firm’s strategic resources, from ecologically friendly technological advances to socially
acceptable innovative paths, consistent with the view that product development and
process improvement can be designed and executed in ways that are less harmful to the
natural environment (Fussler and James, 1996; Segarra-Oña et al., 2014).
Hong et al. (2009) define an ecological strategic orientation as a firm’s long-term
commitment to producing environmentally sound products and services by
implementing environmental improvement goals. Environmentally sound products
can promote a firm’s overall economic performance, through internal integration and
external coordination with both major stakeholders, such as customers and suppliers.
Moreover, to ensure a sustainable orientation for the supply chain, the firm must
maintain its successful past practices while promoting and encouraging the
implementation of consistent environmental innovative initiatives that reinforce its
long-term sustainability (Hong et al., 2009; Awaysheh and Klassen, 2010). The adoption
of sustainable supply chain initiatives depends on the firm’s strategic orientation
(Baines et al., 2005). An ecological strategic orientation, such as eco-reputation or
eco-innovation, influences strategic choices, such that each ecological strategic
orientation can influence the impact of the firm’s decision makers on the adoption of
sustainable business practices throughout the firm (Chiang et al., 2012).
2.2 Sustainable supply chain management
Supply chain management encompasses “a set of three or more entities directly
involved in the upstream or downstream flows of products, services, finances, and/or
information from a source to a customer” (Mentzer et al., 2001, p. 4). This definition sets
the boundaries of the supply chain with the final customer. Traditional supply chains
also are based on the production paradigm (Doran et al., 2007). In contrast, sustainable
supply chains is an inter-disciplinary, cross-cutting issue. The 2005 world summit on
social development (www.un.org/ga/59/hl60_plenarymeeting.html) identified three
pillars of sustainability: economic development (profit), social development (people),
and environmental protection (plant). These pillars are not mutually exclusive but can
be mutually reinforcing. In the contemporary accounting framework, the triple bottom
line provides the measure of business sustainability, in terms of financial, social, and
environmental performance. In addition, Peter Senge, in an interview by Harvard
Business Review, identifies sustainable supply chains as the core enablers of the next
industrial revolution (Prokesch, 2010). The United Nations Global Compact recently
launched a guide for advancing sustainability in global supply chains in four key areas:
human rights, labor, environment, and anti-corruption (www.unglobalcompact.org).
With this study, we focus on the environmental perspective of sustainable supply
chain practices. Specifically, firms must to partner with members throughout their
supply chains to improve energy efficiency while reducing natural resource usage,
waste, and adverse environmental impacts, which together lead to a stronger bottom
line. Sustainable supply chains account for the environmental impacts of products and
services as they flow throughout the supply chain. These environmentally friendly
extensions of traditional supply chains include activities to minimize the negative
environmental impacts of a product or service throughout its entire life cycle.
Sustainable supply chains deal with environmental issues in both forward and reverse
versions (Rao and Holt, 2005). A sustainable forward supply chain would address
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www.un.org/ga/59/hl60_plenarymeeting.html
www.unglobalcompact.org
environmental issues both upstream and downstream (Geyer and Jackson, 2004).
Upstream, sustainable supply chains can have significant effects in terms of improving
suppliers’ environmental performance (Sarkis, 2006). Downstream, these sustainable
supply chains focus on reducing the environmental impacts of the products produced
during their use and disposal. Such reductions often offer significant environmental
benefits, because products generate most of their environmental emissions and waste
during their use, such that these detrimental impacts may exceed those generated during
the manufacturing stage. Through these outcomes, sustainable supply chains provide
both economic and environmental benefits (Carter et al., 2000; Rao and Holt, 2005).
2.3 Sustainable supply chain initiatives
Supply chains encompass all activities associated with the process flow for
transforming raw materials into goods for end users. The process cycle begins with
purchasing, including raw material purchasing activities by suppliers. Manufacturing
activities follow, after which the product must be distributed to customers or retailers
(Hill et al., 2012). According to sustainability literature, the potential green elements in
this cycle include vendor assessments, environmental purchasing policies, green
production policies, waste management, training, cross-functional integration, effective
coordination between companies and suppliers, performance evaluation processes,
the selection of suppliers, and leveraging relationships between suppliers and
customers (Giovanni, 2012). We therefore conceptualize sustainable supply chain
initiatives as those designed to accomplish the firm’s strategic supply chain functions –
purchasing, manufacturing, and packaging – in ways that minimize their negative
impacts on the natural environment. This conceptualization is line with prior
definitions of sustainable supply chains (e.g. Hsu et al., 2013).
Green purchasing refers to an ecologically conscious purchasing initiative that aims
to ensure procured materials or components meet the firm’s eco-friendly goals. The
purchasing process can manifest the firm’s environmental preferences if it includes
green purchasing criteria (Saghiri and Hill, 2014). Carter and Ellram (1998) argue
that green purchasing also should reflect efforts to reduce, reuse, and recycle materials.
Thus, purchasing decisions have significant influences on the sustainable supply chain
(Yang et al., 2013) through the procurement of raw materials and components.
Green manufacturing entails the environmentally conscious production of a
product, with the goal of minimizing its negative environmental impacts throughout its
entire life cycle, as well as promoting positive ecological business operation practices,
such as recycling and reusing products (Dam and Petkova, 2014). That is, green
manufacturing considers environmental impacts in every stage of the product lifecycle
(Giovanni, 2012), in an effort to minimize the environmental impacts of manufacturing
processes, generate minimum waste, and reduce environmental pollution. Pursuing
green manufacturing also helps firms lower their raw material costs, gain production
efficiency, reduce environmental and occupational safety expenses, and improve their
corporate image (Zhu and Sarkis, 2007). Thus, green manufacturing helps firms
achieve profit growth and increase their market share.
Finally, green packaging is environmentally conscious packaging of a product, to
minimize the associated negative environmental impacts. Packaging contributes directly
to product success in supply chains, because it can enable the efficient distribution of
products, as well as lower environmental impacts due to spoilage or waste. Increased
attention to global climate change has made green packaging a primary focus area, to
reduce waste and improve air quality, because different packaging characteristics
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(e.g. size, shape, materials) have different impacts. Hsu et al. (2013) indicate that green
packaging includes considerations of cost (materials and shipping), performance
(adequate protection of the product), convenience (easy to use), compliance (with legal
requirements), and environmental impact (Liu et al., 2013; Lin et al., 2013).
2.4 Reverse logistics and competitive advantage
Dowlatshahi (2000) define reverse logistics as activities by which a producer retrieves
products and components to recycle, rebuild, or dispose of them properly. Reverse
logistics also might refer to the actual process of return or take-back, after the
consumer has used the product or packaging, to reuse, recycle, or reclaim materials,
or else provide safe refills (Carter and Ellram, 1998). Using reverse logistics as a supply
chain performance measure suggests how companies can obtain competitive
advantages by quantifying the efficiency and effectiveness of their actions (Lehtinen
and Ahola, 2010). Thus reverse logistics differentiate a firm, leading to a market
advantage and opportunities to build competitive advantages.
Specifically, reverse logistics create tangible and intangible value by helping firms
first, extract value from used/returned goods instead of wasting manpower, time, and
to procure more raw materials, second, create additional value through increasing
product life cycles, third, improve customer satisfaction and loyalty by paying more
attention to faulty goods and merchandise repairs, and fourth, obtain feedback to
suggest improvements and enhance understanding of the real reasons for product
returns, which should lead to future product improvements or new product designs
(Aitken and Harrison, 2013). Through reverse logistics, manufacturing firms not only
receive products back from the consumer but also collect unsold merchandise for the
manufacturer to take apart, sort, reassemble, or recycle (Yu et al., 2012). Alternatively,
the returned product might be re-sold in secondary channels and thus generate
revenue (Aitken and Harrison, 2013). Reverse logistics also might enhance customer
loyalty, because customers respond positively to environmentally responsible
actions by the firm, so goodwill generated by reverse logistics could be a source of
firm competitiveness.
3. Hypotheses development
We depict the key study constructs in Figure 1. The two strategic orientation
antecedents, eco-innovation, and eco-reputation, precede sustainable supply chain
initiatives. Sustainable supply chain initiatives then relate to the firm’s reverse logistics.
3.1 Relationship of eco-reputation and eco-innovation strategic orientations
According to strategic orientation literature and strategic choice theory, a firm’s
strategic orientations are critical, because they involve the commitment of a large
amount of firm resources (De Toni and Tonchia, 2003). They also tend to be
technically complex, demanding diverse skills gathered from technical experts,
organizational experts, and top management. Furthermore, strategic orientations
depend on external agents, such as suppliers, organized labor unions, and rapidly
changing technology (Shrivastava and Grant, 1985). Strategic orientation choice
involves a process of choosing a particular course of action, which helps explicate the
actions that firms take to achieve enhanced profitability and competitive advantage.
Because a strategic orientation is a pattern of past or intended decisions, guiding
the firm’s ongoing alignment with its external environment and shaping internal
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procedures and policies, a firm may apply multiple orientation decisions at the same
time to fulfill its strategic goals.
Testa and Iraldo (2010) introduce two strategic orientations that favor the adoption
of green supply chain management practices by firms. We consider an eco-reputation
strategic orientation, a strategy designed to make all stakeholders (customers,
suppliers, society) aware of the firm’s efforts to implement eco-friendly systems
and thus enhance its corporate image. We also note an eco-innovation strategic
orientation, a strategy that guides companies to develop innovative products and
operational processes that can improve their environmental performance. Companies
that are frontrunners in developing eco-friendly product and process innovations have
an opportunity to strengthen their leadership and differentiate themselves more from
their competitors.
An organization’s ecologically friendly strategic orientation thus comprises all
positioning strategies associated with a particular issue, such that greater integration
Strategic Orientations
(SO)
Eco-reputation SO
Eco-innovation SO
Sustainable Supply Chain
(SC)
Upstream SC
Green Purchasing
Downstream SC
Green Manufacturing
Green Packaging
Outcome
Reverse SC
Reverse Logistics
Strategic Choice Theory
• Firms have freedom of choice when formulating and implementing strategies.
• Strategic orientation focusses on firms’ strategic choices when facing external challenges.
• Strategic choice theory centers on the intra-organizational level and the provision of certain strategic capabilities.
• Firms use freedom of choice to influence firms’ orientation toward adopting sustainability initiatives.
• Ecological strategic orientation can influence the adoption of sustainable business practices.
Sustainable SCM Literature
• Sustainable SCs account for the environmental impacts
of products /services as they flow throughout the SC.
• Using reverse logistics as a SC business/environment
performance measure.
• Reverse logistics create tangible and intangible value.
• Firms obtain competitive advantages by quantifying
the efficiency/effectiveness of their SC actions.
Strategic Orientations
Sustainable Supply
Chain Initiatives
Outcome
Eco-Reputation
Strategic Orientation
(ERSO)
Eco-Innovation
Strategic Orientation
(EISO)
Green
Manufacturing (GM)
Green
Packaging (GK)
Green
Purchasing (GP)
Reverse Logistics
(RL)
H2a
H2b
H2c
H1
H3a
H3b
H3c
H4a
H4b
H4c
Figure 1.
Research model
and hypotheses
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initiatives
and formulation of positioning strategies should enable them to influence the firm’s
sustainable business practices. That is, eco-reputation and eco-innovation strategic
orientations should be mutually interdependent:
H1. An eco-reputation strategic orientation correlates positively with an eco-innovation
strategic orientation.
3.2 Eco-reputation antecedents of supply chain initiatives
Organizations may adopt sustainable business practices in response to the
expectations of stakeholders. For example, such efforts appeal to consumers as they
become increasingly aware of the need to protect the environment, such that they might
expand the product’s unique selling points and boost corporate reputation.
An eco-reputation strategic orientation also provides a buffer against short-term
performance demands, such that managers can take a longer-term view and
experiment with new strategies to enhance the firm’s reputation (Zhu and Sarkis, 2007).
Ecologically friendly investments are significant expenditures with long return terms,
so firms with an eco-reputation strategic orientation should be better able to make such
investments (Fabbe-Costes et al., 2014; Hoejmose et al., 2013).
Toyota enjoys a strong eco-reputation, considering its top position on the Global 50
Green Brands List (Interbrand, 2013). As an auto manufacturer, Toyota regards its
eco-reputation as its most important strategic resource; it has made large strides in
reducing energy consumption, water use, waste, and toxic emissions (Chan et al., 2012).
Toyota also successfully shares its eco-reputation strategic orientation with its
suppliers; by working with them, Toyota exploits eco-reputation to not only create a
positive image among consumers but also make profit from them, as evidenced by the
economic success of its hybrid-electric Prius and its collaboration to produce the
all-electric Tesla. Half of all Americans consider the ecological impacts of the products
and services they buy (Leiserowitz et al., 2013), and a firm’s eco-reputation represents
an important criterion for purchasing decisions. Many global firms therefore strategize
to develop and maintain environmental reputations. For example, 3M launched an
ecologically friendly version of its post-it notes made from recycled paper and started
packaging large post-it packs in recyclable cartons instead of plastic wrap.
An eco-reputation strategic orientation grants managers the ability to invest capital
to sustain their supply chain programs and wait to reap longer-term reputation benefits
from their deployment (Huq et al., 2014). Eco-reputation is not an optional or low
priority strategic orientation; it becomes the key to a company’s image. Therefore, this
strategic orientation not some nice-to-have “add-on” but rather a core business
philosophy that weaves throughout the company and radiates outward throughout the
entire supply chain and its activities ( Jerónimo et al., 2013). We posit:
H2. An eco-reputation strategic orientation has a positive effect on a firm’s
deployment of (a) green purchasing; (b) green manufacturing; and (c) green
packaging.
3.3 Eco-innovation antecedent of supply chain initiatives
According to Teece (2007), eco-innovation is the firm’s ability to integrate, establish,
and reconfigure external and internal, environmentally friendly capabilities.
Specifically, eco-innovation requires the development of new value through more
efficient and effective environmentally friendly products, services, and processes.
Product eco-innovation focusses on the creation of new products or improvement of
existing products to meet environmental concerns; process eco-innovation focusses on
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the creation and implementation of innovative or substantially improved production or
delivery methods (Blome et al., 2014).
Many global enterprises and governments use the term eco-innovation to emphasize
the contributions of business to sustainable development while also improving
competitiveness. For example, the European Commission launched the eco-innovation
action plan (EcoAP) in 2011 to promote eco-innovation development across Europe.
The EcoAP is a significant milestone, moving the European Union beyond ecologically
friendly technologies and further fostering a comprehensive range of eco-innovative
business activities. The long-term objective will focus on initiating and maintaining
stronger, broader eco-innovation awareness across, and beyond European Union.
In a supply chain context, an eco-innovation strategic orientation guides firms to
develop products and improve processes using product life-cycle viewpoints, as well as
apply stricter environmental requirements for suppliers. Such a strategy requires
environmental competencies and integrates relevant ecological activities, such as
purchasing, manufacturing, and packaging, to improve current product and process
developments (Chen and Hung, 2014). Therefore an eco-innovation strategy motivates
firms to commit extra resources and cultivate innovative capabilities to build supply
chain sustainability. To reach this goal, firms must develop innovative technologies
that reflect the industry-specific characteristics and nature of the business, which
likely differ from current practices, to improve their environmental performance
(Saeed et al., 2014; Hoejmose, et al., 2013). Therefore, we posit:
H3. An eco-innovation strategic orientation has a positive effect on a firm’s deployment
of (a) green purchasing; (b) green manufacturing; and (c) green packaging.
3.4 Reverse logistics outcomes
Manufacturing companies have become increasingly responsible for collecting,
dismantling, and upgrading used products and packaging materials (Zhu et al., 2012).
Reverse logistics is inherently green and ecologically friendly, because repairing,
refurbishing, or recycling a product instead of throwing it in a landfill protects the
environment. Through reverse logistics, returned goods can be put back into inventory
again, re-sold at liquidation centers, or broken down to component parts for sale
(Aitken and Harrison, 2013) – all steps that can cut costs, increase profits, reduce
negative impacts on the environment, minimize liabilities, and improve customer
relationship (Chavez et al., 2013). Resource commitments to reverse logistics thus
should be a priority (Zailani et al., 2012), because of their potential for enhancing
performance through new value creation and offering strategic means to develop
lasting linkages with customers and positive firm images. These reverse flows differ
from standard, outbound operations and need special handling, likely requiring
additional resource allocations throughout the product lifecycle. Allocating sufficient
resources to support sustainable supply chain initiatives constitutes one of the
principle antecedents of strong reverse logistics programs. Reverse logistics also
depend heavily on reversing the sustainable supply chain, to enable firms to identify
and categorize returned products, components, and packaging materials correctly for
disposition, whether used or unused.
Reverse logistics is a continuous, embedded process, not just a one-time occurrence,
such that it entails a built-in process (De Brito and Dekker, 2004). By affecting many
components of the manufacturing process, reverse logistics expands the
responsibilities of the supply chain. Therefore, reverse logistics demands a thorough
reexamination of product life cycles to determine the amount of energy or waste
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consumed and generated by each product in every stage. The successful
implementation of reverse logistics requires a comprehensive review of operational
processes at every level of the company – from raw material procurement to packaging
(Meade and Sarkis, 2002; Murphy and Poist, 2003).
Accordingly, firms need to implement at least the following strategies into each
supply chain activity to make their reverse logistics work:
(1) Green purchasing: the procurement of environmentally friendly materials alters
purchasing requirements and expands the related criteria (Min and Galle, 2001).
green purchasing promotes recycling and the reclamation of purchased materials,
which creates value if used or unwanted products can be recollected. However,
non-returnable products often are less expensive to produce, and virgin materials
tend to be priced equal to or lower than recycled materials (Walton et al., 1998).
(2) Green manufacturing: research and development can design specifications for
environmentally friendly products, and firms can reengineer their
manufacturing and production processes to rely on the addition of recyclable
materials as part of the process. Green manufacturing considers environmental
impacts throughout the product lifecycle, including the sale of used, unsold, or
returned products in secondary markets (Van Hoek, 1999).
(3) Green packaging: examining current packaging can reveal possible changes
and the potential of gathering leftover packaging or using less packaging
(González-Torre et al., 2004). Green packaging addresses all packaging issues,
including size, shape, and materials. Because reverse logistics entails a process
of continuously taking back products or packaging materials to avoid
environmental damages, it entails not just the use of recycled or recyclable
materials but also the impacts of packaging on distribution arrangements,
such as loading and handling efficiency and space utilization. The packaging
used must be less costly, easy to handle, and environmentally friendly
(Wu and Dunn, 1995). Because greener packaging can reduce reverse logistics
costs, a positive relationship likely exists between the deployment of
sustainable supply chain initiatives and reverse logistics.
Adding sustainability concepts for reverse logistics leads to a comprehensive framework
for integrating green purchasing, green manufacturing, and green packaging
(De Brito and Dekker, 2004). This model also acknowledges that modern customers
prioritize sustainability factors in their strategic agendas in both production and service
sectors (Murphy and Poist, 2000). Firms developing ecologically friendly reverse logistics
networks can minimize the cost of returns, focus on designing recyclable packaging and
pallets, reduce unnecessary deliveries, and exploit green materials for product design
(Rogers and Tibben‐Lembke, 2001). Therefore, we hypothesize:
H4. A firm’s reverse logistic outcomes are positively associated with the deployment
of (a) green purchasing; (b) green manufacturing; and (c) green packaging.
4. Methods
4.1 Sample
We conducted survey in Malaysia among all EMS ISO 14001 – certified firms. By
selecting firms with this certification, we ensure that the respondents have embarked,
at least to some extent, on the adoption of sustainable supply chain initiatives. Of the
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2,255 manufacturing firms in Malaysia, 342 companies had obtained ISO 14001
certification. To obtain reliable data from this limited sample, we applied census
sampling methods and requested that all 342 companies participate by providing input
about their practices.
4.2 Respondents
We received 125 completed questionnaires, for a response rate of approximately
36.5 percent. We describe the responding firms in Table I.
4.3 Measures
We used multiple indicators to measure each research construct based on relevant
literature. The Appendix details the survey instrument. Eco-reputation strategic
orientation is the extent to which the firm maintains its environmental reputation
throughout the product lifecycle. The five measurement items came from Testa and
Iraldo (2010). Eco-innovation strategic orientation is company awareness, as reflected by
its adoption of new ideals and strategies in its supply chain practices. These six
measurement items came from Testa and Iraldo (2010). For both eco-reputation and
eco-innovation strategic orientations, respondents used five-point Likert scales
(1¼ “strongly disagree,” 5¼ “strongly agree”). Green purchasing practices include raw
materials and components content requirements and restrictions, content labeling or
disclosure, supplier questionnaires, supplier EMS certification, and supplier compliance
audits. Six measurement items were adapted from Hammer (2006). Green manufacturing
entails production activities applied to the process, such that inputs have relatively low
negative environmental impact, are highly efficient, and generate little pollution. Seven
relevant items were adopted from Ninlawan et al. (2010) and Zhu et al. (2007). Green
Description Categories Frequency %
Ownership of firm Malaysian fully owned 26 20.8
Joint venture 99 79.2
Number of employees Less than 100 12 9.6
100-250 2 1.6
251-500 17 13.6
501-1000 20 16.0
More than 1,000 74 59.2
Age of the firm Less than 6 years 26 20.8
6-10 years 14 11.2
11-15 years 7 5.6
More than 15 years 78 62.4
Type of products Consumer products 66 52.8
Industrial products 48 38.4
Combination/others 11 8.8
Major source for key materials and components Domestic 11 8.8
Regional/Asian 20 16.0
Global 94 75.2
Number of suppliers for key materials and components Single supplier 6 4.8
2-5 suppliers 31 24.8
6-10 supplier 9 7.2
More than 10 suppliers 79 63.2
Note: n¼ 125
Table I.
Respondent profile
information
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Sustainable
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initiatives
packaging includes characteristics such as size, shape, weight, and materials being used.
We adapted four measurement items from Ninlawan et al. (2010). A five-point Likert scale
measured the three sustainable supply chain initiatives (1¼ “not at all,” 5¼ “very high
extent”). Reverse logistics is the process of retrieving products from end consumers, to
capture value or ensure proper disposal. We took seven items from Ninlawan et al. (2010)
and used a five-point Likert scale (1¼ “not at all,” 5¼ “very high extent”) to assess the
implementation of reverse logistics in each firm.
5. Results
Figure 2 depicts the measurement models and Table II provides the descriptive statistics
and zero-order correlation matrix for the six latent variables. The Cronbach’s α statistics
for the constructs range from 0.904 (eco-reputation) to 0.975 (green manufacturing,
reverse logistics), so the scales appear sufficiently reliable. The composite reliability
statistics range from 0.890 (eco-reputation) to 0.972 (green manufacturing); the minimum
AVE of 0.619 (eco-reputation) also exceeds the threshold value of 0.50.
In Figure 2, the CFA results show that the large and significant standardized
loadings of each measured item on its construct offer evidence of convergent validity.
The AVE statistics indicate excellent convergent validity. All the correlation
coefficients are significant and less than 0.5, in support of discriminant validity.
A more rigorous structural equation model approach is the χ2 difference test between
a constrained and unconstrained model for each pair of constructs. Table III summarizes
the results of these χ2 difference tests, which confirm the discriminant validity of the six
constructs. Nomological validity also is supported, according to the various model fit
indices in Figure 3.
Figure 3 also shows that eco-reputation strategic orientation has a positive effect on
sustainable supply chain initiative components, in support of H2. Specifically, an
eco-reputation strategic orientation related strongly to the deployment of green
purchasing ( β¼ 0.33, po0.05), green manufacturing ( β¼ 0.22, po0.05), and
green packaging ( β¼ 0.28, po0.05). Our results also offer broad support for H3;
eco-innovation strategic orientation related positively to the deployment of green
purchasing ( β¼ 0.26, po0.05), green manufacturing ( β¼ 0.35, po0.05), and
green packaging ( β¼ 0.42, po0.05).
The results provide strong evidence of the positive impact of reverse logistics in terms of
sustaining firms’ supply chain initiatives, though these effects differ according to the
individual sustainable supply chain initiative components. Specifically, in line withH4b and
H4c, green manufacturing (β¼ 0.26, po0.05) and green packaging (β¼ 0.19, po0.05)
initiatives positively affect firms’ reverse logistics outcomes. However, green purchasing
(β¼ 0.08, pW0.05) initiatives exhibited no significant relationship with reverse logistics
outcomes, so we cannot confirm H4a. These findings suggest that green manufacturing
and packaging initiatives are more effective in differentiating firms’ reverse logistic
outcomes than are green purchasing initiatives. Perhaps green purchased materials simply
are less visible in reverse logistics than are green manufacturing and green packaging.
6. Discussion
Most emerging countries have undergone rapid economic development quickly.
The downside to this rapid growth is the host of environmental pollution problems that
have arisen and are of serious global concern. In response, this study makes three
important contributions. First, by collecting empirical data from Malaysia, a major
emerging country, we demonstrate for the first time the specific effects of each
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sustainable supply chain initiative on reverse logistics in a developing economy.
Second, we extend prior research on the role of performance measures in green supply
chain management and uncover how reverse logistics create competitive advantages.
Third, we examine two unique, previously untested drivers of green supply chain
GP
0.01
0.36
0.51
0.25
0.27
C1
C2
C3
C4
C5
0.
99
0.80
0.70
0.87
0.86
0.02 C6
0.99
0.01
0.23
GM
0.00
0.09
0.27
0.16
0.35
D1
D2
D3
D4
D5
1.00
0.95
0.85
0.92
0.81
0.19 D6
0.90
0.11 D7
0.94
0.27
0.07
0.06
Eco-Reputation Strategic Orientation (ERSO) Eco-Innovation Strategic Orientation (EISO)
Green Purchasing Initiative (GP) Green Manufacturing Initiative (GM)
ERSO
0.55
0.45
0.23
0.35
0.32
0.17
A1
A2
A3
A4
A5
0.67
0.74
0.88
0.80
0.82
0.23
EISO
0.16
0.16
0.49
0.11
0.00
0.13
B1
B2
B3
B4
B5
0.92
0.91
0.72
0.94
1.00
0.16 B6
0.92
0.07
0.08
0.15
GK
0.12
0.06
0.28
0.04
E1
E2
E3
E4
0.94
0.97
0.85
0.98
RL
0.27
0.24
0.01
0.00
0.15
F1
F2
F3
F4
F5
0.85
0.87
0.99
1.00
0.92
0.23 F6
0.88
0.03
0.24
Green Packaging Initiative (GK) Reverse Logistics (RL)
�2/df=0.74 /3=0.25, p-value=0.86, RMSEA = 0.000
NFI=1.00, CFI=1.00, RFI=1.00, AGFI=0.99
Cronbach’s �=0.904, CR=0.890, AVE=0.619
�2/df=0.75 /5=0.15, p-value=0.98, RMSEA=0.000
NFI=1.00, CFI=1.00, RFI=1.00, AGFI=0.99
Cronbach’s �=0.969, CR=0.964, AVE=0.820
�2/df=7.60 /7=1.09, p-value=0.37, RMSEA=0.026
NFI=0.99, CFI=1.00, RFI=0.99, AGFI=0.94
Cronbach’s �=0.953, CR=0.950, AVE=0.764
�2/df=2.18 /11=0.20, p-value=0.998, RMSEA=0.000
NFI=1.00, CFI=1.00, RFI=1.00, AGFI=0.99
Cronbach’s �=0.975, CR=0.972, AVE=0.833
�2/df=3.71/2=1.86, p-value=0.16, RMSEA=0.083
NFI=0.99, CFI=1.00, RFI=0.98, AGFI=0.93
Cronbach’s �=0.964, CR=0.966, AVE=0.875
�2/df=3.79/7=0.54, p-value=0.80, RMSEA=0.000
NFI=1.00, CFI=1.00, RFI=0.99, AGFI=0.97
Cronbach’s �=0.975, CR=0.971, AVE=0.850
Notes: �, Cronbach’s �. CR, composite reliability = (�c) = (Σ�)2/ [(Σ�)2+Σ(�)].
AVE, average variance extracted = (�v) = (Σ�2) / [Σ�2+Σ(�)]. where � is the
indicator loadings, and � is the indicator error variances
Figure 2.
Measurement models
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initiatives, eco-reputation strategic orientation and eco-innovation strategic orientation.
The results offer useful theoretical and managerial implications.
Advancing sustainable business practices has the potential to help manufacturing
firms in Malaysia manage their competing goals of profit growth and environmental
protection. Although this model was developed in Malaysia, the findings should apply
to firms in other emerging countries too. Most emerging countries remain in an early
stage of economic development and face trade-offs between wealth creation and potential
negative effects on social and environmental conditions. Manufacturing firms’ apparent
efforts to pursue profit growth at all costs is the leading cause for a country’s dismal
pollution record; unsustainable business practices lead to an array of environmental
problems. The information in this paper can help business leaders in emerging markets
develop sustainable supply chain activities that ensure their business success, through
reverse logistics. Business leaders in emerging countries also should strengthen their
eco-strategic orientation and develop sustainable business practices to enhance
their implementation of reverse logistics, which can help them fulfill their
environmental responsibilities. Building a sustainable business culture is a long
process, but manufacturing firms’ ability to advance sustainable business practices
Mean
( µ)
SD
(σ) ERSO EISO GP GM GK RL
ERSO correlation coefficient
significant (2-tailed) 3.122 0.889 1.000
EISO correlation coefficient
significant (2-tailed) 2.665 0.908 0.351 (0.000) 1.000
GP correlation coefficient
significant (2-tailed) 3.031 1.033 0.396 (0.000) 0.372 (0.000) 1.000
GM correlation coefficient
significant (2-tailed) 2.898 1.027 0.291 (0.000) 0.386 (0.000) 0.281 (0.000) 1.000
GK correlation coefficient
significant (2-tailed) 2.822 0.983 0.387 (0.000) 0.439 (0.000) 0.418 (0.000) 0.362 (0.000) 1.000
RL correlation coefficient
significant (2-tailed) 3.008 1.043 0.322 (0.000) 0.330 (0.000) 0.251 (0.000) 0.284 (0.000) 0.305 (0.000) 1.000
Table II.
Descriptive statistics
and correlations of
the constructs
Constrained model Unconstrained model χ2 Difference test, df¼ 1
Paired analysis χ2 df χ2 df χ2 diff. p-value
ERSO-EISO 50.42 38 39.16 37 11.26 0.0008
ERSO-GP 108.65 40 100.82 39 7.83 0.0051
ERSO-GM 50.51 49 39.40 48 11.11 0.0009
ERSO-GK 49.43 25 39.60 24 9.83 0.0017
ERSO-RL 60.11 40 51.52 39 8.59 0.0034
EISO-GP 128.24 48 118.84 47 9.40 0.0022
EISO-GM 53.46 58 43.58 57 9.88 0.0017
EISO-GK 40.22 31 32.04 30 8.18 0.0042
EISO-RL 59.17 48 49.00 47 10.17 0.0014
GP-GM 73.49 60 63.56 59 9.93 0.0016
GP-GK 61.41 33 55.61 32 5.80 0.0160
GP-RL 149.83 50 140.58 49 9.25 0.0024
GM-GK 36.17 41 26.00 40 10.17 0.0014
GM-RL 46.19 60 36.71 59 9.48 0.0021
GK-RL 55.58 33 44.82 32 10.76 0.0010
Table III.
Discriminant
validity test
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ultimately could contribute to their competitiveness by enhancing their firms’ reputation
and increasing consumer confidence at both international and national levels.
Eco-reputation strategic orientation and eco-innovation strategic orientation can
enable sustainable supply chain initiatives. Our results show that eco-reputation
strategic orientation is positively associated with sustainable supply chain initiatives;
an eco-innovation strategic orientation suggests that sustainable supply chain
initiatives are less risky than failing to adopt such practices. In addition, when the
firm has both an eco-reputation and an eco-innovation strategic orientation, the two
strategic orientations positively affect each other. Most studies of the drivers of
organizational sustainability efforts empirically examine only the independent effects
of the antecedent variables; our results suggest the antecedents also can interact in
their effects, so they enhance understanding of when and why firms engage
in sustainable activities. Incorporating bi-directional relationships thus can enhance
knowledge of the drivers of organizational sustainable involvement. Furthermore,
most sustainable supply chain studies draw their conceptual framework from the
notion of institutional theory, with the assumption that external institutional forces
motivate a company’s implementation of sustainable initiatives (Kauppi, 2013).
Our findings suggest that the enablers of sustainable business practices may not be
as divergent as has commonly been assumed. Instead, engaging in sustainable
supply chain initiatives might combine the organization’s overall strategic
orientations with respect to the natural environment and deliver environmental
benefits (Stonebraker and Liao, 2004).
The results highlight the potential value of simultaneously examining different
components of sustainable supply chain initiatives. The limited research in
developing economy domains tends to focussed on a single aspect of sustainable
supply chain initiatives. Our analyses reveal relatively strong positive correlations
among the three different sustainable supply chain initiatives (Table II) but also
indicate that each initiative can have different impacts on outcomes in different
conditions. Empirical research on sustainable supply chain management should
allow for this possibility.
Sustainable Supply
Chain Initiatives
OutcomeStrategic Orientations
Eco-Reputation
Strategic Orientation
(ERSO)
Eco-Innovation
Strategic Orientation
(EISO)
Green
Manufacturing (GM)
Green
Packaging (GK)
Green
Purchasing (GP)
Reverse Logistics
(RL)
0.33
0.22
0.28
0.50
0.26
0.35
0.42
0.08
0.26
0.19
Insignificant path
Significant at �=2.5%
�2/df = 780.14/504 = 1.548, RMSEA = 0.066, NFI = 0.94, NNFI = 0.97, CFI = 0.97, IFI = 0.97, RFI = 0.93, PGFI = 0.62
ECVI: Model = 7.76, Saturated Model = 9.60, Independence Model = 123.10
CAIC: Model =1,310.51, Saturated Model = 3,467.85, Independence Model =15,395.17
Figure 3.
Results of structural
equation model
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7. Management implications
This study also offers important new insights for managers. Figure 4 synthesizes the
results with their related managerial implications. Our findings offer needed empirical
support for investing in sustainable supply chain initiatives, and we offer strong
evidence of the benefits obtained from reverse logistics in sustainable supply chain
initiatives (Alblas et al., 2014). Managers can be confident that sustainable supply
chain initiatives will benefit their firms’ reverse logistics. Ecological requirements are
key criteria for products and production, particularly for companies that seek ways to
ensure economic sustainability by staying competitive and profitable. This study offers
empirical evidence that implementing sustainable supply chain initiatives leads to
reverse logistics, creating value, and a new source of competitive advantages
( Jayaraman and Luo, 2007).
For managers interested in developing sustainable supply chain initiatives, our
results also offer some alternatives. Decision makers in the firms attempting to nurture
sustainable supply chain initiatives should not overlook the importance of
eco-reputation and eco-innovation strategic orientations, which our results identify
as important enablers (Awaysheh and Klassen, 2010). In general, making sustainable
supply chain investments is easier if an ecological strategic orientation is a top priority
within the firm or top managers emphasize eco-friendly business practices as sources
of the company’s image (Roehrich et al., 2014). However, managers also need to attend
carefully to the bi-directional relationship between eco-innovation and eco-reputation
strategic orientation. In the presence of both, managers may find it easier and more
effective to emphasize green supply chain activities.
The significant reverse logistics benefits stemming from sustainable supply chain
initiatives suggest that manufacturing companies cannot only receive products back
from consumers but also collect unsold merchandise, to take apart, sort, reassemble, or
recycle. The returned product also can be re-sold in secondary channels and generate
revenue. Managers thus should emphasize the strategic benefits of sustainable supply
chain initiatives, rather than regarding reverse logistics as a cost center. They
also should place more emphasis on the benefits of environmental sustainability,
to encourage their firms to become environmentally sensitive.
8. Limitations and future studies
This research has some limitations, and our findings also suggest several avenues for
research. First, we collected most of the data from a single key informant in each
Malaysian company. The potential thus exists for key informant common method bias,
though we followed the recommendations by Phillips (1981) and used credible
respondents (i.e. EMRs) to minimize this threat. Further research employing
multi-informant designs or direct investigator observations might be useful though,
to confirm our results. Second, our sample included manufacturing companies in
Malaysia; we cannot guarantee that our results generalize to different industries.
This research was developed primarily among manufacturing firms, with little
consideration of the green supply chain behavior of service sectors. Thus, we
encourage further research to examine the applicability of our findings to service
sectors (Hill and Brown, 2007). Third, though we identify three components of
sustainable supply chain initiatives, other components could exist, and ongoing
research should investigate them. Forth, internal stakeholders, such as employers, are
critical to drive sustainable purchasing. However, the research design of our study
focusses on the firm’s strategic orientations in general; we do not aim to understand
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s
Figure 4.
Managerial
implications
103
Sustainable
supply chain
initiatives
each stakeholder’s behavior. Noting the importance of internal stakeholders, we now
address this concern as a potential future study. Fifth, we collected our sample from
certified manufacturing firms, which tend to be larger and relatively resource
abundant. Excluding small and medium-sized enterprises (SMEs) from our study might
bias our findings. The incentives for SMEs to develop sustainable supply chains thus
may differ from those that drive well-established firms. Moreover, with their limited
resources, their ability to implement reverse logistics is questionable. Further research
should test our results and validate our model using non-certified SMEs.
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Appendix. Survey instrument
I. Strategic orientations
This section assesses the strategic green orientations that firms use to implement green supply
chain initiatives. We ask respondents to indicate on a five-point Likert scale (1¼ strongly
disagree, 5¼ strongly agree) how the following focusses, concepts, and practices guide their
firm’s green supply chain initiatives in terms of green purchasing, green manufacturing, and
green packaging.
(A) Eco-reputation strategic orientation (ERSO)
A1. Our company is well-known for environmentally responsible and contributes in green
initiatives.
A2. Consumers recognize our company and products due to our involvement in various green
activities.
A3. Our company policy promotes green initiatives/activities and we have made good
progress.
A4. Our company respects environmental welfare and is committed to develop green products.
A5. We enhance our firm’s image and reputation through development of green initiatives.
(B) Eco-innovation strategic orientation (EISO)
B1. Our company allocates adequate resources for new green innovation initiatives/activities.
B2. Our top management emphasizes process and product innovation that promotes
green initiatives.
B3. Green life-cycle assessment is an important criterion while developing new products.
B4. Our company competes on innovative driven goals and green initiatives.
B5. Innovation culture is well-established in my company.
B6. Our company aggressively conducts training and education in innovation-based green
initiatives.
II. Sustainable supply chain initiatives
This section assesses the extent of existence of sustainable supply chain initiatives
(green purchasing, green manufacturing. and green packaging), We ask respondents to
indicate on a five point Likert scale (1¼ not at all, 5¼ very high extent) the existence of the
following initiatives in their firm.
(C) Green purchasing (GP)
Cl. Provides suppliers with design specifications that include green or environmental
requirements.
109
Sustainable
supply chain
initiatives
C2. Purchases materials from suppliers who are qualified in green partner environmental
standards.
C3. Audit suppliers regularly to ensure that they are in compliance with environmental
regulations.
C4. Requires key suppliers to be certified in EMS, such as ISO 14001.
C5. Ensure purchased components are free of undesirable items such as lead or hazardous
materials.
C6. Evaluates suppliers based on specific environmental criteria.
(D) Green manufacturing (GM)
D1. Produces products with reused or recycled contents such as recycled plastics and glass.
D2. Uses life-cycle assessment to evaluate the environmental load of products.
D3. Produces products that are free of hazardous substances, such as lead, mercury, and
chromium.
D4. Designs products to ensure they have recyclable or reusable contents.
D5. Produces products that reduce the consumption of materials and energy during use.
D6. Reduces power consumption in products during manufacturing and transportation.
D7. Increases product life-span resulting in higher efficiency and productivity.
(E) Green packaging (GK)
El. Makes sure that packaging uses renewable or recyclable contents.
E2. Makes sure that packaging is reusable.
E3. Minimizes the use of materials in packaging.
E4. Avoids or reduces the use of hazardous materials in packaging.
III. Reverse logistics
This section assesses the extent of reverse logistics implementation in the firm for the purpose of
reuse, recycle, or reclamation of materials from the product or packaging. We ask respondents to
indicate on a five-point Likert scale (1¼ not at all, 5¼ very high extent) the existence of reverse
logistics implementation in their firm.
(F) Reverse logistics (RL)
Fl. Collects used or unwanted products from customers for recycling, reclamation of materials,
or reuse.
F2. Collects used packaging from customers for reuse or recycling.
F3. Requires customers to collect packaging materials for us.
F4. Collects used or unwanted products from customers for remanufacturing.
F5. Collects shipping materials from customers for reuse or recycling.
F6. Returns products to customers after refill or repair.
Corresponding author
Dr Chin-Chun Hsu can be contacted at: vincent.hsu@unlv.edu
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reproduction prohibited without permission.
Appendix.Survey instrument
lable at ScienceDirect
Journal of Cleaner Production 129 (2016) 608e621
Contents lists avai
Journal of Cleaner Production
journal homepage: www.elsevier .com/locate/ jc lepro
Critical success factors for reverse logistics in Indian industries: a
structural model
Sachin Kumar Mangla a, Kannan Govindan b, *, Sunil Luthra c
a Department of Mechanical Engineering, Graphic Era University, Dehradun, India
b Center for Sustainable Engineering Operations Management, Department of Technology and Innovation, University of Southern Denmark, Denmark
c Department of Mechanical Engineering, Government Polytechnic, Jhajjar, Haryana, India
a r t i c l e i n f o
Article history:
Received 22 February 20
15
Received in revised form
22 February 2016
Accepted 16 March 2016
Available online 12 April 2016
Keywords:
Reverse logistics (RL)
Critical success factors (CSFs)
Sustainability
AHP
DEMATEL
Indian manufacturing industries
* Corresponding author.
E-mail address: kgov@iti.sdu.dk (K. Govindan).
http://dx.doi.org/10.1016/j.jclepro.2016.03.124
0959-6526/
© 2016 Elsevier Ltd. All rights reserved.
a b s t r a c t
Industries face significant pressures to enact eco-friendly practices in their supply chain due to the
constraints of natural resources and growing ecological awareness among customers. Reverse logistics
(RL) has been considered as a systematic approach for industries to improve their environmental impacts
and to ensure sustainability in business. Industries are enthusiastic to adopt RL activities in their busi-
nesses, but they also face challenges such as insufficient knowledge and resources regarding RL imple-
mentation. Therefore, we seek to evaluate the critical success factors (CSFs) linked to the implementation
of RL in manufacturing industries in India. In this work, a structural model is proposed by using
Analytical Hierarchy Process (AHP) and Decision Making Trial and Evaluation Laboratory (DEMATEL)
methods to evaluate the CSFs in RL adoption. The AHP methodology assists in establishing the priorities
of the CSFs, while the DEMATEL approach categorizes the causal relationships among them. The findings
of this work shows that the Global competitiveness main factor is highly prioritized, and thus, needs to
be focused greatly in order to increase the effectiveness of RL adoption in business. The relative priority
of the remaining main factors through AHP analysis is given as Regulatory factors – HR and organizational
factors -Economic factors – Strategic factors. The findings also indicate that Global competitiveness;
Regulatory; HR and organizational main factors are classified under cause group, while Economic and
Strategic main factors belong to effect group. This model will help business analysts and supply chain
managers formulate both short-term and long-term, flexible decision strategies for successfully man-
aging and implementing RL adoption in the supply chain scenarios.
© 2016 Elsevier Ltd. All rights reserved.
1. Introduction
Conservation of the environment has taken a prime position
among areas of concern for managers and practitioners all across
the globe. Likewise, customers are more environmentally
conscious, which creates a demand for industries to adopt clean,
green, eco-friendly processes for their businesses (Millet, 2011;
Sarkis et al., 2011; Almeida et al., 2013; Seuring and Gold, 2013;
Luthra et al., 2014a, 2015a; Gandhi et al., 2015). Growing
competitive and financial pressures, diminishing product life cy-
cles, and stringent environmental rules have increased the
attention paid to green and Reverse Logistics (RL) activities that
industries embrace to improve their environmental impacts
(Subramoniam et al., 2009; Chan et al., 2012; Mangla et al., 2013;
Zhu and Geng, 2013). RL comprises all operations related to the
recovery and reuse of products and materials, and proves to be a
rational instrument for industries to improve their firms’ sus-
tainability in terms of ecological, economic, and social gains
(Schwartz, 2000; Sarkis, 2003, 2010; Zhang et al., 2011; Nikolaou
et al., 2013; Abdulrahman et al., 2014). In addition, RL opera-
tions and its related practices are also proven to be crucial in
reducing operational expenses (PricewaterhouseCoopers’ report,
2008). RL has gained attention among business organizations as
an effective, strategic approach to improving profitability, product
lifecycles, supply chain complexity, consumer preferences, and
reducing environmental impact (Thierry et al., 1995; Fleischmann
et al., 1997; Carter and Ellram, 1998; Van Hoek, 1999; Stock, 1998,
2001; Toffel, 2003; Neto et al., 2008; Tsai et al., 2009; Hu and
Bidanda, 2009; Gunasekaran and Spalanzani, 2012; Govindan
et al., 2015).
Delta:1_given name
Delta:1_surname
Delta:1_given name
http://crossmark.crossref.org/dialog/?doi=10.1016/j.jclepro.2016.03.124&domain=pdf
mailto:kgov@iti.sdu.dk
http://crossmark.crossref.org/dialog/?doi=10.1016/j.jclepro.2016.03.124&domain=pdf
www.sciencedirect.com/science/journal/09596526
http://www.elsevier.com/locate/jclepro
http://dx.doi.org/10.1016/j.jclepro.2016.03.124
http://dx.doi.org/10.1016/j.jclepro.2016.03.124
http://dx.doi.org/10.1016/j.jclepro.2016.03.124
S.K. Mangla et al. / Journal of Cleaner Production 129 (2016) 608e621 609
However, the adoption and implementation of RL practices is
relatively difficult from industrial viewpoints. Many industries are
comparatively less familiar with how to initiate RL and what ben-
efits could be realized through implementing RL practices (Chan
and Kai Chan, 2008). To deal with this uncertainty, scholars and
practitioners have tried to isolate the important determinants of
initiation and implementation of RL among industries (Vijayan
et al., 2014).
Several factors are vital to the successful implementation of RL
in business, such as management commitment, globalization, reg-
ulations, consumer requirements, financial resources, competi-
tiveness, and benchmarking (Jindal and Sangwan, 2011; Chio et al.,
2012; Mangla et al., 2013). Given that these factors are critical for
industries in order to adopt RL efficiently (Chio et al., 2012), we
need to identify and evaluate the various Critical Success Factors
(CSFs) required for the implementation of RL practices in the in-
dustrial supply chain.
The goal of this work is to evaluate the CSFs related to initiation
and implementation of RL on tactical (or operational) and strategic
levels in business. It is no surprise that different industries may
exhibit different perceptions in adopting RL practices in their
respective businesses (Srivastava and Srivastava, 2006). To
acknowledge these considerations and to achieve the above
formulated objectives, a two phase-methodology is introduced and
used in this work. In the first phase, various CSFs that assist in the
implementation of RL from the industrial viewpoint are deter-
mined. For this phase, several different industries operational in the
western region of India were examined. A literature survey and
discussions from industrial experts resulted in a collection of the
most commonly accepted RL implementation CSFs. In the second
phase, the finalized common RL implementation CSFs were sub-
jected to evaluation, using Analytical Hierarchy Process (AHP) and
Decision Making Trial and Evaluation Laboratory (DEMATEL)
methods, through the input of industry and field experts. The AHP
method (Saaty, 1980) helps to prioritize or to identify the essential
RL implementation CSFs. On the other hand, the DEMATEL method
(Gabus and Fontela, 1972) is used to study the interrelationships
between the RL implementation CSFs with the help of a causal map.
It assists practicing managers and policy makers to prepare both
short-term and long-term flexible decision strategies that will
prove beneficial for performance improvements of RL imple-
mentation from an industrial perspective.
The remainder of the paper is arranged as follows. Literature
relevant to this work is discussed in Section 2. Section 3 provides
detail on the proposed research methods. The proposed research
framework is given in Section 4, and its application to Indian
manufacturing industries is presented in Section 5. Next, Section 6
identifies results obtained from the research and their implications.
Finally, research conclusions are presented in Section 7, along with
limitations and scope for future work.
2. Relevant literature
The present section includes the literature on RL implementa-
tion in industries in Indian context, RL implementation factors, and
draws the research gaps for this study.
2.1. Industrial RL implementation in India
RL can be expressed as the process of planning, implementing,
and regulating the efficient and cost effective flow of rawmaterials,
in-process inventory, finished goods, and related information from
the point of consumption to the point of origin for the purpose of
recapturing value or proper disposal (Rogers and Tibben-Lembke,
2001; De Brito and Dekker, 2004; Blumberg, 2005; Meade et al.,
2007; Wadhwa et al., 2009). With regard to the adoption and
implementation of RL initiatives, Abdulrahman et al. (2014) argued
that RL literature in developing countries context is still in its in-
fancy state. India accounts for approximately 17.5% of the world’s
population. Due to industrialization, manufacturing industries are
growing at a rapid pace, leading to the generation of a huge amount
of hazardous and non-hazardous waste. According to Comptroller
and Auditor- General’s (CAG) report, over 7.2 MT of industrial or
hazardous waste was generated in India in 2000, out of which 1.4
million tonswas recyclable, 0.1 million tons was incinerateable, and
5.2 million tons was destined for disposal on land (MoEF, 2000). In
addition, India Central Pollution Control Board (CPCB, 2000)
documented that some 41,523 industries in the country generate
about 7.90 million tons of hazardous/industrial waste every year, of
which recyclable hazardous waste is 3.98 million tons (50.38%),
landfill waste is 3.32 million tons (42.02%), and incinerateable
waste is 0.60 million tons (7.60%). The waste sector in India has
evolved greatly in last 15 years (from 2000 onwards) and waste is
generated in several forms such as industrial waste, e-waste, and
bio medical waste, municipal waste. According to the report of
Novonous waste management market in India is expected to be
worth US$ 13.62 billion by 2025. It is expected that. E-waste
management market is likely to grow at a compound annual
growth rate (CAGR) of 10.03% by 2025. Bio medical waste man-
agement market may grow at a CAGR of 8.41% (Novonous, 2014). In
addition, the generation andmanagement of Municipal SolidWaste
(MSW) is also becoming a serious problem for India. Indian MSW
management market is likely to grow at a CAGR of 7.14% by 2025.
Based on the latest report of CPCB (CPCB, 2014) 1, 27,486 Tons per
day (TPD) of MSW was generated in India in 2011e12. Of which,
only 15,881 TPD (12.50%) was processed for eco-friendly disposal
(MoEF, 2012e13). Recently, Kannan et al. (2016) suggested that
formal recycling of e-waste could contribute to sustainable society.
From these numbers, we can conclude that there is a substantial
scope of RL implementation within Indian industries that, if
implemented, would be crucial not only in reducing the amount of
waste but also in improving organizational, ecological, financial,
and competitive performance levels (MoEF, 2012e13).
RL is distinguished as a crucial means to lower the waste gen-
eration and to prevent pollution by managing the environmental
burden of products after their end-of-life (Ravi, 2012). However, the
concept of RL is not as popular among Indian business organiza-
tions (Ravi et al., 2005; Hung Lau and Wang, 2009; Sharma et al.,
2011). In India, this hesitance may be due to lack of support from
top management and other business partners; these decision
makers are often not ready to spend more money to implement RL
solutions after investing large amounts of capital to set up the fa-
cility and infrastructure (Ravi et al., 2005). Also, governmental
support has an influence on the strategic decision of RL imple-
mentation for any organization. Analyzing some RL studies relevant
to Indian contexts, Jindal and Sangwan (2011) listed and analyzed
sixteen barriers to the implementation of RL through their litera-
ture studies. They find that RL practices can play an important role
in achieving sustainability in Indian business contexts. In their
study, Sharma et al. (2011) analyzed barriers in context to Indian
industries for RL implementation and segregated factors into
driving factors and driven factors. Srivastava and Srivastava (2006)
examined several categories of products in order to make a sys-
tematic understanding of the possibility of implementing RL in
Indian context. Ravi et al. (2005) described the management of RL
operations by investigating a paper industry. Pati et al. (2008)
presented a mixed integer goal programming model to help in
the appropriate management of the RL system through paper
recycling in India. Govindan et al. (2012) analyzed third party RL
providers with the help of interpretive structural modeling by
S.K. Mangla et al. / Journal of Cleaner Production 129 (2016) 608e621610
taking a case study from a tire company. Diabat et al. (2013)
examined the interaction among major barriers hindering the
implementation of third-party logistics in Indian manufacturing
industries. Lack of qualification for employees in third-party lo-
gistics provider and fear of employees of the firm have been found
as the most hindering barriers in implementation of third-party
logistics. Mangla et al. (2013) recognized and analyzed fourteen
variables related to the handling and returning of products by
closing the loop of a green focused supply chain in paper mill in-
dustries in an Indian perspective. Researchers have determined
that the different variables associated with the initiation and or
implementation of product recovery activities (i.e., RL initiatives)
are important to distinguish, and their subsequent analysis may
help the decision makers to achieve higher ecological-economic
benefits (Mangla et al., 2012).
In addition, in the 12th Five Years Plan (2012e2017), it appears
that RL is being practiced in India, but it is still in unorganized
sectors and not much consideration is given to improving envi-
ronmental performances. Under these considerations, Critical
Success Factors (CSFs) of RL implementation need to be identified
and analyzed more rigorously. This step would help industries in
India to implement RL in their respective businesses, and to
approach RL in a more organized chain. It will further assist Indian
industries to improve their economical, social, and environmental
performances, and it should strengthen sustainability in business
(Jindal and Sangwan, 2011).
2.2. RL implementation factors
Gonz�alez-Benito and Gonz�alez-Benito (2006) confirmed that
pressure from stakeholders and the values and beliefs endorsed by
the manager’s environmental awareness leads more quickly to the
implementation of eco-friendly practices in logistics operations. It
also reveals the fact that the organizations with environmentally
aware managers tend not to follow a reactive approach; instead,
they are more proactive towards eco-friendly requirements. How-
ever, in accordance with the study conducted by Chio et al. (2012),
the successful implementation of RL leads to improvisation in the
organization’s performance, financial position, and competitive
advantage. In the same work, these authors also insist that a suc-
cessful implementation of RL is only possiblewith topmanagement
support and commitments. The foremost requirement of all is the
integration of every function for a smooth flow of material in both
(forward and reverse) directions.
Nevertheless, there are several external and internal factors
governing the effective and efficient implementation of RL prac-
tices in the supply chain. Some of the external and internal factors
suggested by researchers are government regulations, customer
demand, policy entrepreneurs, support of top management,
stakeholder commitment, incentive systems, quality of inputs, and
vertical integration (Srivastava, 2008; Hung Lau and Wang, 2009;
Tsai et al., 2009; Rahman and Subramanian, 2012; Dowlatshahi,
2012). Ho et al. (2012) concluded that internal and external fac-
tors significantly influence RL. They suggest that financial and hu-
man resources play an important role in companies’
implementation of RL, whereas tangible resources do not have
much influence on the practice. They also declare that companies
with excellent collaboration and relationship with other business
partners can make use of RL more effectively and efficiently (Ho
et al., 2012). Rogers and Tibben-Lembke (1999) identified several
key RL management elements, including asset recovery, compact-
ing disposition cycle time, centralized return centers, gate keeping,
zero returns, negotiation, RL information systems, remanufacture
and refurbishment, financial management, and outsourcing. Carter
and Ellram (1998) listed some critical RL implementation factors
given as regulations, customer demand, policy entrepreneurs, and
so forth.
It has been stated that the critical (key) success factor theory
enables managers to know the importance of process improvement
for their company (Grimm et al., 2014). The theory of critical suc-
cess factors is primarily based on strategy research, which recog-
nizes the functions, activities, and measures to improve a
company’s competitive advantage from an organizational supply
chain context (Dinter, 2013; Vasconcellos and S�a, 1988). Hence, it is
important to align Critical Success Factors (CSFs) with the firm’s
desired outcome. However, constant supervision is required to
recognize CSF and its relevant activities to support decision making
and to develop high performance management systems, especially
in supply chains (Bai and Sarkis, 2012). Therefore, the identification
of CSF in terms of both how and why is important steps in adopting
and implementing RL initiatives from a supply chain context.
2.3. Research gaps
The benefits of RL implementation are not yet fully realized in
some of the world’s emerging economies. The adoption and
implementation of RL practices is also relatively difficult frommany
industrial viewpoints (Prakash and Brua, 2015). While a lot of
attention has been paid to the implementation of RL practices in
developed countries, there is still a lot to do in a developing country
like India (Jindal and Sangwan, 2011; Sharma et al., 2011;
Subramanian et al., 2014). Govindan et al. (2015) suggested in
their research that multi objective decision making is still a gap in
different studies as compared to single objective analyses in the
area of RL/CLSC. As real world problems are rarely single objective
only, it is necessary for researchers to pay more attention to multi
objective functions instead of single objective ones (Govindan et al.,
2015).
From the extensive literature, we observed that several enablers
and barriers exist to implement RL activities in the business (Jindal
and Sangwan, 2011; Chio et al., 2012; Bouzon et al., 2016). To the
best of our knowledge, the specific consideration of CSFs in the
implementation of reverse logistics to maximize sustainable ad-
vantages is not covered in the literature. Business organizations face
many complexities and challenges in implementing RL activities.
Thus, this work aims to identify the RL implementation CSFs to
provide a theoretical ground for the managers by showing the role
of identified CSFs in RL implementation initiatives. The identified
CSFs can help in understanding the realistic issues to adopting RL
practices from an organizational supply chain perspective. Hence,
within the framework and understanding of the theory of CSFs, the
present research seeks to identify and analyze CSFs to contribute to
successful implementation of reverse logistics from the Indian
manufacturing industry perspective. Tomeet the above highlighted
research gap, the AHP and DEMATEL methods have been used;
other details about the application of AHP and DEMATEL are given
in next section.
3. Research methods
This section presents the description of the proposed and uti-
lized research methods. The AHP method has been used to rank
factors according to their significance on the basis of industry ex-
perts’ opinion. However, there is a need to determine the causal
interactions between factors useful for managers in framing short-
term decision making strategies (Najmi and Makui, 2010). DEMA-
TEL is recognized as a powerful tool in dealing with the issue; it
portrays a basic concept of contextual relation among the elements
S.K. Mangla et al. / Journal of Cleaner Production 129 (2016) 608e621 611
of the system. The DEMATEL method can evaluate decision ele-
ments by signifying the interdependence between them, which
may help policy makers to frame long-term decision strategies
(Chio et al., 2012). Thus, the AHP and the DEMATEL methods when
applied together will give a clearer illustration of use for industries
to plan both the tactical or operational and the strategic decision
strategies. However, the details of the research methods are given
in the following sub-sections.
3.1. AHP method
AHP, first introduced by Thomas L. Saaty (1980), is a flexible
multi criteria decision analysis technique, designed to solve un-
structured decision problems. The AHP technique is based on the
fundamentals of the decomposition of the problem, of the pair-
wise assessments, and finally of the generation and synthesis of
priority vector (Ho, 2008; Sarmiento and Thomas, 2010; Luthra
et al., 2015b). In contrast to the analytic network process (ANP),
AHP is a linear evaluation technique. On the other hand, it needs to
develop several pair-wise assessment matrices in ANP, and in
addition, it involves a complex survey process for non-expert’s
viewpoint (Harputlugil et al., 2011). The methodology of AHP en-
ables the managers to analyze the complicated system more easily
(Vaidya and Kumar 2006; Talib et al., 2011; Govindan et al., 2014;
Mani et al., 2014; Kumar et al., 2015; Mangla et al., 2015c). How-
ever, AHP has several limitations as well, given as (Ishizaka and
Labib, 2009):
� Rank reversal (i.e. changes in the importance ratings whenever
criteria or alternatives are added-to or deleted-from the initial
set of criteria or alternatives compared).
� The assumption of criteria independence.
� The use of judgment scales whilemaking pair-wise comparisons
may involve ambiguity and human bias.
The steps involved in employing the AHP methodology (Chang
et al., 2007; Madaan and Mangla, 2015) for this research are
described as below:
Step 1: To define the goal: The goal of this research, i.e. to
evaluate the success factors in implementation of RL, is defined.
Based on this, the factors and sub-factors are established that
help in structuring a decision hierarchy. The sources of literature
and expert judgments will be crucial for this.
Step 2: To collect data and form the pair-wise evaluations: In
this step, data is collected to frame the pair-wise evaluations
among factors. A judgment matrix (designated as ‘A’) is formed
which is used for calculating factor priorities. Let A1, A2 … An, be
the set of stimuli. The computed judgments on a pair of stimuli
Ai, Aj, are denoted as,
A ¼ �aij
�
where; i; j ¼ 1;2;&;n: (1)
The survey instrument in terms of questioners’ evaluation can
be used to collect data. Based on the data collected, the rating or
pair-wise evaluations among the factors are acquired by means of a
nine rating Saaty’s scale, which assists to achieve numerical
quantities representing the values of aij (elements of the pair-wise
comparison matrix) transformed from verbal judgments.
Step 3: To attain the Eigen values and Eigen vectors: In this step,
the framed pair-wise evaluation matrices were operated in or-
der to obtain the importance weights of the factors. Based on
obtained importance weights, the priority for the respective
factor is attained.
3.2. DEMATEL method
DEMATEL approach was developed by Science and Human Af-
fairs Program of the Battelle Memorial Institute of Geneva some-
where in 1972 and 1976 (Gandhi et al., 2015). This method relies on
graph theory, and enables an analysis of complicated problems by
means of visualization techniques (Lin, 2013). Compared to inter-
pretive structural modeling (ISM), the methodology of DEMATEL, on
the other hand, assists in capturing the contextual relations be-
tween elements in the system and defining the strength of their
interrelationships, as well (Wu, 2008). The procedural steps of
DEMATEL methodology (Tzeng and Huang, 2011; Jia et al., 2015)
with regard to this work is given as follows:
Step 1: To define the goal and factors to be evaluated: In this
step, a critical review of literature is required to explore and
gather relevant data. The expert’s judgment is also crucial in this
step for discussion on the issue to achieve the goal. The probable
factors associated with the effective implementation of RL are
selected and finalized as factors to be evaluated from the in-
formation gathered and expert judgments.
Step 2: To form the initial direct relation matrix and average
matrix (M): An initial relation matrix is formulated based on the
direct influence between any two factors and is obtained
through the expert’s judgment by asking them to score the
factor on the basis of scale given as, 0e ‘No influence’; 1e ‘Little
influence’; 2 e ‘High influence’; 3 e ‘Very high influence’.
If ‘n’ be the number of factors and ‘k’ be the number of re-
spondents with 1 � k � H, then for each respondent (n � n) non-
negative matrices can be established as Xk ¼ [xkij]. The notation
‘xij’ indicates the degree to which the expert conceives that factor i
affects factor j. Based on this, it can be possible to construct X1, X2,
X3 …, XH matrices given by H respondents respectively (H repre-
sents the number of experts). To incorporate all opinions from H
respondents, the average matrix or the average direct relation
matrix A ¼ [aij] is constructed by means of Eq. as follows:
mij ¼
1
H
XH
K¼1
xkij: (2)
Step 3: To compute the normalized direct-relation matrix (D):
The average matrix (M) is transformed into a normalized direct-
relation matrix by using the Eq. given below,
D ¼ M � S (3)
where, S ¼ min
2
6664
1
max
Pn
j¼1jmijj
; 1
max
Pn
i¼1jmijj
3
7775 .
Step 4: To attain the total relation matrix (T): The total relation
matrix (T) is computed by using the Eq. given below:
T ¼ DðI � DÞ�1 (4)
where ‘I’ is the identity matrix, after attaining the Matrix
T ¼ [tij]n�n, the summation of all the rows and columns are
calculated.
Let [ri]n�1 and [cj]1�n be the vectors representing the sum of
rows and sum of columns of the total relationmatrix respectively. ri
S.K. Mangla et al. / Journal of Cleaner Production 129 (2016) 608e621612
summarizes both direct and indirect effects imparted by factor ‘i’ to
the other factors, whereas, cj depicts both direct and indirect effects
received by factor ‘j’ from the other factors. Sum (ri þ cj) known as
‘Prominence’ demonstrates the total effects given and received by
factor ‘i’, whereas the difference (ri – cj) known as ‘Relation’ dem-
onstrates the net effect through which factor ‘i’ impacts the system.
Specifically, if the value (ri – cj) is positive, factor ‘i’ is in the net
cause group, while factor ‘i’ will be in the net receiver group if the
value (ri – cj) is negative (Tzeng et al., 2007).
4. Proposed research framework
The research framework for evaluating the CSFs in effective
adoption and implementation of RL practices, based on the AHP
and DEMATEL methods, consists of three phases. Phase 1: identi-
fication of the most common RL implementation CSFs from litera-
ture resources and from industrial and field expert inputs. Phase 2:
prioritizing the CSFs to develop the short-term, flexible decision
plans in order to adopt RL practices using the AHPmethod. Phase 3:
analyzing the causal interactions among CSFs to formulate the
long-term, flexible decision strategies in order to adopt RL practices
using the DEMATEL method. The research framework for evalu-
ating the CSFs in implementation of RL in Indian manufacturing
industries is shown in Fig. 1.
5. An application example of the proposed model to
manufacturing industries in India
5.1. Data collection
The main source of the data collection is manufacturing com-
panies operational in the western region of India. A total of 50
manufacturing companies were targeted for the data collection.
These companies were covered under convenience sampling, not
Fig. 1. Proposed Rese
random sampling. Companies were selected on the basis of prior
experience and using personal contacts. There is no formula for
taking sample size in convenience sampling. It all depends upon
the on cost and resources needed for data analysis and time limits
to complete the project. Due to cost and resources and time con-
straints, it is assumed that the considered sample size would be
sufficient and representative of the population under analysis.
Further, after frequent phone calls, e-mails and meetings, 42
companies agreed to take part in the process in the end. A ques-
tionnaire was formed and circulated among various middle and
senior level managers and field experts of the manufacturing
companies in question to collect data needed for this research
work. The selected managerial and field experts are highly profi-
cient in their respective domains and have an industrial experience
of more than 10 years. The middle and lower level managers were
primarily selected for data collection, because they are primarily
involved in strategic decision making of adoption and imple-
mentation of RL initiatives from the industrial context (Mangla
et al., 2015a). After having several discussion sessions and group
meetings with experts, a total of 42 replies were collected. Out of
these 42 replies, 30 replies were found suitable in all respects (i.e.
completely filled). These 30 replies were examined for further
analysis. The response rate was nearly around 60%, which is
acceptable. Further, according to Malhotra and Grover (1998), a
response rate of above 20% is considered as a reasonable one. The
basic profile of the respondent industries is shown in Table 1.
The data collected is used in three phases as described in the
following sub-sections.
5.1.1. Phase 1: identification and selection of the common RL
implementation critical success factors
Initially twenty-two CSFs for the implementation of RL were
identified on the basis of literature review. Later, a questionnaire
was formed and mailed to different manufacturing industries in
arch framework.
Table 1
Basic profile of the respondent industries.
S. No. Basic data of respondents Criteria Number of
respondent
1 Type of industry Paper industry 14
Sugar industry 05
Heavy engineering 05
Automobile industry 14
Iron and steel industry 04
Total 42
2 Annual turnover
(in Indian rupees)
Less than or Equal to
1000 Millions
15
1001 to 5000 Millions 20
More than 5000 Millions 07
Total 42
3 Nature of business Original Equipment
Manufacturer
06
Supplier 36
Total 42
4 Average numbers
of suppliers
Less than or Equal to 50 04
50 to 200 20
More than 200 18
Total 42
5 Environmental
management system
Yes 30
No 0
In Progress 12
Total 42
Source: Industry log book, data records, and expert inputs.
S.K. Mangla et al. / Journal of Cleaner Production 129 (2016) 608e621 613
India for their inputs to rank the significance of each factor on the
scale of 1e5 (where, 1-least significant, 2- less significant, 3- sig-
nificant, 4- high significant, and 5-most significant). The purpose of
this scalewas to rate the importance of initially identified 22 factors
with regard to the discussion sessions arranged with the experts
according to the effective adoption and implementation of RL ini-
tiatives in Indian context. The factors with a rating of 1 or 2 were
decided to be deleted, but we retained ratings of 3 and above for
each factor. Therefore, there was no elimination from the initial list.
In addition, a column was added to the questionnaire where the
respondents (industry and field experts) can add any other critical
factor important to the RL implementation point of view as per
their perception. By virtue of this, 3 more factors were added to the
initial literature identified 22 RL implementation CSFs. The three
added factors are given as e Extended producer responsibilities,
Benchmarking, and Globalization. Hence, a total of 25 CSFs linked
to implementation of RL for Indian manufacturing industries are
selected. These finalized 25 CSFs were then classified into five main
factors depending on their intended meaning and functional sim-
ilarities (see Table 2). These are given ase Regulatory factors (RF),
Global competitiveness factors (GCF), Economic factors (EF), HR
and organizational factors (HROF), and Strategic factors (SF).
5.1.2. Phase 2: determining the relative importance of the RL
implementation common success factors using AHP
The finalized common RL implementation CSFs is evaluated by
using the AHPmethod. It helps to recognize the relative importance
of each factor based on the ranks obtained from their numerical
priorities. For this, a hierarchal structure is constructed to analyze
the problem. It comprises of three levels: goal statement (Level-1),
main factors (Level-2), and sub factors (Level-3) as shown in Fig. 2.
Next, the pair-wise evaluation matrix for the main factors and
each sub factor is constructed by taking into consideration the
expert’s judgments. The importance rating of each expert is
collected based on scale as mentioned in Section 3.1. Notably, the
geometric meanmethod is among themost common usedmethods
in AHP to aggregate the individual ratings of the experts (Saaty,
2008). Thus, in this work, geometric mean of individual opinions
is computed for determining the ranks of the factors. The pair-wise
evaluation matrix for the main group factors is represented in
Table 3 below.
After following the steps mentioned in Section 3.1, Eigen values
and Eigen vectors are calculated, and is given as maximum Eigen
value ¼ 5.245; Consistency index (C.I.) ¼ 0.0612. The relative
weights attained and corresponding ranks for the main factor are
shown in Table 4.
The consistency ratio (C.R.) is calculated which comes out to be
0.055 (C.R. ¼ 0.0612/1.11). As evident, the consistency ratio (C.R.) is
well below the permissible limits (i.e. C.R.� 0.10); thus, the results
are considered to be acceptable. Likewise, the relative weights of all
the sub factors are calculated. Further, for obtaining the global
weights of all the sub factors, the relative weight of each main
factor is multiplied with its corresponding sub factor weights (see
Table 5).
5.1.3. Phase 3: determining interdependence among the RL
implementation common success factors using DEMATEL
In this phase, DEMATEL approach is used in order to determine
the interdependence between listed common CSFs relevant to RL
implementation from industries in Indian perspective. It assists to
evaluate the interrelationship between the CSFs in terms of a causal
effect map. For this, the same selected industry and field experts
were contacted and asked to rate the CSFs on the scale of 0e3
depending upon the influence of one factor over other factors. This
step is done to construct the pair-wise matrix of the main success
factors needed to construct the average matrix (A) and which is
formed by taking the average of the responses of the experts
(shown in Table 6).
In the next step, the normalized initial direct relation matrix (D)
is formed using Eq. (3) (see Table 7).
Following this, the total relation matrix (T) is constructed by
using Eq. (4), and is shown in Table 8.
According to Table 8, values in (r þ c) column (i.e. prominence),
demonstrates the total effect of each main factor over the entire
system; thus, Global competition factors (GCF) havemore influence
in comparison to other success factors. Likewise, values in (r e c)
column (i.e. relation), helps to divide the success factors into cause
and effect groups depending on their positive and negative values
attained respectively. Next to this, the threshold value has been
calculated, which facilitates to making this structure distinct. It is
obtained by taking the average of all the factors in total relation
matrix (T). It may help to reflect how one success factor influences
other factors, and assists to filter out some negligible effects in the
causal effect map. The causal-effect map of the main factors is
shown in Fig. 3.
Based upon the prominence values, the importance of main
factors in the implementation of RL practices in Indian
manufacturing industries is given as GCF-SF-HROF-RF and EF (see
Fig. 3). Further identified six main factors have been categorized
into cause-effect groups. The cause-effect diagram provides valu-
able insight to analyze the main factors in the implementation of
reverse logistics in Indian manufacturing industries. The main
factors – namely GCF, RF, and HROF – have been categorized into
the cause group, and the other two main factors (namely SF and EF)
are categorized into the effect group.
With respect to the differentmain factors, their position, and the
relative importance in the system, experts distinguish the main
factor which affects the decisions of the implementation of RL in
Indian manufacturing industries greatly, and thus, improvements
are made accordingly. Similarly, the DEMATEL calculations have
been performed for sub factors within their respective main factors
(Appendix A). The causal-effect map for the sub factors has also
been formed as shown in
Appendix B
.
Table 2
Common success factors related to RL implementation.
S. No. Success factor Description Source
Regulatory factors (RF)
1 Government norms and support (RF1) Government directives and support act as very important factors for
industries to put RL in practice
Kumar and Putnam, 2008; Hung Lau
and Wang, 2009; Subramoniam et al.,
2009; Ho et al., 2012; Rahman and
Subramanian, 2012
2 Preferential tax policies (RF2) Favorable taxation policies can motivate industries to implement RL
practices
Shaik and Abdul-Kader, 2012;
Abdulrahman et al., 2014
3 Environmental management
certifications (RF3)
Certification helps organizations to start and encourage environmentally
friendly activities in their business activities, and generates consciousness
among the employees
Knemeyer et al., 2002; Kumar and
Putnam, 2008; Hung Lau and Wang,
2009; Chio et al., 2012
4 Extended producer responsibility (RF4) Manufacturers should be responsible enough to manage products at their
end-of-life within India
Opinion received from the experts
5 Waste management practices (RF5) Wastemanagement practices is a big concern for industries to contribute for
society and environment
Knemeyer et al., 2002
Global competitiveness factors (GCF)
6 Competition (GCF1) Adopting RL practices can tremendously improve an organizational
competitive image in the market
Knemeyer et al., 2002; Subramoniam
et al., 2009; Chio et al., 2012; Giannetti
et al., 2013
7 Benchmarking (GCF2) Benchmarking the operations may significantly improve the RL adoption at
industrial context
Opinion received from the experts
8 Globalization (GCF3) Globalization proves to be a thrust for industries in RL adoption Opinion received from the experts
9 Green image building (GCF4) RL has been recognized as an important step for industries to enhance their
green image
Hsu and Hu, 2009; Hung Lau andWang,
2009; Subramoniam et al., 2009;
Mangla et al., 2014a
10 Sustainability (GCF5) Implementation of RL practices helps to bring sustainability in the business Kumar and Putnam, 2008; Lee et al.,
2010; Luthra et al., 2014b; Mangla et al.,
2015b; Nikolaou et al., 2013;
Subramanian et al., 2014
Economic factors (EF)
11 Reduced consumption of raw/virgin
material (EF1)
RL offers a huge scope of value recovery from used products, so helps in
reducing the raw/virgin material consumption
Seuring and Müller, 2008; Akdo�gan and
Coşkun, 2012
12 Decreased waste generation (EF2) Adopting RL operations like recycling, reuse, remanufacturing results in the
reduction of waste generation
Hung Lau and Wang, 2009; Pigosso
et al., 2010; Akdo�gan and Coşkun, 2012
13 Financial opportunities (EF3) Financial opportunities in terms of the second hand market can be obtained
through RL adoption
Rahman and Subramanian, 2012; Shaik
and Abdul-Kader, 2012
HR and organizational factors (HROF)
14 Stakeholders’ role and support (HROF1) Stakeholders such as investors, employees, management, etc. are
considered to be significant in making the decision to bring in the RL
perspective within the business culture
Gonz�alez-Benito and Gonz�alez-Benito,
2006; Rahman and Subramanian, 2012;
Shaik and Abdul-Kader, 2012
15 Experts involvement (HROF2) Experts involvement and knowledge can be valuable for the successful
implementation of RL
Ho et al., 2012; Abdulrahman et al.,
2014
16 Organization’s policy and mission
(HROF3)
Organization’s policy, mission and vision are very crucial for the acceptance
of the implementation of RL model
Dowlatshahi, 2005; Gonz�alez-Benito
and Gonz�alez-Benito, 2006
17 Top management commitment and
support (HROF4)
Top management commitment and support is very important for initiation
and implementation of RL
Dowlatshahi, 2005; Abdulrahman et al.,
2014
18 Employee expertise and involvement
(HROF5)
RL implementation needs employee involvement; otherwise, its effective
implementation could be very difficult
Ho et al., 2012; Abdulrahman et al.,
2014
19 Customer environmental awareness
(HROF6)
Customer environmental perception and knowledge is key to insist
industries to adopt RL practices
Tsoulfas and Pappis, 2008; Rahman and
Subramanian, 2012; Shaik and
Abdul-Kader, 2012; Abdulrahman
et al., 2014
Strategic factors (SF)
20 Integration and coordination (SF1) Integration and coordination among SC members may result in successful
implementation of RL
Rahman and Subramanian, 2012;
Lambert et al., 2011
21 Technology advancements (SF2) Adopting new processes and technology in RL initiatives will result in
increased efficiency
Lambert et al., 2011; Shaik and
Abdul-Kader, 2012
22 Management information system (SF3) It helps in bringing visibility within the system, thus, assisting on all levels of
RL implementation
Lambert et al., 2011
23 Infrastructure (SF4) Infrastructure plays a major role in RL adoption Lambert et al., 2011
24 Understanding best practices (SF5) Understanding RL implementation best practices will be crucial at industrial
perspective
Abdulrahman et al., 2014
25 Flexibility (SF6) Flexibility in operations, process and methods can help in adopting
successful RL practices in business
Knemeyer et al., 2002; Bai and Sarkis,
2013; Nagarajan et al., 2013
S.K. Mangla et al. / Journal of Cleaner Production 129 (2016) 608e621614
6. Results and discussions
From Table 4, the main factors to implement RL practices can be
arranged in relation to their relative importance or ranking as e
Global competitiveness factors (GCF), Regulatory factors (RF), HR
and organizational factors (HROF), Economic factors (EF), and
Strategic factors (SF). The relative importance or ranking of the sub
factors has also been determined. Next, considering the DEMATEL
results, the main factors GCF, RF, and HROF belong to the cause
group, and the main factors EF and SF belong to the effect group. It
clearly indicates that AHP based highly prioritized factors are the
causal factors in accordance with the DEMATEL results. In addition,
the importance order and causality mechanisms of the main factors
and sub factors in efficient implementation of RL have also been
Fig. 2. AHP based hierarchical structure for the research.
S.K. Mangla et al. / Journal of Cleaner Production 129 (2016) 608e621 615
recognized. Based on this, the present research offers several
management science implications as follows.
The finding of this research work reveals that the Global
competitiveness main factor (GCF) acquires the first rank, and
consequently, it obtains the highest priority among other main
factors (see Table 4). Regarding GCF, it is fitted to cause group factor
(see Fig. 3), due to the positive value of (r e c) score (i.e. 0.875). It
Table 3
Pair wise evaluation matrix for main factors.
Factors RF GCF EF HROF SF
RF 1 1 3 2 3
GCF 1 1 5 3 5
EF 1/3 1/5 1 1/3 3
HROF ½ 1/3 3 1 3
SF 1/3 1/5 1/3 1/3 1
has a considerable significant influence on the other main factors.
Therefore, it can be inferred that the GCF grouping is a crucial factor
for industries in order to reduce the waste generation and emis-
sions and to increase their environmental performances (Chio et al.,
2012). Hence, it needs a great managerial commitment. Within this
main factor, there are five sub factors, namely GCF1, GCF2, GCF3,
GCF4, GCF5. These can be arranged in accordance with their
Table 4
Ranking of main factors in RL implementation.
Main factors Relative weights Ranks
GCF 0.3794 1
RF 0.285 2
HROF 0.1761 3
EF 0.0977 4
SF 0.0618 5
Table 5
Ranking of sub-factors in RL implementation.
Main factors Relative weights Sub factors Relative weights Relative ranking Global weights Global ranking
Regulatory factors (RF) 0.285 RF1 0.497 1 0.142 2
RF2 0.071 5 0.020 17
RF3 0.182 2 0.052 6
RF4 0.158 3 0.045 8
RF5 0.093 4 0.027 14
Global competitiveness factors (GCF) 0.379 GCF1 0.150 3 0.057 5
GCF2 0.124 4 0.047 7
GCF3 0.448 1 0.170 1
GCF4 0.208 2 0.079 3
GCF5 0.071 5 0.027 13
Economic factors (EF) 0.098 EF1 0.637 1 0.062 4
EF2 0.258 2 0.025 15
EF3 0.105 3 0.010 22
HR and organizational factors (HROF) 0.176 HROF1 0.200 2 0.035 10
HROF2 0.073 6 0.013 21
HROF3 0.162 4 0.029 12
HROF4 0.243 1 0.043 9
HROF5 0.135 5 0.024 16
HROF6 0.186 3 0.033 11
Strategic factors (SF) 0.062 SF1 0.217 3 0.013 20
SF2 0.278 1 0.017 18
SF3 0.224 2 0.014 19
SF4 0.119 4 0.007 23
SF5 0.113 5 0.007 24
SF6 0.048 6 0.003 25
Table 6
Average direct relation matrix (A) (Main factors).
0.00 2.67 2.33 2.00 2.33
2.33 0.00 2.33 2.00 2.33
0.67 1.33 0.00 1.67
2.00
1.33 2.00 2.33 0.00 2.33
1.67 1.67 1.67 2.00
0.00
S.K. Mangla et al. / Journal of Cleaner Production 129 (2016) 608e621616
priority, given as: – Globalization (GCF3), > Green image building
(GCF4), > Competition (GCF1), >Benchmarking (GCF2), > Sustain-
ability (GCF5). Globalization (GCF3), is highly ranked in global
ranking column aswell (see Table 5), and demonstrated as themost
important success factor for Indian industries to put into practice
the RL initiative decisions. To have more insights into the results,
the success factors GCF1, GCF2, GCF3 are categorized as cause group
factors and GCF4, GCF5, are classified under effect group factors
based on the (re c) values (see Appendix A). This grouping suggests
that there is a critical need to regulate cause group factors, and
consequently, the effect group factors can surely acknowledge the
objectives of successful accomplishment of RL adoption across In-
dian industries.
Regulatory main factors (RF) obtain the second highest priority
in the list. The establishment of well-defined and environmental
supportive regulating directives and guidelines is very significant
for industries in adopting the RL initiatives at industrial standpoints
(Jindal and Sangwan, 2011; Sharma et al., 2011). Further, in relation
to this main factor, it finds its place among the cause group factor
(Fig. 3), indicating that it may act as a major contributing factor to
increase the success rate of RL adoption and implementation
Table 7
Normalized direct relation matrix (D) (Main factors).
0.00 0.29 0.26 0.22 0.26
0.26 0.00 0.26 0.22 0.26
0.07 0.15 0.00 0.18 0.22
0.15 0.22 0.26 0.00 0.26
0.18 0.18 0.18 0.22 0.00
among industries in Indian context. The five sub factors related to
this main factor are from RF1 to RF5. The preference or relative
importance order for these sub factors is given as Government
norms and support (RF1), > Environmental management certifi-
cations (RF3), > Extended producer responsibility (RF4), > Waste
management practices (RF5), > Preferential tax policies (RF2). In
addition to this, Government norms and support (RF1), is ranked
secondly as per global ranking and proves to be a key factor in
taking on RL aspects in business (Table 5). The success factors RF1,
RF3, and RF4 found their places in the cause group (see Appendix
A), which implies that they have significant influential impacts
over the other factors found in the effect group (namely RF2, RF5).
Moreover, all these factors play a significant role to the point of
Indian industries where RL initiatives are still in infancy. Clearly, a
systematic implementation of strategies and plans linked to these
factors’ completion may foster sustainable business developments
among Indian industries.
HR and organizational factors (HROF) occupies third rank in the
list. It finds its place among the cause group factor (Fig. 3), which
implies that it is relatively important among all other main factors.
Having proficient human resources, their expertise, and knowledge
along with organizational capabilities in terms of employee
involvement and their skills may resolve the difficulties relevant to
RL adoption and provide an opportunity to undertake the accep-
tance of RL’s contemporary activities like reuse, recycling, or
remanufacturing from business viewpoints (Sharma et al., 2011;
Abdulrahman et al., 2014). Stakeholders such as investors and
partners are pushing industries to accept RL related activities in
Table 8
Total relation and direct-indirect influence matrix (Main factors).
Factors RF GCF EF HROF SF R r þ c r e c
RF 0.91 1.33 1.44 1.30 1.48 6.45 10.88 2.030
GCF 1.08 1.06 1.40 1.26 1.44 6.24 11.61 0.875
EF 0.66 0.83 0.79 0.88 1.00 4.15 10.16 �1.860
HROF 0.91 1.12 1.27 0.97 1.31 5.57 11.04 0.100
SF 0.87 1.02 1.13 1.06 1.01 5.08 11.31 �1.145
C 4.42 5.37 6.01 5.47 6.24 Threshold value ¼ 1.10
RF
GCF
EF
HROF
SF
–
2.50
-2.00
–
1.50
–
1.00
–
0.50
0.00
0.50
1.00
1.50
2.00
2.50
10.00 10.20 10.40 10.60 10.80 11.00 11.20 11.40 11.60 11.80
Fig. 3. Causal effect map (Main factors).
S.K. Mangla et al. / Journal of Cleaner Production 129 (2016) 608e621 617
supply chains (Chio et al., 2012). The relative ranking of six sub
factors related to this main factor are – Top management commit-
ment and support (HROF4), > Stakeholders’ role and support
(HROF1), > Customer environmental awareness (HROF6), > Orga-
nization’s policy and mission (HROF3), > expertise and involve-
ment (HROF5), > Experts involvement (HROF2). Moreover, success
factors HROF1- HROF4- HROF6 occurs in the cause group, which
has a significant influence over the factors HROF2- HROF3- HROF5,
which are in the effect group (see Appendix A).
Economic factors (EF) acquired the fourth importance level and
play a crucial role in adopting effective green concepts in an in-
dustrial context. Further, considering the causal effect map, it be-
longs to effect group. It suggests that the various associated
activities in implementation of RL practices have a tendency to in-
fluence finance flow and resources (Chio et al., 2012). Under this
consideration, it may be difficult for Indian industries to initiate and
adopt RL practices in the initial stage of business, but at the later
stage, it will offer a huge financial opportunity in terms of the
reduction of raw material consumption, of significant cuts in waste
generation, and of financial opportunities for used products in the
new or secondary market, etc. Thus, the Indian industries may be
financially benefitted and contribute more to their country’s econ-
omy. This main factor contains three sub factors and the priority
order for them is listed as – Reduced consumption of raw/virgin
material (EF1), > Decreased waste generation (EF2), > Financial
opportunities (EF3). With regard to these three sub factors, EF1 and
EF2 fall under the cause group, whereas EF3 comes under the effect
group (see Appendix B). Thus, managers are suggested to consid-
erably resolve the causal matters helpful in performance improve-
ments in aspects of RL adoption and implementation.
Strategic factors (SF) hold the last place in priority list. It would
be valuable for industries if they have strategic plans and visions
associated with adoption and implementation of RL practices in
their businesses. Understanding and analyzing Strategic factors is
important to develop strengths into competitive advantages and to
improve certain weaknesses related to technology, infrastructure,
supply chain coordination and integration, and flexibility; such
improvements will result in increased environmental, economical,
and social performances of the Indian industries. There are six sub
factors in strategic factors, and the preference order for them is
highlighted as Technology advancements (SF2), > Management
information system (SF3), > Integration and coordination (SF1), >
Infrastructure (SF4), > Understanding best practices (SF5), >
Operational flexibility (SF6). The success factors SF1, SF2, SF3 and
SF4 found their places in the cause group (see Appendix B), which
implies that they have significant influential impacts over the other
factors occurring in the effect group, namely SF5 and SF6.
AHP results of main factors to implement RL practices in relation
to their priority are given, in order, as GCF-RF-HROF-EF and SF. The
DEMATTEL results of main factors to implement RL practices ac-
cording to the prominence values are given as GCF-SF-HROF-RF and
EF. From this, we can say that AHP and the prominence results are
almost consistent. The combined results will help managers not
only to prioritize the RL implementation success factors, but also to
obtain their causal interactive relationships. This understanding
may result in performance improvements in their industries, and it
may help to ensure sustainable business developments.
6.1. Implications of research
The AHP and DEMATEL based model proposed in this work will
enable Indian manufacturing company managers to understand
different CSFs to implement RL practices in India. It would be
crucial to know the relative importance and causal interactions of
the various CSFs and the techniques for implementing RL adoption
from industrial standpoints. This research work will certainly pre-
pare them for the more efficient and effective implementation of RL
practices in India. The findings obtained in this work will help
managers and practitioners to improve the sustainability of the
organizations in implementing RL practices of the industries. CSFs
with higher priority demonstrate more of a tactical or operational
orientation; on the other hand, those categorized as cause and ef-
fect groups are more geared towards performance and result
orientation. However, strategic results/desired effects can be ach-
ieved by continuously improving cause group factors. This work
may help RL practitioners/managers to manage these identified
CSFs according to their AHP ranking priority and DEMATEL based
prominence to achieve sustainability in the business.
From the results, Global competitiveness and Regulatory factors
are highly prioritized factors and belong to cause group factors as
well. In that way, the companies should contact and lobby the
government and regulating authorities to express their concerns of
the issue of RL implementation and its benefits in business. Gov-
ernment and various regulating agencies support is much needed
to adopt green and product recovery activities (Madaan and
Mangla, 2015). To help companies, a well-designed and system-
atic reverse logistics network is recommended to overcome the
complexities in returning and recycling collected products for their
reuse (Mangla et al., 2015a). In this sense, some motivational pro-
grams and seminars/campaigns may be conducted to educate
customers regarding products’ reuse, recyclability, etc. In addition,
some easily accessible collection stations may be opened to
enhance the return and recovery of used products. Strict penalty
and rewards systems may improve the recovery mechanism.
S.K. Mangla et al. / Journal of Cleaner Production 129 (2016) 608e621618
To end with, the proposed network model may provide some
valuable guidelines to the supply chain analysts and management
science professionals to develop their plan of action in terms of
design of the short-term and the long-term, flexible decision stra-
tegies for implementing RL on various business levels.
Factors RF1 RF2 RF3 RF4 RF5 r r þ c r e c
RF1 0.53 1.03 0.82 0.95 0.83 4.17 6.76 1.58
RF2 0.45 0.54 0.55 0.65 0.54 2.73 6.77 �1.31
RF3 0.57 0.85 0.50 0.69 0.65 3.25 6.38 0.11
RF4 0.61 0.91 0.75 0.61 0.66 3.54 7.05 0.02
RF5 0.44 0.71 0.51 0.62 0.40 2.69 5.77 �0.39
c 2.59 4.04 3.14 3.52 3.08 Threshold value ¼ 0.65
Factors GCF1 GCF2 GCF3 GCF4 GCF5 R r þ c r e c
GCF1 2.60 2.57 2.71 2.74 2.74 13.36 26.40 0.33
GCF2 2.76 2.34 2.69 2.66 2.67 13.13 25.07 1.18
GCF3 2.85 2.59 2.54 2.77 2.72 13.46 26.13 0.80
GCF4 2.41 2.22 2.34 2.18 2.37 11.53 24.24 �1.19
GCF5 2.42 2.23 2.38 2.36 2.19 11.58 24.27 �1.12
c 13.03 11.95 12.67 12.71 12.70 Threshold value ¼ 2.52
Factors EF1 EF2 EF3 r r þ c r e c
EF1 1.03 1.25 1.66 3.93 7.02 0.84
EF2 1.15 0.80 1.45 3.40 6.19 0.60
EF3 0.92 0.76 0.84 2.52 6.47 �1.44
c 3.09 2.80 3.95 Threshold value ¼ 1.09
7. Conclusions, limitations, and scope for future work
Industries are constantly seeking ways to curb their negative
impacts on the environment to ensure business sustainability. The
implementation of reverse logistics (RL) practices has received
major attention among developed countries; however, it needs a
more in-depth examination in order to most effectively benefit a
developing country such as India. Nevertheless, there are several
critical factors linked to the implementation of RL practices. Hence,
it is necessary to analyze these factors to increase the RL imple-
mentation success rate. Therefore, from industrial viewpoints, the
various CSFs related to the implementation of RL practices are
evaluated in this study.
In this research work, an attempt has been made to evaluate the
CSFs in RL implementation, by framing both short-term and long-
term flexible decision strategies with AHP and DEMATEL
methods. The AHP method helps to rank the factors (i.e. deter-
mining of the priority) according to their relative importance. On
the other hand, DEMATEL helps to establish interactive and or
causal relationships between the factors, and classifies them into
cause and effect groups.
The proposed AHP and DEMATEL based model is extended to
the manufacturing industries in the Indian context. RL has been
either already initiated or is in an early stage of adoption in the
industries surveyed. A total of 25 common RL implementation CSFs
have been selected, based on literature resources and the industry
and field expert judgments.
The findings of this work shows that the Global competitiveness
main factor (GCF) is highly prioritized, and thus, needs to be
focused greatly in order to increase the effectiveness and efficiency
of RL adoption in business. The relative priority or importance order
of the remaining main factors through AHP analysis is given as
Regulatory factors (RF) – HR and organizational factors (HROF),
-Economic factors (EF) – Strategic factors (SF). The findings also
indicate that Global competitiveness; Regulatory; HR and organi-
zational main factors are classified under cause group, while Eco-
nomic and Strategic main factors belong to effect group. The cause
group factors are vital due to their direct impact on the overall
system; therefore, it would be significant to focus on these group
factors to expedite the overall performance. On the contrary, effect
group factors tend to be easily affected by other factors (i.e. from
the factors of cause group), and thus, make a significant contribu-
tion towards achieving the desired goals (Mangla et al., 2015b. The
results in terms of relative priorities and of interactive relationships
for the sub factors are also derived.
This work has its own limitations, which can be taken as op-
portunities for future research. The work carried out in this
research is based on the methods of AHP and DEMATEL, and
Factors HROF1 HROF2 HROF3 HROF4
HROF1 0.70 1.07 1.19 1.09
HROF2 0.53 0.60 0.79 0.73
HROF3 0.75 0.97 0.89 0.98
HROF4 0.76 0.98 1.09 0.82
HROF5 0.64 0.90 0.96 0.86
HROF6 0.64 0.77 0.85 0.79
c 4.02 5.29 5.77 5.26
identifies 25 CSFs in the context of implementation of RL in Indian
context. Some other CSFs have not been revealed and classified. For
future studies, the hierarchical intertwined interactions and feed-
back paths among recognized RL implementation CSFs can be
analyzed by using other multi-criteria analysis methods like the
Technique for Order of Preference by Similarity to Ideal Solution
(TOPSIS), Analytic Network Process (ANP)methods, and other fuzzy
or grey related MCDM approaches (Govindan et al., 2015a, 2016;
Govindan and Chaudhuri, 2016; Xia et al., 2015). The proposed
model may be applied to other sectors of industry, for example,
service or construction that seeks to analyze the RL implementation
performance at various business levels. It should be noted that the
expert’s opinion may vary with industry type and its priorities.
Appendix A
DEMATEL Calculations for Sub-factors within their respective
Main factors.
Total relation and direct-indirect influence matrix (Regulatory
factors).
Total relation and direct-indirect influence matrix (Global
competitiveness factors).
Total relation and direct-indirect influence matrix (Economic
factors).
Total relation and direct-indirect influence matrix (HR and
organizational factors).
HROF5 HROF6 r r þ c r-c
0.97 0.90 5.92 9.94 1.89
0.67 0.55 3.87 9.16 �1.41
0.90 0.76 5.25 11.02 �0.51
0.95 0.71 5.30 10.56 0.03
0.66 0.65 4.68 9.49 �0.14
0.67 0.51 4.22 8.30 0.14
4.81 4.08 Threshold value ¼ 0.81
S.K. Mangla et al. / Journal of Cleaner Production 129 (2016) 608e621 619
Total relation and direct-indirect influence matrix (Strategic
factors).
Factors SF1 SF2 SF3 SF4 SF5 SF6 r r þ c r-c
SF1 0.84 1.01 1.09 0.98 1.12 1.07 6.11 11.84 0.37
SF2 1.09 0.98 1.23 1.09 1.27 1.26 6.91 12.85 0.97
SF3 1.03 1.04 0.95 1.02 1.20 1.14 6.38 12.62 0.15
SF4 0.98 0.99 1.00 0.83 1.16 1.06 6.03 11.79 0.26
SF5 0.82 0.89 0.90 0.87 0.85 0.95 5.27 12.00 �1.46
SF6 0.98 1.03 1.07 0.98 1.12 0.94 6.12 12.53 �0.29
c 5.74 5.94 6.24 5.77 6.73 6.41 Threshold value ¼ 1.02
Appendix B
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1. Introduction
2. Relevant literature
2.1. Industrial RL implementation in India
2.2. RL implementation factors
2.3. Research gaps
3. Research methods
3.1. AHP method
3.2. DEMATEL method
4. Proposed research framework
5. An application example of the proposed model to manufacturing industries in India
5.1. Data collection
5.1.1. Phase 1: identification and selection of the common RL implementation critical success factors
5.1.2. Phase 2: determining the relative importance of the RL implementation common success factors using AHP
5.1.3. Phase 3: determining interdependence among the RL implementation common success factors using DEMATEL
6. Results and discussions
6.1. Implications of research
7. Conclusions, limitations, and scope for future work
Appendix A
Appendix B
References
Contents lists available at ScienceDirect
Resources, Conservation & Recycling
journal homepage: www.elsevier.com/locate/resconrec
Reverse logistics and closed-loop supply chain of Waste Electrical an
d
Electronic Equipment (WEEE)/E-waste: A comprehensive literature review
Md
T
asbirul Islam⁎, Nazmul Huda⁎
School of Engineering, Macquarie University, NSW 2109, Australia
A R T I C L E I N F O
Keywords:
Reverse logistics (RL
)
Closed-loop supply chain (CLSC)
Waste Electrical and Electronic Equipment
(WEEE)
E-waste management
Literature review
Sustainabilit
y
Circular economy
A B S T R A C T
Reverse logistics (RL) and the closed-loop supply chain (CLSC) are integral parts of the holistic waste man-
agement process. One of the important end-of-life (EOL) products considered in the RL/CLSC is Waste Electrica
l
and Electronic Equipment (WEEE)/E-waste. Numerous research papers were published in the RL and CLS
C
disciplines focusing WEEE separately. However, there is no single review article found on the product-specific
issues. To bridge this gap, a total of 1
57
papers published between 1999 and May 2017 were selected, cate-
gorized, analyzed using content analysis method. The method involves four steps: material collection, de-
scriptive analysis, category selection and material evaluation. For the systematic literature review, the step
s
were followed and four main types of research in the field of RL and CLSC of E-waste, namely designing and
planning of reverse distribution, decision making and performance evaluation, conceptual framework, and
qualitative studies were identified and reviewed. Research gaps in literature were diagnosed to suggest future
research opportunities. The review first of its kind that may provide a useful reference for academicians, re-
searchers and industry practitioners for a better understanding of WEEE focused RL/CLSC activities and re-
search.
1. Introduction
Due to growing environmental regulations, potential recovery of
valuable material resources for the secondary market, and sustainable
business practices, over the last twenty years, the concept of reverse
logistics (RL) has been accepted and widely practiced in manufacturing
industries all over the world. The definition of RL according to Stock
(1992) refers to “… the term often used for the role of logistics in re-
cycling, waste disposal and management of hazardous materials; a
broader perspective includes all issues relating to logistics activities
carried out in source reduction, recycling, substitution, reuse of mate-
rials and disposal”. This definition links directly RL activities in a waste
management scenario that provides a more holistic approach to re-
source conservation and recycling of end-of-life (EOL) products. As
waste generation by various industries is increasing at a skyrocketing
pace, many governments across the globe compel the producer/man-
ufacturer to implement the extended producer responsibility (EPR)
principle. According to the Organisation for Economic Co-operation
and Development (OECD), ‘’EPR is a policy approach under which
producers are given a significant responsibility – financial and/or
physical – for the treatment or disposal of post-consumer products’’
(OECD, 2017). With this instrument, manufacturers now have to
develop a sustainable reverse supply chain (RSC) besides the conven-
tional forward logistics (FL) system. According to Stevens (1989), a
forward supply chain (FSC) is’ ’a system consisting of material sup-
pliers, production facilities, distribution services, and customers who
are all linked together via the downstream feed-forward flow of mate-
rials (deliveries) and the upstream feedback flow of information (or-
ders)’’. On the other hand, when the FSC and RSC systems are con-
sidered in an integrated manner, the concept of the closed-loop supply
chain (CLSC) evolved. It considers efficient product return management
and conducts value recovery activities so that secondary materials can
be used as input for ‘’new’’ customer product. Rather considering legal,
social responsibilities, or even operational and technical details of the
FSC and RSC, the CLSC focuses explicitly on business perspectives of the
supply chains. According to Guide and Van Wassenhove (2009), ‘’CLSC
management is the design, control, and operation of a system to max-
imize value creation over the entire life cycle of a product with dynamic
recovery of value from different types and volumes of returns over
time’’. From the sustainability viewpoint in all three dimensions – so-
cial, economic and environmental – in conjunction with the circular
economy, RL/CLSC is an emerging area of research that attracts both
academic and industry practitioners. According to Geissdoerfer et al.
(2017), ‘’ A circular economy is a regenerative system in which resour
ce
https://doi.org/10.1016/j.resconrec.2018.05.026
Received 20 November 2017; Received in revised form 21 March 2018; Accepted 24 May 2018
⁎ Corresponding authors.
E-mail addresses: md-tasbirul.islam@hdr.mq.edu.au (M.T. Islam), nazmul.huda@mq.edu.au (N. Huda).
Resources, Conservation & Recycling 137 (2018) 48–
75
Available online 01 June 2018
0921-34
49
/ © 2018 Elsevier B.V. All rights reserved.
T
http://www.sciencedirect.com/science/journal/09213449
https://www.elsevier.com/locate/resconrec
https://doi.org/10.1016/j.resconrec.2018.05.026
https://doi.org/10.1016/j.resconrec.2018.05.026
mailto:md-tasbirul.islam@hdr.mq.edu.au
mailto:nazmul.huda@mq.edu.au
https://doi.org/10.1016/j.resconrec.2018.05.026
http://crossmark.crossref.org/dialog/?doi=10.1016/j.resconrec.2018.05.026&domain=pdf
input and waste, emission, and energy leakage are minimized by
slowing, closing, and narrowing the material and energy loops. This can
be achieved through long-lasting design, maintenance, repair, reuse,
remanufacturing, refurbishing, and recycling’’ and sustainability is de-
fined as the balanced integration of economic performance, social in-
clusiveness, and environmental resilience, to the benefit of current and
future generations. Based on the above definition of RL/CLSC, the
generic diagram can be illustrated as in Fig. 1.
Among the various EOL products identified in RL and CLSC re-
search, E-waste is found as a significant one. The question is how dif-
ferent is the RL and CLSC systems from a generic form when WEEE is
considered. A lot of previously published papers have not clearly spe-
cified the difference which is a drawback of some of the earlier studies.
E-waste possesses some special characteristics and features that
make its RL and CLSC systems unique from general RL and CLSC sys-
tems. WEEE is one of the fastest-growing streams at present due to a
shorter product lifecycle (PLC) and rapidly changing customer attitudes
towards disposing of them (Islam et al., 2016; Nnorom and Osibanjo,
2008). According to “Global E-waste Monitor Report 2017” published
by United Nations University (UNU), in the year 2016, 44.7 million
tonnes (Mt) of e-waste was generated in the world and only 20% was
recycled through proper channels (Baldé et al., 2017). This generation
volume is significant compared to other EOL items. For example, every
year, only 8 to 9 million tonnes of end-of-life vehicle (ELV) is generated
(Eurostat, 2018) which is 5 times less than the WEEE generation.
Globally, to tackle the emerging waste stream under comprehensive
WEEE management policies, several countries implemented regulations
towards minimizing the negative environmental impact and prioritizing
valuable resource recovery. To bind all the stakeholders legally in
managing E-waste, European Union (EU) is at the forefront. On 13th
August, 2012, the EU WEEE DIRECTIVE 2012/19/EU came into force
by which member countries in the EU are obliged to follow the recovery
and recycling target implementing EPR policy. According to the Di-
rective, E-waste is divided into ten different categories (until 15 August
2018) (Directive, 2012). Table 1 shows WEEE product categories with
target recovery and recycling rate.
In principle, complex processes of RL and CLSC start with the dis-
posal of EOL electrical and electronic equipment (EEE). However, in
WEEE’s return management, multiple factors along with a higher de-
gree of uncertainties such as quality, quantity and time are involved
(Chen and He, 2010). First, the huge amount of generation is coming
from three distinct sources: households, government and institutions,
and businesses (Li et al., 2006). Households dispose of a range of
equipment starting from large household equipment like refrigerators,
washing machines to small consumer electronics, mobile phone;
whereas information and communication technology (ICT) equipment
is largely discarded by organizations. On the other hand, for the same
equipment, average lifespan varies significantly. Second, the method of
E-waste collection from the sources varies substantially in terms of
collection points (e.g. municipality collection points, retailers, product
manufacturers, EEE repairs, third party recycling service provider
companies etc.) involved in a EOL-WEEE recovery process (Iacovidou
et al., 2017). For instance, households can discard their E-waste in a
number of ways: 1) at the municipal collection points, 2) leave it to
their kerbside, 3) drop it off at special events, 4) return back to re-
tailers/ point of purchase, and 5) return back to manufacturers/re-
cyclers appointed by manufacturer. For business and other organiza-
tions, leasing became increasingly popular and in this process, leasing
companies are responsible for EOL dispositions which further involve
RL service providers for transportation, local recyclers and small busi-
nesses that deal with reuse of EEE items. Disposing E-waste to perma-
nent drop-off locations is also practiced by institutions. Third, collected
quantities then transported to treatment facilities where WEEE goes
through testing, inspection, and sorting and dissembled according to
specific product categories before transferred for processing. An opti-
mized network design plays a crucial role in efficient and successful RL
processes. For example, in Switzerland, three take-back systems,
SWICO, SENS, Swiss Lighting Recycling Foundation (SLRS) together
established
60
00 collection points by which 95% of the E-waste is
collected and recycled (SWICO, 2017). Fourth, depending upon the
material content and value proposition (i.e. quality of waste), five dif-
ferent disposition alternatives (e.g. reuse, repair, remanufacture and
Fig. 1. Generic diagram of CLSC including forward and reverse flow, adapted from Chopra and Meindl (2007).
M.T. Islam, N. Huda Resources, Conservation & Recycling 137 (2018) 48–75
49
recycling) exist which is often problematic selecting the best available
alternative. Top management of computer hardware industries strug-
gles to evaluate the ultimate fate of EOL-computers (Ravi et al., 2005a,
2005b). Large quantities of E-waste is also disposed of in landfills.
Compared to other EOL products, E-waste has a complex material
structure containing both environmentally hazardous substances (i.e.
mercury, cadmium, lead, chromium, poly/brominated flame re-
tardants, ozone-depleting chemicals such as CFC etc.) and valuable
critical raw materials (CRM), such as copper and gold (Kumar et al.,
2017). Physical and mechanical processing supply secondary materials
recovered from WEEE to the EEE and other industries (Işıldar et al.,
2017). Thus, RL and CLSC of E-waste are very unique in terms of as-
sociated collection and EOL options involved. Fig. 2 shows the CLSC
diagram of E-waste.
The number of international peer-reviewed articles published on
RL/CLSC issues focusing on WEEE is increasing considerably. However,
no single review has yet been conducted to summarize all the relevant
articles with a product-specific focus. To the best of the authors’
knowledge, this is the first attempt at reviewing RL/CLSC articles fo-
cused on WEEE. As the body of literature is growing considerably, this
review aims to provide a complete picture of the field, by categorizing
the content of the literature and reviewing it into four distinct research
types: designing and planning of reverse distribution, decision making
and performance evaluation, conceptual framework and qualitative
studies. After reviewing the articles, research gaps were identified and a
number of future research directions have been identified so that future
researchers can work in line with the research gaps in the field. The
paper is organized as follows: Section 2 discusses the research metho-
dology of the study. Section 3 provides a detailed analysis of the arti-
cles. Research gaps are analyzed and future research directions are
addressed in Section 4, and Section 5 reaches a conclusion.
2. Research methodology
A literature review plays a critical role in scholarship as well as it
helps to explore and structure thoroughly a particular research area
(Easterby-Smith et al., 2012; Vom Brocke et al., 2009). With a valid
literature review, knowledge on the concerning area can be further
advanced by identifying key conceptual contents that works as a path to
new theory development and new scope of investigation (Machi and
McEvoy, 2016; Meredith, 1993). For a systematic literature review, this
study implemented four steps processes as prescribed by Mayring
(2001) under the qualitative content analysis method: material collec-
tion, descriptive analysis, category selection and finally, material eva-
luation. Fig. 3 shows four steps process model for content analysis
method. An extensive description of the method can be found in
Mayring’s recent publication (Mayring, 2014). The application of the
method for reviewing supply chain management literature can be found
in papers by Seuring and Gold (2012) and Seuring et al. (2005). Several
of the previous review articles (non EOL product focused) in the RL/
CLSC field (e.g. Seuring and Gold (2012), Gold et al. (2010), Govindan
et al. (2015), Agrawal et al. (2015)) have implemented this metho-
dology.
2.1. Material collection
In this literature review material collection and unit of analysis is
the first step. A single journal article/conference paper/book chapter
was defined as unit of analysis. In this study, a two-phase process was
initiated. In the first phase, keywords such as ‘’reverse logistics’’ and
‘’closed-loop supply chain’’ along with ‘’WEEE or E-waste’’ were used in
title, abstract and keywords to carry article search. This keywords were
used in the Scopus (www.scopus.com), and Web of Science (WoS) da-
tabases with an option that search only the papers those written in
English. After analyzing title and abstract, further search of literature
were inductively connected with the categorization of RL/CLSC i.e.Ta
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M.T. Islam, N. Huda Resources, Conservation & Recycling 137 (2018) 48–75
50
http://www.scopus.com
designing and planning of reverse distribution, decision making and
performance evaluation, conceptual framework and qualitative studies
(e.g. survey, interview etc.). In this case, some of the essential key
words were utilized, for instance, ‘’open-loop network design’’, closed-
loop network design’’, ‘’third-party reverse logistics provider’’, ‘’vehicle
routing’’, ‘’product recovery’’, ‘’organization and business perspective’’,
product return’’ and ‘’reverse logistics processes’’; along with the
mandatory search term ‘’reverse logistics’’, ‘’closed-loop supply chain’’
and ‘’WEEE/E-waste’’. Besides, those studies that have considered waste
battery and printer cartridges were also included in this study. Total
2
58
papers were retrieved and all collected papers were taken into
consideration for a fast check of relevancy and final content for the
literature review. Articles those found most relevant to the above
mention categorization were considered for this study. Finally, total
157 papers were selected, reviewed and analyzed in detail. Besides,
journal articles, in the final collection 26 conference papers and 3 book
chapters are included. The selection of the papers for this state-of-the-
art review seems sufficient because of concentration (e.g. RL/CLSC of
WEEE) on particular issues.
2.2. Descriptive analysis
To understand the broad range of concepts, motivation, modeling
approach to a specific problem, papers were arranged from more than
sixty journals. Fig. 2 shows the articles published by numerous outlets.
From Fig. 4, it is found that most of the papers were published in re-
nowned journals such as International Journal of Production Research,
Resources, Conservation and Recycling, Waste Management, International
Journal of Production Economics and Production and Operations Manage-
ment.
Annual distribution of the number of papers published from the year
1999 to 2017 in both RL and CLSC is shown in Fig. 5. Most of the papers
were selected from recent publications. 20 papers out of 157 papers
were published before the year 2006, whereas rest of the articles (135)
were selected from the year 2006 and afterward. The highest number of
papers were published in the year 2010. From this trend, it is clear that
the number of published papers is growing considerably in the last few
years due to the increasing interest of WEEE centric RL/CLSC analysis.
Fig. 2. Closed-loop supply chain of E-waste.
Fig. 3. Summary of the steps involved in qualitative content analysis citied in Seuring et al. (2005).
M.T. Islam, N. Huda Resources, Conservation & Recycling 137 (2018) 48–75
51
2.3. Category selection
The main categorization of the content of this study and research
framework is presented in the Fig. 6. As mentioned in the material
collection section, the literature is classified into 4 major research
types/categories. These four categories are (1) Designing and planning
of reverse distribution (DPRD); (2) Decision making and performance
evaluation; (3) conceptual framework based studies; (4) Qualitative
studies. Distribution of research articles for 4 different categories is
shown in Fig. 7. DPRD has the highest percentage (
55
%) of publications
whereas other categories possess less percentage which depicts the
necessity for future exploration of these areas under the broad RL/CLSC
of WEEE research field.
Open-loop network design (OLND), closed-loop network design
(CLND), third-party reverse logistics provider (3PRLP) selection and
vehicle routing (VR) related papers fall broadly under the category of
DPRD. The highest number of papers (51 papers) were published in the
OLND sub-category. Fig. 8 shows the trend of published papers in the
DPRD research area. The papers in the main field of research were
further sub-categorized into specific issues (that evolved during
Fig. 4. Number of papers published in journals, conferences and book chapters.
Fig. 5. Annual distribution of the published papers (157 papers: 1999-2017).
M.T. Islam, N. Huda Resources, Conservation & Recycling 137 (2018) 48–75
52
material collection and category selection stages) which are shown in
Fig. 9.
2.4. Material evaluation
The last and final stage of the content analysis process is the ma-
terial evaluation. Rigor in validity is attained by validation test per-
formed by two researchers using the deductive and inductive ap-
proaches simultaneously. Reliability of the content was measured by
both intra-rater reliability and inter-rater reliability. After material
collection, all necessary information extracted from the selected articles
were input in spreadsheet software conducted by the researchers by
which repetition error by the researchers was minimized. With the
same keywords used to search the articles were utilized in the google
scholar database, and two researchers found the similar results in
identifying correct articles and coding their content in a spreadsheet
application. With this reliability was established. Through searching
and cross-checking the publications independently, sufficiency, as well
as the validity of the correct content of the collected paper, was ac-
cepted.
3. In-depth analyses of the literature
3.1. Analyzing papers on DPRD
The primary concern of DPRD is to design collection and
Fig. 6. Categorization and research framework of the studies.
Fig. 7. Distribution of research articles for different categories.
M.T. Islam, N. Huda Resources, Conservation & Recycling 137 (2018) 48–75
53
transportation network and vehicle route for EOL product acquisition.
This specific task also signifies the planning, functions and logistics
capability of stakeholders/actors engaged in the networks and how FSC
and RSC could be integrated from CLSC perspective. According to
Fleischmann et al. (1997), the performance of a reverse distribution
channel mainly depends on three major issues: 1) actors involved in the
reverse distribution channel, 2) locations and functions carried out in
the channel and 3) relation between FSC and RSC. As mentioned ear-
lier, a considerable number of papers have been published with this
issue in four major sub-categories which are 1) Open-loop network
Fig. 8. Number of articles published on DPRD.
Fig. 9. Issues of the main research fields of RL/CLSC of WEEE.
M.T. Islam, N. Huda Resources, Conservation & Recycling 137 (2018) 48–75
54
T
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in
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te
ch
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iq
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e
fo
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m
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lt
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en
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al
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re
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re
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IN
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P
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ci
al
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d
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co
n
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ic
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u
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om
es
et
al
.
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0
1
1
)
W
E
E
E
re
co
ve
ry
n
et
w
or
k
M
in
im
iz
e
to
ta
l
n
et
w
or
k
co
s
t
–
M
IP
an
d
G
en
er
al
A
lg
eb
ra
ic
M
od
el
in
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Sy
st
em
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S)
C
P
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co
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ic
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or
tu
ga
l
(c
on
ti
nu
e
d
on
ne
xt
pa
ge
)
M.T. Islam, N. Huda Resources, Conservation & Recycling 137 (2018) 48–75
55
T
ab
le
2
(c
on
ti
nu
ed
)
R
ef
er
en
ce
M
od
el
fo
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s
O
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s
U
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/
co
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n
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ed
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th
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ti
li
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d
m
od
el
in
g
ap
p
ro
ac
h
So
lv
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by
Su
st
ai
n
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il
it
y
d
im
en
si
on
co
n
si
d
er
ed
C
ou
n
tr
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T
u
zk
ay
a
et
al
.
(2
0
1
1
)
M
et
h
od
ol
og
ic
al
d
ev
el
op
m
en
t
of
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L
n
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ig
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A
lt
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at
iv
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lo
ca
ti
on
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of
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tr
al
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ce
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te
r
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d
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im
iz
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io
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V
ar
ia
ti
on
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ex
p
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te
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of
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In
te
gr
at
ed
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n
al
yt
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n
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ss
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)
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P
SI
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ic
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u
rk
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ia
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n
g
et
al
.
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0
1
0
)
D
ec
is
io
n
su
p
p
or
t
fo
r
R
L
n
et
w
or
k
d
es
ig
n
–
–
LP
LI
N
G
O
,
Fl
ex
si
m
E
co
n
om
ic
C
h
in
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h
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al
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0
1
0
)
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em
an
u
fa
ct
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ri
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g
–
ba
se
d
R
L
n
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w
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k
d
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ig
n
O
p
ti
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al
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rs
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is
as
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m
bl
y
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rs
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re
tu
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n
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id
en
ti
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ca
ti
on
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ti
m
u
m
sh
ip
m
en
t
p
at
h
.
–
M
at
h
em
at
ic
al
m
od
el
in
g
G
A
E
co
n
om
ic
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h
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a
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h
oi
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h
en
ak
is
(2
0
1
0
)
O
p
er
at
io
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al
m
od
el
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g
of
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h
ot
ov
ol
ta
ic
(P
V
)
re
cy
cl
in
g
n
et
w
or
k
–
–
M
at
h
em
at
ic
al
m
od
el
in
g
–
E
co
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ic
U
SA
G
am
be
ri
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i
et
al
.
(2
0
1
0
)
R
es
ou
rc
e
al
lo
ca
ti
on
an
d
en
vi
ro
n
m
en
ta
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im
p
ac
ts
of
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E
tr
an
sp
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ti
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ay
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d
m
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m
n
u
m
be
r
of
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qu
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ed
ve
h
ic
le
fo
r
op
er
at
io
n
–
Li
fe
cy
cl
e
as
se
ss
m
en
t
(L
C
A
)
m
od
el
in
g
an
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6
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l8
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n
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ic
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en
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ic
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It
al
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A
ch
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(2
0
1
0
)
O
p
ti
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iz
at
io
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of
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ll
ec
ti
on
p
oi
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ts
an
d
re
cy
cl
in
g
fa
ci
li
ti
es
M
in
im
iz
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of
co
st
–
M
IL
P
C
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LE
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co
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re
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K
aw
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ol
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a
(2
0
1
0
)
R
ec
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ra
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ge
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en
t
(R
N
A
)
–
R
ec
ov
er
y
ti
m
e
an
d
re
tu
rn
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an
ti
ty
G
ra
p
h
th
eo
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y
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ag
en
t
te
ch
n
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og
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–
E
co
n
om
ic
P
ol
an
d
H
an
qi
n
g
an
d
R
u
(2
0
0
9
)
R
ec
ov
er
y
st
at
io
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lo
ca
ti
on
s
fo
r
T
h
ir
d
-p
ar
ty
R
L
se
rv
ic
e
p
ro
vi
d
er
–
–
M
at
h
em
at
ic
al
m
od
el
in
g
E
co
n
om
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C
h
in
a
D
en
g
an
d
Sh
ao
(2
0
0
9
)
M
u
lt
i-
p
ro
d
u
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fl
ow
-b
as
ed
re
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cl
in
g
n
et
w
or
k
M
in
im
iz
at
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of
to
ta
l
co
st
–
A
n
al
yt
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al
m
od
el
in
g
M
A
T
LA
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E
co
n
om
ic
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ai
w
an
G
u
er
ra
et
al
.
(2
0
0
9
)
V
eh
ic
le
an
al
ys
is
–
n
u
m
be
r
of
ve
h
ic
le
s
to
be
al
lo
ca
te
d
M
in
im
iz
at
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of
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te
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en
ti
on
ti
m
e
–
Si
m
u
la
ti
on
A
R
E
N
A
E
co
n
om
ic
It
al
y
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ru
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ow
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d
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ob
bi
(2
0
0
9
)
O
p
ti
m
iz
at
io
n
of
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ca
ti
on
s
of
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ll
ec
ti
on
st
at
io
n
s
in
an
R
L
n
et
w
or
k
–
–
M
IL
P
C
P
LE
X
E
co
n
om
ic
D
en
m
ar
k
W
an
g
et
al
.
(2
0
0
8
)
O
p
ti
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iz
at
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th
e
tr
ea
tm
en
t
an
d
tr
an
sf
er
st
at
io
n
lo
ca
ti
on
s
T
ot
al
co
st
,
d
is
ta
n
ce
s
an
d
W
E
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E
am
ou
n
t
to
be
tr
an
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er
re
d
–
Fu
zz
y
m
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lt
i-
ob
je
ct
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e
IP
LI
N
G
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E
co
n
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ic
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h
in
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sh
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ej
in
g
(2
0
0
8
)
O
p
ti
m
iz
at
io
n
of
W
E
E
E
re
cy
cl
in
g
n
et
w
or
k
C
os
t
m
in
im
iz
at
io
n
–
M
IL
P
LI
N
G
O
E
co
n
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ic
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h
in
a
C
ag
n
o
et
al
.
(2
0
0
8
)
E
va
lu
at
io
n
of
th
e
ca
p
ac
it
y
an
d
co
st
of
th
e
ex
is
ti
n
g
R
L
n
et
w
or
k
–
–
A
n
al
yt
ic
al
m
od
el
–
E
co
n
om
ic
It
al
y
Le
e
an
d
D
on
g
(2
0
0
8
)
Lo
ca
ti
on
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ll
oc
at
io
n
of
p
ro
d
u
ct
re
co
ve
ry
n
et
w
or
k
–
–
T
w
o-
st
ag
e
h
eu
ri
st
ic
–
d
et
er
m
in
is
ti
c
p
ro
gr
am
m
in
g
C
P
LE
X
E
co
n
om
ic
Si
n
ga
p
or
e
Q
u
ei
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ga
et
al
.
(2
0
0
8
)
P
er
fo
rm
an
ce
ev
al
u
at
io
n
of
th
e
re
cy
cl
in
g
p
la
n
t
lo
ca
ti
on
s
–
–
M
C
D
M
–
P
R
O
M
E
T
H
E
E
–
E
co
n
om
ic
Sp
ai
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R
ou
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et
al
.
(2
0
0
8
)
D
et
er
m
in
at
io
n
of
be
st
W
E
E
E
m
an
ag
em
en
t
sc
en
ar
io
(i
.e
re
co
ve
ry
lo
ca
ti
on
s,
n
et
w
or
k
d
es
ig
n
)
–
–
M
C
D
M
-P
R
O
M
E
T
H
E
E
D
E
C
IS
IO
N
LA
B
so
ft
w
ar
e
E
co
n
om
ic
,
so
ci
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an
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en
vi
ro
n
m
en
ta
l
C
yp
ru
s
Sr
iv
as
ta
va
(2
0
0
8
a,
2
0
0
8
b)
C
os
t-
effi
ci
en
t
lo
ca
ti
on
–a
ll
oc
at
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of
va
lu
e
re
co
ve
ry
n
et
w
or
k
(i
.e
.
co
ll
ec
ti
on
ce
n
te
rs
an
d
re
w
or
k
fa
ci
li
ti
es
)
–
–
–
–
E
co
n
om
ic
In
d
ia
W
an
g
an
d
Y
an
g
(2
0
0
7
)
D
es
ig
n
in
g
Lo
ca
ti
on
an
d
co
n
fi
gu
ra
ti
on
of
re
cy
cl
in
g
n
et
w
or
k
M
ax
im
u
m
u
ti
li
za
ti
on
of
re
so
u
rc
es
an
d
m
ax
im
iz
at
io
n
of
re
ve
n
u
e
–
M
IL
P
C
P
LE
X
E
co
n
om
ic
T
ai
w
an
K
ar
a
et
al
.
(2
0
0
7
)
C
os
t
of
R
L
n
et
w
or
k
–
–
Si
m
u
la
ti
on
A
re
n
a
so
ft
w
ar
e
E
co
n
om
ic
A
u
st
ra
li
a
C
h
an
g
et
al
.
(2
0
0
6
)
Lo
ca
ti
on
se
le
ct
io
n
in
R
L
n
et
w
or
k
M
in
im
iz
at
io
n
of
th
e
to
ta
l
co
st
–
M
IP
LI
N
G
O
E
co
n
om
ic
C
h
in
a
A
h
lu
w
al
ia
an
d
N
em
a
(2
0
0
6
)
M
u
lt
i-
ob
je
ct
iv
e
op
ti
m
iz
at
io
n
of
R
L
n
et
w
or
k
M
in
im
iz
in
g
en
vi
ro
n
m
en
ta
l
ri
sk
an
d
co
st
–
IL
P
–
E
co
n
om
ic
an
d
en
vi
ro
n
m
en
ta
l
In
d
ia
(c
on
ti
nu
ed
on
ne
xt
pa
ge
)
M.T. Islam, N. Huda Resources, Conservation & Recycling 137 (2018) 48–75
56
T
ab
le
2
(c
on
ti
nu
ed
)
R
ef
er
en
ce
M
od
el
fo
cu
s
O
bj
ec
ti
ve
fu
n
ct
io
n
s
U
n
ce
rt
ai
n
ti
es
/
co
n
st
ra
in
ts
co
n
si
d
er
ed
in
th
e
m
od
el
U
ti
li
ze
d
m
od
el
in
g
ap
p
ro
ac
h
So
lv
ed
by
Su
st
ai
n
ab
il
it
y
d
im
en
si
on
co
n
si
d
er
ed
C
ou
n
tr
y
Fr
an
k
e
et
al
.
(2
0
0
6
)
R
em
an
u
fa
ct
u
ri
n
g
ca
p
ac
it
ie
s
an
d
p
ro
d
u
ct
io
n
p
ro
gr
am
s
op
ti
m
iz
at
io
n
–
Q
u
al
it
y,
qu
an
ti
ty
,
re
li
ab
il
it
y
of
ca
p
ac
it
ie
s,
p
ro
ce
ss
in
g
ti
m
es
,
d
em
an
d
fo
r
re
m
an
u
fa
ct
u
re
d
p
ro
d
u
ct
s
IL
P
LI
N
G
O
E
co
n
om
ic
G
er
m
an
y
N
ag
u
rn
ey
an
d
T
oy
as
ak
i
(2
0
0
5
)
D
ev
el
op
m
en
t
of
re
cy
cl
in
g
p
ol
ic
y
in
st
ru
m
en
ts
fo
r
m
u
lt
i-
ti
er
ed
re
cy
cl
in
g
n
et
w
or
k
–
–
–
FO
R
T
R
A
N
ba
se
d
al
go
ri
th
m
–
U
SA
Sh
ih
(2
0
0
1
)
O
p
ti
m
iz
at
io
n
of
in
fr
as
tr
u
ct
u
re
d
es
ig
n
an
d
re
ve
rs
e
n
et
w
or
k
fl
ow
(C
ol
le
ct
io
n
an
d
re
co
ve
ry
lo
ca
ti
on
s,
re
so
u
rc
e
al
lo
ca
ti
on
s,
m
at
er
ia
l
fl
ow
s)
M
in
im
iz
at
io
n
of
to
ta
l
co
st
Fi
xe
d
co
st
an
d
op
er
at
io
n
co
st
,
an
d
re
ve
n
u
e
fr
om
se
ll
in
g
re
cl
ai
m
ed
m
at
er
ia
l
M
IP
–
E
co
n
om
ic
T
ai
w
an
Fl
ei
sc
h
m
an
n
et
al
.
(2
0
0
1
)
Im
p
ac
t
of
p
ro
d
u
ct
re
co
ve
ry
on
R
L
n
et
w
or
k
an
d
fa
ci
li
ty
lo
ca
ti
on
–
–
M
IL
P
C
P
LE
X
E
co
n
om
ic
T
h
e
N
et
h
er
la
n
d
s
So
d
h
i
an
d
R
ei
m
er
(2
0
0
1
)
R
ec
yc
li
n
g
n
et
w
or
k
m
od
el
in
g
–
–
N
on
-l
in
ea
r
m
at
h
em
at
ic
al
p
ro
gr
am
m
in
g
C
P
LE
X
an
d
G
en
er
al
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M.T. Islam, N. Huda Resources, Conservation & Recycling 137 (2018) 48–75
57
design (OLND), 2) Closed-loop network design (CLND), 3) Analyzing
third-party reverse-logistics provider (3PRLP) selection, and 4) Vehicle
routing problem (VRP). In the following sub-sections, the papers are
reviewed in details.
3.1.1. Open-loop network design (OLND)
According to Salema et al. (2007), ‘’An RL network establishes a
relationship between the market that releases used products and the
market for “new” products. When these two markets coincide, we talk
of a closed-loop network, otherwise of an open loop’’. OLND focuses on
the activities and flows of the reverse channel. Collection, inspection,
sorting, disassembly, reprocessing/recycling, and disposal operations
are the major RL activities, with the flow of returned products from one
place/process to another (Akçalı et al., 2009). The selected papers
under the heading of OLND in this study are divided into 4 major
subcategories that are described in this subsection. The detailed sum-
mary of the OLND studies is illustrated in Table 2.
3.1.1.1. Location-allocation problem. Shokouhyar and Aalirezaei (2017)
determined the most appropriate locations of collection centers (CCs)
and recycling plants (RPs) in a WEEE RL network in Iran using multi-
objective genetic algorithms (GA). Important decisions on the trade-off
among social, environmental and economic impacts of the network
design can be made from this study. Ayvaz et al. (2015) developed a
two-stage stochastic programming model that determined optimal
locations for collecting, sorting and recycling centers (RCs). Besides
finding the locations, it also determined the amount of WEEE (in
weight) to be transported between nodes in a generic RL network. Kilic
et al. (2015) developed a stochastic mixed-integer linear programming
(MILP) model that determined the optimum locations of storage sites
and recycling facilities that fulfill the minimum recycling rate
prescribed by EU WEEE Directive 2012/19/EU (Directive, 2012).
Shokohyar and Mansour (2013) developed a simulation-based op-
timization model to determine the optimal locations for CCs and RPs in
a network. This research considered three dimensions of the sustain-
ability criteria. Considering a social sustainability indicator, this re-
search considered employment, damage to the worker, local develop-
ment. Total net profit was considered under an economic sustainability
indicator, while the environmental impact was quantified using an Eco-
indicator related to WEEE transportation. Gomes et al. (2011) proposed
a generic nationwide WEEE recovery network (RN) model to identify
the best location of CCs and sorting centers (SCs) with short-term
(tactical – less than a year) network planning. Besides economic cost,
environmental costs attributed to CO2 emissions may influence network
decisions – locations and mode of transport. Tuzkaya et al. (2011) de-
veloped a novel methodology for RL network design (RLND) that uti-
lized integrated multi-criteria decision making (MCDM) and GA
methodology to investigate two strategic-level (long-term) objectives
such as the best possible locations for CCs and cost minimization of the
RL network.
Xianfeng et al. (2010) proposed a linear-programming (LP) model
for the recycling network to identify collection and recovery locations,
resource allocation, and material flows of the network. This simulation-
based work identified that the uncertainties of the recycling network
were time, quantity and quality and recycling levels. Hanqing and Ru
(2009) analyzed a model that was concerned with a self-sustaining
recovery pattern of a 3PRLP focusing on appropriate recovery locations.
Wang et al. (2008) developed a fuzzy multi-objective LP model that
optimizes the locations of transfer stations (TSs) and treatment facilities
(TFs) considering five objective functions. Achillas et al. (2010) pre-
sented a decision support tool for policy makers to optimize the existing
infrastructure of collection points and recycling facilities in an RL
network in Greece. The authors implemented mixed integer linear
programming as their modeling approach which was later solved by
CPLEX solver.
Chang et al. (2006) developed a mixed-integer programming (MIP)
model that aimed to optimize the RL network structure and minimize
the total cost including the collection cost, fixed costs, transportation
cost, daily operation cost, waste disposal cost. Cost minimization was
achieved by selecting optimum locations for disassembly/reprocessing
plants in the network. Shih (2001) proposed an optimization model for
infrastructure design and reverse network flow for home appliances and
computers in Taiwan. In the model, the authors considered the total
cost (e.g. transportation cost, operating cost, fixed cost for new facil-
ities, final disposal cost and landfill cost) minimization in various as-
pects of the RL network such as collection and recovery locations, re-
source allocations and material flows within the network. Chong et al.
(2014) examined an economically self-sustained RL network design
considering collection centers, processing centers, transportation, sec-
ondary market, recycle centers and disposal sites that can cover the
overall expenses of an RL system.
Ayvaz and Bolat (2014) presented a two-stage stochastic RLND
model making strategic decisions on RP locations. Wang et al. (2011)
developed a multi-echelon RL network for the purpose of collecting and
processing WEEE. The authors tried to identify the best possible loca-
tions of CCs and disposal stations. Source-specific circulation of WEEE
from collection centers to disposal facilities was also identified. Grunow
and Gobbi (2009) developed an MILP model to evaluate the config-
uration of the existing CC’s locations. The study found that collective
schemes (in Danish Producer Responsibility System) are economically
beneficial for logistic activities, better-off in developing a competitive
market and cost-efficient in providing services.
To achieve better RSC management with flexibility in its design,
Wang and Yang (2007) developed an MILP model that integrated fa-
cility location and configuration problems of WEEE recycling. Max-
imizing the overall utilization of the returned products and revenue
generation from recycling were the two major objectives in their RL
modeling. Guerra et al. (2009) developed a modular simulation model
for the number of vehicles to be assigned in an RL network considering
minimization of the intervention time at the collection centers.
Ahluwalia and Nema (2006) developed an integrated planning and
design model using integer linear programming (ILP) to minimize the
environmental risk as well as cost from a computer-waste management
system. With the model, they presented a decision support tool that can
be used to select an optimum configuration of waste management fa-
cilities – segregation, storage, treatment/processing, reuse/recycle and
disposal, and allocation of waste in the facilities. Zhi et al. (2010) for-
mulated a two-stage resource-constrained project-scheduling problem
(RCPSP) based RL network with a remanufacturing focus. Minimizing
costs, quantity of WEEE, and return and disassembly centers were the
major objects of the modeling. Authors found that RCPSP is beneficial
for WEEE take-back logistics when locations of the collecting centers
and disassembly centers are uncertain.
3.1.1.2. Product recovery (PR). Qiang and Zhou (2016) developed a
robust RL network-optimization model considering uncertainty of
recovery on the basis of a risk preference coefficient and a penalty
coefficient. Assavapokee and Wongthatsanekorn (2012) created a
deterministic strategic infrastructural RLND for the state of Texas in
the USA, so that product recovery activities can be supported by the
network for old TVs, CPUs and CRT monitors. Golinska and Kawa
(2011) proposed a recovery network arrangement (RNA) model with a
focus on recycling. The authors solved problems arising in the typical
dynamic configuration of an RL network – goods flow visualization,
coordination mechanism with FL, minimization of delivery time, stock
and cost.
Kawa and Golinska (2010) proposed a model to restructure the
configuration of a recycling RN for waste computers in a dynamic
supply-chain scenario where recycling enterprises are dependent on
each other. Their model provided potential ways in finding cost-effi-
cient supply-chain paths of the whole enterprise network, according to
their individual appropriate capacities. The leader company in the
M.T. Islam, N. Huda Resources, Conservation & Recycling 137 (2018) 48–75
58
supply-chain network can provide supply of recycled materials to its
customers quickly with competitive price. Cagno et al. (2008) proposed
an analytical model for RN to evaluate the capacity and cost of the
existing network of refrigerator recycling with an estimation of future
values. Lee and Dong (2008) developed a network-flow-based de-
terministic programming model for the purpose of designing an end-of-
lease computer products RN that consists of both forward and reverse
logistics flow.
Srivastava (2008a, 2008b) designed a multi-period value RN of re-
turned white goods such as refrigerators, washing machines. He found
that for flexible volume acquisition, remanufacturing is not a viable
economic proposition for India. Fleischmann et al. (2001) developed a
facility location model for PR and remanufacturing by integrating RN
with the existing RL structure in the Netherlands. Fixed costs, trans-
portation costs, rate of return, recovery processing technology, com-
bining FL with reverse transportation, regional legislative requirements
and EOL management were considered in the model. Sodhi and Reimer
(2001) developed a non-linear mathematical programming model for
optimizing recycling operations (i.e. disassembly and material-recovery
decisions of recyclers and processors) in such a way that the net cost for
material removal becomes a minimum, thus economic sustainability of
WEEE recycling can be achieved. Krikke et al. (1999) established a
stochastic dynamic-programming (SDP) model to determine an optimal
degree of disassembly with optimal recovery and disposal options, so
that the recycling cost of PC monitors can be reduced.
Piplani and Saraswat (2012) developed a min-max based robust
optimization model using MILP to determine the suitability of facility
utilization according to product flow and to address the uncertainties of
the repair and refurbishing network, namely as number of products
returned, percent of faulty products and warranty fraction of modules.
Xie et al. (2013) proposed a model on an RL reuse network based on
the election-campaign algorithm (ECA). Experience from the domestic
and overseas research about RL management, the authors provided an
optimized model that focus the minimization of disused electronic
items at regional level in China. Kara et al. (2007) developed a simu-
lation-based RL network model for collecting EOL white goods from the
Sydney Metropolitan Area in Australia. With the study, it was under-
stood how the collection system interacted with the current WEEE
management structure.
3.1.1.3. Cost. Shanshan and Kejing (2008) developed an integrated
optimization model for location of the disassembly and bulk recycling
facilities in a recycling network. In addition, optimized material flows
among different actors in the network were determined, where cost
minimization was considered as the objective function. Yu and Solvang
(2016) proposed a stochastic optimization model to design and plan an RL
system considering economic efficiency and environmental impacts on the
system. The model provided policy implications for government authority
in allocating subsidies for companies working with WEEE treatment.
Elbadrawy et al. (2015) proposed a mathematical model for an RL
recycling network that aimed to minimize the total cost of the network,
consisting of collection cost, installation cost of setting up sorting fa-
cilities, repairing. Besides, the costs, the model also considered the
processing capacity of the recycling facilities and the optimal trans-
ported weights of WEEE from collection to recycling facilities. Yu and
Solvang (2013) designed an RL network to treat multi-sourced WEEE
considering environmental (in the form of greenhouse gas emission
from transportation) and economic (cost minimization) dimensions.
They found that, even though reuse, repair, remanufacturing and re-
cycling of WEEE significantly increases the profit of the network, gov-
ernment still needs to provide subsidies and incentives to operators
present in the RL network. Cao and Zhang (2011) proposed an in-
tegrated method based on multi-objective optimization (NSGA II) and a
multi-attribute decision-making model analyzing the optimal flow of
WEEE in an RL network considering the total profit and accumulated
energy consumption in the network.
Dat et al. (2012) proposed an RL network-optimization model for
recycling that aimed to minimize the total processing cost of the net-
work. They found that, in order to reduce the total cost, the transpor-
tation cost should be minimized. Achillas et al. (2012) presented a
single-period multi-criteria optimization model for multi-type carriers
of WEEE to allocate the types of carrier to be used in an RL network.
Total logistics costs, consumption of fossil fuel and production of
emissions due to transportation were estimated by the model. Deng and
Shao (2009) proposed an analytical recycling network configuration
model to find the total minimum cost (transportation cost, operating
cost and final disposal cost) in the presence of a recycling capacity
constraint of the network, and sales revenue of reclaimed materials
derived from the network. The authors found that WEEE compression at
pre-processing sites is an important task for the entire recycling process
and provided the essential implication of product design for recycling.
3.1.1.4. Secondary market. Rousis et al. (2008) developed a decision-
making model based on the MCDM method using PROMETHEE to
investigate possible alternative scenarios for WEEE management in
Cyprus. According to the developed model, partial disassembling of
WEEE and forwarding the recyclable material fractions to secondary
markets and disposing of the residues to landfills was the best scenario
in the existing setting. Franke et al. (2006) developed a generic mobile-
phone remanufacturing plant’s capacity planning and facility adoption
planning by using a discrete-event capacity and program planning
simulation model. In the model, they considered uncertainties in the
remanufacturing process, such as the quantity and condition of mobile
phones, reliability of capacities, processing times, and demand for
remanufactured product.
Nagel and Meyer (1999) proposed a new approach that system-
atically analyzed and modeled EOL networks, focusing on disassembly
and recycling of refrigerators in Germany from ecological and eco-
nomical points of view. Bereketli et al. (2011) developed a fuzzy linear-
programming technique for multi-dimensional analysis of a preference
(LINMAP) model to evaluate and select the best WEEE treatment
strategy in an RL network. It was found that reuse and recycling were
the best strategy in the current management practice in Turkey.
Choi and Fthenakis (2010) developed an operational mathematical
model to assess the feasibility of developing a recycling infrastructure
for thin-film solar photovoltaic (PV) waste. The authors intended to
propose a generalized framework to overcome the challenges in PV
waste recycling experiencing mathematical models proposed for other
waste products. Nagurney and Toyasaki (2005) presented a multi-tiered
network equilibrium model that focused on a policy instrument for
recycling. They found that policy instruments that involve original
equipment manufacturers (OEMs) and integrate a classic supply-chain
network with recycling perform best in terms of efficiency and effec-
tiveness, as seen in Japan and in European member states. Liu et al.
(2014) developed an evolutionary RL network model that measured the
enterprise’s logistics capability standard as an effective output of the
network. In this study, authors formulated the problem in multiple
objective programming using LINDO6.1. The results showed that
maximum utilization of processing centers in the network had impact
on lower operating cost and maximum profit for recycled products
prepared for secondary market.
3.1.1.5. After-sales service. Due to increasing customer awareness and
EPR policy, manufacturers are now responsible for product servicing
after selling their equipment to ensure better economic and
environmental performance. Besides the traditional purchase of EEE,
leasing and offering product warranty became popular means of
minimizing waste generation as well as prolongation of EOL phase of
the EEE (Mont, 2000; Shokohyar et al., 2014).
Shokohyar et al. (2013) presented an integrated MIP and simula-
tion-based optimization model to determine the optimal number of
leasing periods, the optimal duration of leasing period and optimal EOL
M.T. Islam, N. Huda Resources, Conservation & Recycling 137 (2018) 48–75
59
options for the diverse range of components of returned product con-
sidering uncertainties of repair and replacement services performed at
the time of leasing periods. The results of the study provide optimal
solutions to EEE leasing companies achieving maximum profit,
minimum environmental impact and selecting best EOL option.
Considering producer and customer’s viewpoint towards after sales
service, Arabi et al. (2017) developed a Stackelberg game theory
finding optimized periods of warranty (for in-use products) and out-of-
warranty (EOL products) that minimize total cost incurred during the
periods. The authors implemented backward induction method solving
the problem and results of the study showed that offering warranty
period not only optimizes the cost of the producer and consumer but
also helps to extend the product lifecycle towards achieving environ-
mental objectives.
3.1.2. Closed-loop network design (CLND)
Network design with CLSC refers to transforming a supply chain
into a closed-loop entity by forming a direct and coordinated re-
lationship between FL activities (i.e. material processing, manu-
facturing, and distribution) and tasks associated with RSC (Akçalı et al.,
2009). Compared to OLND, only a few studies have been found that
considered a CLSC network focusing on WEEE; they are discussed in
this part of the paper. A summary of the CLND studies are presented in
the Table 3.
3.1.2.1. Location-allocation problem. Chen et al. (2015) developed a
CLND in which the delivery routes and quantity of different materials
derived from printer cartridges were considered, for achieving a
maximum recycling rate and profit. Their model provided near-
optimal and time-efficient solutions for optimization of the CLSC
network. Amin and Zhang (2013) proposed a multi-objective three-
stage CLSC model to evaluate and select three major factors in a
network that determine the configuration of the network: suppliers of
used products, remanufacturing subcontractors, and refurbishing sites.
Qiang et al. (2013) investigated a CLSC network in the USA, con-
sidering competition, distribution-channel investment, and un-
certainties in the recycling network (i.e. yield rate and demand) for
printer cartridges. In their model, they considered three decentralized
decision makers – raw-material suppliers, manufacturers (they collect
recycled products directly from the demand market), and retail outlets.
Alumur et al. (2012) proposed a multi-period profit maximization CLSC
model aiming to improve the network configuration and capacities of
inspection centers and remanufacturing plants by optimizing locations.
The model made an impact on reducing transportation costs between
facilities.
Amin and Zhang (2012) proposed an MILP model based on re-
turn–recovery pairs and PLC to configure a CLSC network that consisted
of manufacturer, collection, repair, disassembly, recycling, and disposal
sites for waste mobile phones in Canada. Krikke (2011) proposed a
CLSC network-configuration model with combined disposition and lo-
cation-transport decisions to assess the impact of photocopier machine
recovery and remanufacturing on carbon foot printing. The author
found that a regional CLSC network could perform efficiently and ef-
fectively when recycling is included.
Easwaran and Üster (2010) presented a multi-product CLND model
that considered hybrid manufacturing/remanufacturing facilities and
finite-capacity hybrid distribution/collection centers to serve a set of
retail locations. Chandiran and Surya Prakasa Rao (2008) investigated a
centralized CLSC network-design model that had facility location and
network configuration for distribution and collection of spent batteries.
Decentralized network, manufacturer’s dilemma in managerial control
over the collection, disturbance to existing network, time pressure and
integral design of both reverse and forward supply chain flow were
addressed in the study.
Schultmann et al. (2003) developed a hybrid CLSC planning and
optimization model that deals with location-specific recycling options
for spent batteries in the steelmaking industry. They found that the
performance of recycling can be improved by modifying the recovery
strategies of a network. Jayaraman et al. (1999) proposed a re-
manufacturing-focused CLSC model that focused on the location of re-
manufacturing/distribution facilities, the trans-shipment, production,
and stocking of the optimal quantities of remanufactured products, and
managerial decisions.
Gupta and Evans (2009) developed a multi-product multi-objective
goal-programming (GP) model that analyzed the operational level of a
CLSC using three different techniques – why–what’s stopping analysis,
fundamental objective hierarchy, and means objective network.
3.1.2.2. Cost. Kannan et al. (2010) developed a mathematical model
using MILP considering a multi-echelon, multi-period, multi-product
CLSC network with a focus on cost reduction, for making decisions in
the material procurement, distribution, recycling and disposal of waste
batteries. Fernandes et al. (2010) constructed a CLSC network-
optimization model of spent lead batteries considering production of
the batteries, their distribution to customers, and EOL collection in
Portugal. The costs included in their modeling were cost of opening
warehouses, raw materials acquisition from supplier, EOL product
acquisition from customers, and transportation resources. Grant and
Banomyong (2010) investigated product-recovery-management related
activities that affected the strategic design and implementation of a
CLSC for single‐use cameras. They found that OEMs could benefit from
the entire supply chain by standardizing high‐quality raw materials,
using a modular product structure, maintaining control over cost of the
entire process and avoiding third‐party collectors and processors.
Chouinard et al. (2008) proposed a stochastic programming model
to design a CLSC network considering location specific network-design
decisions such as recovery and demand volumes with respect to capa-
city constraints and operating costs. Hammond and Beullens (2007)
presented a variational inequality approach to strategic modeling of
oligopolistic CLSC considering legislation. The authors suggested that
reverse-chain activities could be stimulated by legislation when some
minimum recovery levels of all new products were included. On con-
trary, when there is interdependence of a number of factors: increase in
collection targets, landfill costs and manufacturer-pay schemes, legis-
lation became difficult to implement.
According to Mata-Lima et al. (2013) the dimensions of the sus-
tainability triangle comprise social, economic and environmental as-
pects linked with technology. Considering these dimensions, papers on
both OLND and CLND were analyzed for which dimension they cov-
ered. Fig. 10 shows the coverage of sustainability dimensions in the
network-design studies. It was found that the economic dimension was
given the highest priority in designing the networks, whereas social and
environmental issues are poorly addressed. Only three studies were
found that considered economic, social and environmental dimensions
all together.
Another important aspect in network design is the consideration of
uncertainty. Fig. 11 shows the percentage of different uncertainty
parameters considered in the network-design studies. The returned
amount (28%) was found to be one of the most used uncertainty
parameters in designing networks whereas environmental influence,
source and reliability of capacities were considered relatively less (only
3%).
3.1.3. Analyzing third-party reverse-logistics provider (3PRLP) selection
The concept of 3PRLP was introduced after the successful experi-
ence from third-party logistics (3 PL) in the forward supply chain
(Mahmoudzadeh et al., 2013). Krumwiede and Sheu (2002) studied
flexibility of transportation in RL activities. It showed that 3PRLP plays
a significant role by taking back obsolete items from customers/end-
users in implementing EPR principles. In this study, out of the 157
papers (in the main research areas), only 11 papers focused on the
3PRLP problem; they are discussed in this subsection of the paper.
M.T. Islam, N. Huda Resources, Conservation & Recycling 137 (2018) 48–75
60
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s
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m
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9
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em
an
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fa
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ri
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fo
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d
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M
in
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es
th
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E
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U
SA
M.T. Islam, N. Huda Resources, Conservation & Recycling 137 (2018) 48–75
61
Sabtu et al. (2015) presented a study to find influential attributes for
selecting and evaluating 3PRLP. They found that the organization role
was the most significant attribute that intensified the third party logistic
provider’s performance. Xuping et al. (2013) investigated the re-
lationship between production enterprises and 3PRLP. They found that
3PRLP’s environmental protection ability and effort level towards
working with asymmetric information under the constraints determines
the financial incentive for recycling. Atasu et al. (2013) developed a
mathematical model to investigate the impact of the collection cost
structure on the optimal reverse-channel choice of manufacturers who
have the ability to shape the sales of retailers, and collection quantity
(in the case that manufacturers remanufacture their own products).
Wei and Zhao (2013) investigated the decisions of reverse-channel
choice in a fuzzy CLSC environment where a manufacturer, a retailer,
Fig. 10. Sustainability dimensions considered in open-loop and closed-loop network-design studies.
Fig. 11. Uncertainty parameters in RL/CLSC WEEE network designs.
M.T. Islam, N. Huda Resources, Conservation & Recycling 137 (2018) 48–75
62
and a third party collect used products for profit in three different
collection modes. The authors considered the demand, manufacturing
cost and collecting cost are fuzzy rather than stochastic or determi-
nistic. Hong and Yeh (2012) developed a retailer-non retailer collection
model for profit maximization. In the retailer-based collection model, a
manufacturer cooperated with a third-party to collect the used product
from customers, and in a non-retailer case, a third-party company is
commissioned by the manufacturer for collection activities. The re-
search found that when the return rate, manufacturer’s profits, and
channel members’ total profit were considered, non-retailer based col-
lection performs better than the other. However, if the third-party firm
is a not-for-profit organization working for recycling and disposal, then
retailer-based collection outperforms.
Sasikumar and Haq (2011) designed an optimized multi-echelon,
multi-product closed-loop distribution supply chain (CLDSC) network
integrating the issue of selecting the best 3PRLP in order to achieve
efficiency in cost and an optimum delivery schedule. Results of the
study showed that cost reduction from CLDSC could be achieved by
optimizing the cost of the forward-distribution channel. Cheng and Lee
(2010) developed a decision-making approach for practitioners of RL in
industrial marketing on outsourcing of 3PRL for the thin-film-transistor
liquid-crystal display (TFT-LCD) sector in Taiwan. The authors found
that information technology (IT) management is an essential activity in
outsourcing (in terms of accommodating return) and this task can be
performed better by 3PRLP than the manufacturers of TFT-LCDs.
Kannan (2009) proposed a structured model for evaluating and se-
lecting the best 3PRLP under a fuzzy environment for the battery in-
dustry by formulating the problem as MCDM which was solved by the
AHP and fuzzy analytic hierarchy process (FAHP).
Yuksel (2009) developed a WEEE collection-center location model
for 3PRLP considering three factors – cost, accessibility and environ-
ment using the AHP method. The model evaluated the existing locations
of the centers in Turkey then compared with the best alternatives.
Xanthopoulos and Iakovou (2009) proposed a methodology that aimed
to integrate optimal designing of disassembly processes and aggregate
planning of the recovery processes for WEEE. In the study, a simulation
was implemented for capturing uncertainties in RL operations. The
overall objective of the methodology was to recover both ecological and
economic value from the recovered WEEE items, and thus reduce the
produced quantities of WEEE. This methodology provided effective
decision support to mid-level management involved in resource re-
covery. Xu (2008) introduced a WEEE take-back information platform
based on the Electronic Product Code (EPC) that allowed involvement
of various agents in the RSC for information sharing and to measure the
responsibility and efficiency of the 3PRLPs in the take-back system.
3.1.4. Vehicle routing problem (VRP)
Based on combinatorial optimization and IP, the vehicle routing
problem (VRP) typically seeks the optimum set of routes in a network
for vehicle fleets delivering goods or services to a given set of customers
at minimum cost (Dantzig and Ramser, 1959). In the conventional FSC,
vast number of papers were published, however, in the RL/CLSC lit-
erature, this topic should be considered as new. In this subsection the
papers are summarized.
Mar-Ortiz et al. (2013) designed a Greedy Randomized Adaptive
Searching Procedure (GRASP) algorithm to determine the collection ca-
pacity and processing time of a fixed and heterogeneous fleet of vehicles
with special features that were generally used in the collection of WEEE
from customers. Mar-Ortiz et al. (2012) developed an algorithm to opti-
mize emerging waste-white-goods collection systems with three different
manufacturing interfaces: network design, vehicle routing and cellular
disassembly. Mar-Ortiz et al. (2011) proposed a facility-location oriented
collection vehicle routing model to evaluate the overall performance of
collection routes and to optimize a recovery network (RN) in Spain. The
authors redesigned the recovery network and reduced the number of ve-
hicles and the depot size required in the collection route.
Gamberini et al. (2010) presented a WEEE transportation-optimi-
zation network model that considered both technical (in terms of sa-
turation of vehicle capacity, the utilization of vehicle working times)
and environmental performance. Manzini et al., 2011 proposed a model
that integrated VR and the allocation of customer demand (according to
suppliers) under various modes of transportation. Both cost and en-
vironmental effects minimization were considered in the model that
supported decision making in transport planning. Gallo et al. (2010)
proposed a methodology to analyze the processing time at collection
centers to treatment centers combining VR. The research identified
efficiency parameters in waste recovery from the customer at the col-
lection center and reprocessing center, for recycling that quantifies the
current trend of WEEE flows. Guerra et al. (2009) described a logical
model of VRP that analyzed the WEEE distribution flow that consisted
of the number of vehicles allocated within a region in Italy and the
minimum intervention time required at the collection centers. The re-
search explored different network configurations and scenarios without
imposing high costs, which was achieved by information on the number
of vehicles to be adopted in the network.
Kim et al. (2009) presented a VR model in order to minimize the
transport distance from WEEE CCs (of local authorities) and distribu-
tion centers of major manufacturers to four regional recycling centers
located in Korea. Fernández et al. (2006) presented a recycling-focused
RSC model concerned with the optimum amount of waste mobile
phones to be collected to guarantee the supply of waste for recycling
companies. In this VR problem, they considered: 1) the locations of the
central and transfer stations, 2) the limited capacity in the VR and 3)
the presence of multiple depots in the network. They found that in long-
term planning, if a centralized recycling facility is considered in a
network it will not be profitable.
3.2. Analyzing the decision-making and performance-evaluation studies
A vast area of research in the RL/CLSC of WEEE focuses on decision
making and performance evaluation of the RL/CLSC processes (see
Fig. 1) and networks (including transportation), the economic and en-
vironmental performance of organizations and businesses and WEEE
management. Product acquisition, collection, inspection and sorting,
and disposition (i.e. recycling, reuse, repair, remanufacturing and dis-
posal) are the major RL/CLSC processes (Agrawal et al., 2015). The
papers that considered the issue are given in detail in following sub-
sections. The papers are summarized in Table 4.
3.2.1. RL/CLSC process perspectives
Tari and Alumur (2014) incepted a multi-period multi-objective
mixed-integer programming decision making model for distributing
collected WEEE from collection centers to recycling firms, with same
amount. The three objectives were considered: cost minimization,
equity among different firms, providing steady flow of products to each
firm. Additional focus were given on collection center location and
capacities of the collection centers in a given planning horizon. Temur
et al. (2014) developed an evaluation criteria to measure performance
of a RL system, considering social acceptability, environmental risks,
biodiversity conservation, operation and investment costs, energy and
transportation infrastructure, legal/political environment, and growth
potentials.
Moussiopoulos et al. (2012) proposed a model for evaluating the
justification of the present facility locations with future alternatives for
WEEE collection in Greece. They also estimated transportation costs by
considering national and local waste management conditions, practices
and possibilities. They found that WEEE management system can only
be profitable when the quantity recovered is maximum. Ponce-Cueto
et al. (2011) developed a model using AHP to make decision on col-
lection center locations for waste batteries. Calculating the distance
between collection points for the model was solved by Visual Basic
computer programming. Theoretically, with the model maximum
M.T. Islam, N. Huda Resources, Conservation & Recycling 137 (2018) 48–75
63
T
ab
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4
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M
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co
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)
M.T. Islam, N. Huda Resources, Conservation & Recycling 137 (2018) 48–75
64
T
ab
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V
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0
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an
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is
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m
ak
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in
R
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p
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je
ct
se
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–
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0
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–
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0
1
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ai
w
an
M.T. Islam, N. Huda Resources, Conservation & Recycling 137 (2018) 48–75
65
number of batteries can be collected from the network in Spain.
Machado et al. (2010) developed an assignment model to evaluate
the minimum transportation cost considering distances from sorting
centers to treatment and recovery centers, and flow of WEEE within the
centers. Result of the study found that utilization of the model effec-
tively increase the performance of WEEE management than the existing
RL structure in Portugal. Tonanont et al. (2008) developed a mea-
surement framework analyzing the performance of reverse channels
considering four perspectives – customer satisfaction, sorting and
storing, asset recovery and transportation. They used Balanced Scor-
ecard (BSC), AHP and Data Envelopment Analysis (DEA).
Wadhwa et al. (2009) developed a group decision support tool by
using MCDM and fuzzy-set theory to rank reprocessing alternatives to
design effective and efficient flexible return policy considering cost,
time, legislative factors, environmental impact, quality and secondary
market. The authors ranked the alternatives as Reselling >
Repairing > Cannibalization > Remanufacturing > Refurbishing.
Zhang et al. (2004) developed a conceptual web based information
system that was able to provide decision support for reverse manu-
facturing and product environmental impact assessment; and evaluate
operations for recycling and remanufacturing process.
Jayaraman (2006) presented an analytical approach to production
planning and control for CLSC, focusing on product recovery and reuse.
The model attempts to develop formal systems in an intermediate to
long-term planning environment that concerned remanufacturing ag-
gregate production planning, inventory control, and other tactical de-
cision-making. Ferrer and Ketzenberg (2004) developed a SDP using
Markov chain decision process to evaluate the value of information
(remanufacturing cost, process capabilities, facility performance) on
remanufacturing of products such as copier machines and medical
equipment.
Krikke et al. (2003) developed a quantitative decision model using
MILP for a CLSC design problem for refrigerators to identify optimal
locations (centralized vs decentralized) of the repair network, product
design for optimization of environmental impact and total cost asso-
ciated in the repair network. Ravi et al. (2005a, 2005b) proposed a
holistic framework based on ANP approach for selecting alternatives for
RL operations for EOL computers. In the model, the authors presented
determinants, dimensions, and enablers of the RL with alternatives in a
hierarchical form. Considering the dimensions, four perspectives were
derived by BSC analysis: customer, internal business, innovation and
learning, and finance.
3.2.2. Organizational and business perspectives
Liu et al. (2010) developed a WEEE RL performance evaluation
model based on multi-step fuzzy analytical method to observe the im-
pact of flexibility, openness and extensibility on RL capability at orga-
nization level. Shih et al. (2012) presented a forecasting model that
applied ANP process and sensitivity analysis to predict the sales volume
of printers in Taiwan, so that recycling and treatment fees as incentives
attained from the government can be adjusted for recycling industries.
The authors found percentage error that evolved using ANP was small
when it was compared with other statistical techniques. Subramanian
et al. (2013) investigated manufacturer’s component commonality de-
cision of remanufacturing in manufacturing and sales of new products
in a CLSC environment.
Nenes and Nikolaidis (2012) developed a multi-period MILP model
to manage used mobile phones return from the remanufacturing com-
pany’s perspective by incorporating multiple suppliers and several
quality levels of returned items for environmentally friendly and eco-
nomically viable reuse activities in Greece, under CLSC aspect. Li et al.
(2009) developed a two-step SDP optimization model to access the
optimal collection price of used-products considering risk attitude of
remanufacturer, and to estimate optimal selling price for quantity of
remanufactured products as profit of a remanufacturing enterprise in
China. Galbreth and Blackburn (2010) developed a mixed integer non-
linear programming (MINLP) model considering multi-commodity
network flow with economies of scale and product obsolescence when
off-shore remanufacturing was considered (in case of an electronic
manufacturing company).
Keh et al. (2012) investigated the performance of IBM Montpellier
on three main objectives: 1) economic opportunities via reselling and
reusing of parts and components, 2) dealing the issues of waste man-
agement and legislation compliance and 3) meeting social challenge by
preserving local jobs. All three dimensions of sustainability were
highlighted in the research from large multi-national company’s per-
spective under the case of CLSC. Maslennikova and Foley (2000) in-
vestigated the performance and productivity of Xerox in three areas –
environmental performance, customer satisfaction, and improved
company performance. The authors found that by incorporating design-
for-the-environment (DfE) principles into company’s strategic en-
vironmental goal, it reduce resource and energy consumption from
factories as well as boost revenue generation. The principles also pushes
product redesigning that eventually enhanced PR. Linton and Johnston
(2000) developed a decision support system (DSS) for Nortel Networks
in order to improve its remanufacturing operations for circuit assem-
blies. The modeling of the system consisted of algebraic equation and
simulation that offered an integration of RL operation with information
technology to better plan outbound and inbound product flows.
Sharma et al. (2007) developed a MILP model from the perspective
of an electronic equipment leasing company to assist better leasing,
logistics and asset management decisions including EOL disposal op-
tions. The primary objective of the study was to maximize the dis-
counted net profit of the system by gaining periodic revenue from
leasing assets. Potter et al. (2011) proposed a set of measures for au-
diting purposes to provide a clear picture of CLSC performance by in-
vestigating parameters such as, level of product stocks, effect of in-
accurate forecasting at organization level and acquisition of high
quantities of products before launching from integrated distribution
management of mobile phone. The article also identified links between
both faulty and non-faulty PRs in design, sourcing, manufacturing and
forecasting related to forward supply chain as well as the performance
in the integrated condition. Dhib et al. (2016) presented a compro-
mising strategy by using entropic analyze, ambiguity notions and co-
operative theory in order to evaluate the sustainable performance and
decision-making of WEEE management in Tunisia.
Guide and Pentico (2003) presented a closed-loop hierarchical planning
model that analyzed financial incentives to control PRs from managerial
perspective. The developed model intended to provide decision support in
product acquisition, operational planning and control, as well as demand
management and product pricing for in a remanufacturing of mobile phone.
Wee Kwan Tan et al. (2003) evaluated the performance of a US-based
computer manufacturing company which had RL operations in the Asia‐-
Pacific region. Authors found that repair, refurbishment, recovery and re-
turn management were the major operational RL activities of the company
and the critical implementation of IT based system to oversee off-shore PRs
was the major performance determinant.
Guide et al. (2008) developed an analytical model for disposition
decision driven PR considering time value of returned product, the
condition of the product and the impact of congestion at the printer
remanufacturing facility. They found that high decay rate coupled with
high facility utilization eventually increase the profit of the re-
manufacturer. Janse et al. (2010) developed a diagnostic tool that was
theoretically and empirically grounded to assess the practice and po-
tential improvement of RL activities of a consumer electronic company,
from business perspective. The authors identified that strategic part-
nerships, performance visibility, top management awareness, strategic
focus on PRs, reclaiming value from returns, and prompt supply of re-
manufactured products to market were the major areas that a company
should concern for performance improvement. Mukhopadhyay and Ma
(2009) developed a two stage stochastic programming model to de-
termine and evaluate optimal quantity of used products to acquire, and
M.T. Islam, N. Huda Resources, Conservation & Recycling 137 (2018) 48–75
66
production decisions (i.e. buying new parts from external suppliers) for
a remanufacturing firm.
Tan and Kumar (2008) developed a decision making model using LP
for manufacturers of computer to access the viability of RL operation
under profit maximization condition in Singapore. They found that RL
is profitable when return volume of waste computer is high and returns
are reused, repaired instead of disposal. Wee Kwan Tan and Kumar
(2006) developed a decision making model using system dynamics and
simulation for manufacturers of computer to maximize profit that can
be achieved from RL operations. The authors found part replacements
from hardware suppliers are more profitable than refurbished computer
parts. Regardless of return volumes (during processing), viability of the
operations can significantly be affected by transportation delay and
supplier delay. Ravi et al. (2008) proposed hybrid managerial decision-
making activity model using ANP and zero one goal programming
(ZOGP) for selecting feasible RL projects for EOL computers according
to the available resources of a company.
3.2.3. Product lifecycle perspective
Mazhar et al. (2007) presented an integrated approach to estimate
useful remaining life of electrical and electronic components for their
reuse using life cycle data analysis, Weibull and artificial neural net-
works. With the developed approach, effective life cycle time can be
estimated by experimental observation of motor speed, winding tem-
perature and power of a washing machine’s electric motor. The authors
claimed that the result provided a decision making tool for achieving
process and equipment down-time in a CLSC environment focusing on
reuse. Chung et al. (2014) analyzed life-cycle costs (LCCs) and life-cycle
energy consumption (LCEC) using architecture and supply chain eva-
luation method to provide the most beneficial modular structure pro-
duct design decisions from lifecycle perspective within CLSC environ-
ment.
3.3. Analyzing conceptual framework studies
According to Miles and Huberman (1994), conceptual framework is
a visual or written product, one that “explains, either graphically or in
narrative form, the main things to be studied—the key factors, con-
cepts, or variables—and the presumed relationships among them”. Due
to the complexity of EOL product characteristics and the involvement of
many different actors in RL/CLSC, new research areas were interlinked
by researchers from various disciplines. These studies generally try to
construct and suggest a new modeling approach, solution methodology,
analyzing approach or evaluation methodology based on a specific
problem (Govindan and Soleimani, 2017). For instance, Camgöz-Akdag
and Aksoy (2014) proposed a conceptual model for WEEE management
considering green-supply-chain management. The findings showed that
limited information from the manufacturing firms, finding available
data about the outcomes of the system, and the reluctance of firms to
share information were found to be major difficulties in implementing a
legislation-driven RL system. Some of the studies that used a conceptual
framework are described in this section.
3.3.1. RL/CLSC system and/or process focused studies
Pimentel et al. (2013) proposed a conceptual model for developing
an RL system in Brazil. Funding, system cost and development re-
quirements for the WEEE recycler’s certification were the major com-
ponents of the model. From an Asian perspective, Chong et al. (2014)
developed a conceptual mathematical model to assess the amount of
profit from reselling refurbished computers and components to cover
the overall expenses of an EOL computer RL system in Malaysia. Shi
et al. (2012) developed a model based on a framework of industrial
information integration engineering (IIIE) that focused on application
of enterprise systems or e-business systems in the RL process of used
batteries, investigating the information flows that can be implemented
in designing an RL system. The IIIE was developed back in 2008 as a
new discipline that focuses on application of computing technologies
with a wide range of engineering discipline. It represents a set of
foundational concepts and techniques that facilitate the industrial in-
formation integration process capable of integrating complex informa-
tion structure of an engineering system with emerging enabling tech-
nology such as Business Process Management (BPM), Service-oriented
Architecture (SOA) etc. (Da Xu, 2014).
3.3.2. Remanufacturing-focused
El korchi and Millet (2011) introduced a framework that allowed
generation of alternative structures that have less environmental impact
and higher economic benefits in RL, with a remanufacturing focus. The
authors found that the location of treatment facilities was the key
performance indicator of a remanufacturing system when integrated-
product forward logistics and reverse-logistics channel-design decisions
need to be made. Van Wassenhove and Zikopoulos (2010) developed a
conceptual mathematical model to estimate the grading errors occur-
ring because of overestimation of the quality of a returned product that
affects the optimal procurement decisions of a remanufacturer. Robotis
et al. (2005) studied the characteristics of remanufacturing as a tool to
develop a secondary market from a reseller’s perspective by developing
a conceptual mathematical model. For mobile phones, the authors
found that, based on the technology and competition in a market,
adding value by remanufacturing and making the used products more
attractive to customers can increase resellers’ profits significantly. This
way resellers could manage their inventory to serve a secondary market
and take important procurement decisions.
3.3.3. Recycling-focused
Li et al. (2010) presented a descriptive multi-level management
model to establish an RL coordination mechanism among Chinese re-
cycling companies in order to internalize the externalities of recycling,
such as air and land pollution, which were often not taken into con-
sideration by the policy makers. With the management model, the role
and responsibilities of the government departments and manufacturers
were highlighted, achieving larger profit from material recovery by
WEEE recycling. Walther et al. (2008) developed a conceptual mathe-
matical model using LP. Furthermore, the concept of a negotiation
approach was implemented into the programming via Lagrangian re-
laxation and sub-gradient optimization. In the model, a coordination
mechanism was established between one primary recycling company
and a group of other recyclers in a recycling network who must meet
the obligations of environmental legislation.
3.3.4. Organizational perspective
Lei and Qu (2011) analyzed obstacles, necessity, risks and func-
tional modules of an information-sharing platform in a virtual sym-
biotic network that allowed WEEE reverse-logistics stakeholders (i.e.
member enterprises) to realize effective communication among the
members. They found that, if information flow is utilized effectively, an
enterprise’s profit, environment benefits and social efficiency could be
attained. Atasu and Souza (2013) presented a conceptual deterministic-
monopoly demand-model in order to understand the trade-offs in pro-
duct recovery that affect a company’s choice (i.e. optimal quality and
pricing choices when compared with the benchmark scenario without
PR). The authors found that, depending on the form of PR, product
quality choice can be better or decline, while product take-back legis-
lation can induce an enhancement in quality choice by firms. In addi-
tion, it was found that EOL product can be collected either by a retailer
collection channel or by the original OEMs. Savaskan and Van
Wassenhove (2006) developed a model that focused on the interaction
of the manufacturer’s choice of collecting small consumer items such as
waste single-use cameras and mobile phones, and strategic product
pricing decisions when retailing is competitive.
At present, electronics manufacturers are attempting to create an
image of corporate citizenship that reflects their effort to deliver
M.T. Islam, N. Huda Resources, Conservation & Recycling 137 (2018) 48–75
67
environment-friendly products to customers. Guide and Wassenhove
(2001) developed a conceptual analytical framework to analyze the
profitability of reuse activities and PR management of the firms that
influence the operational requirement of business decisions (i.e. ac-
quisition price and the nominal quality of the returned product) in the
product-acquisition process. The authors found that product acquisition
was the control lever of an EOL PR system, and in reuse activities
profitability was a real concern for the firm.
Geyer and Doctori Blass (2010) developed a conceptual and de-
scriptive model that summarized the exiting business model of mobile
collection, reuse and recycling in the USA. They found that the in-
centives given to the manufacturers and refurbishers were not aligned
with the environmental-performance-examining reuse case.
3.3.5. Formal and informal sector
Ghisolfi et al. (2017) developed a model on social inclusion of in-
formal waste pickers into the environmental policy in the RSC in Brazil.
The authors found that, for developing such a system, environmental
policy should be restructured according to the country-specific WEEE
management agenda, with a high collection rate of used products, ro-
bust infrastructure, technology, supply of skilled labor and increase
demand for recycled products. Liu et al. (2016) proposed a quality-
based price competition model for PR in a dual-channel environment
(informal and formal sector recycling). During product acquisition,
quality is the single most important factor in determining the acquisi-
tion price of returned product for both sectors. In addition, they found
that the acquisition price is an important factor in a competitive re-
cycling market. When the government subsidy is low, the informal
sector is at the forefront in collecting WEEE, while the formal sector has
limited penetration in the market for PR. They suggested that the
controlling authority should re-adjust the subsidy level for the informal
sector and that the sector should only be considered for refurbishing
activities.
3.3.6. Product return
Srivastava (2008a, 2008b) proposed a model considering the stra-
tegic, operational and customer-service constraints of product returns
in the Indian context. Zikopoulos and Tagaras (2007) developed a
mathematical model considering RSC that consisted of two collection
sites and one refurbishing site that confronted a stochastic demand for
refurbished products in a single-period setting. With the model, the
authors investigated the impact of uncertainty in an inventory man-
agement scenario, when the returned product’s (e.g. computers, prin-
ters and mobile phones) quality affected the system profitability. With a
conceptual framework based on the Maximum Expected Utility (MEU)
principle, Parlikad and McFarlane (2007) showed that the availability
of product-specific information has a positive impact on PR. The au-
thors also found that Radio-frequency identification (RFID) was an ef-
ficient product identification technology that provided efficiency in PR
decisions.
3.3.7. Global reverse supply chain and climate change
Developing countries have already received an opportunity to get
carbon credit from developed countries under the Clean Development
Mechanism (CDM). Research conducted by Caiado et al. (2017) found
that WEEE is one of the growing waste streams in developing countries,
and with the novel concept of RL carbon credit, developing countries
could develop WEEE recycling and disposal infrastructure. Xu et al.
(2017) designed a conceptual global reverse-supply-chain (GRSC)
model using MILP for WEEE recovery and recycling under various un-
certainties (transportation costs and currency exchange rates) and
carbon emission constraints, considering transboundary movement of
WEEE from Greece to China. Landers et al. (2000) developed a con-
ceptual framework of a virtual-warehousing (VW) model for real-time
global visibility of logistics assets such as inventory and vehicles. With a
case study of a mobile phone company’s effort the authors found that
VW had a significant contribution to repair service when considering
transportation, labor costs and service times.
3.4. Analyzing the qualitative studies
Due to a growing environmental concern evolved in customers,
industry practitioners and government agencies in product disposal and
subsequent operations (i.e. in the RL and CLSC processes), there is a
necessity to identify how customers behave to specific actions taken at
regional level across the globe. Customers play significant role in RL
dispositions (Shumon et al., 2014). The topic regarding the level of
awareness towards WEEE and the behavior to dispose of it appro-
priately by customers received attention among researchers in shaping
RL/CLSC processes. Overall, in this study qualitative refers to the stu-
dies conducted via in-depth interviews and surveys, where the re-
spondents were customers, companies, and other stakeholders/actors
associated in the WEEE RL/CLSC activities. This specific study type can
lead to new theory development via practical understanding and
knowledge (Govindan et al., 2015).
Jafari et al. (2017) the investigated factors affecting a resident’s
behavior in returning WEEE and participating in RL activities in Iran.
The authors conducted a questionnaire survey followed by a statistical
analysis with logistic regression using Minitab and SPSSS. In the re-
search, a consumer’s incentive dependency towards WEEE recycling
was characterized, and it was found that household income, household
size, education and marital status were important factors in planning
formal RL efforts taken by the government. Besides, government’s
support in incentives and awareness building programs was found to be
crucial for the success of shaping attitudes towards WEEE recycling.
Public perception is an important factor in developing an RL model. For
example, Cao et al. (2016) estimated the generation of WEEE, as well as
public perception and opinion on WEEE management, via material flow
analysis (MFA). In regions where WEEE-related data are incomplete,
conducting a survey was found to be essential to overcome the limita-
tion. The researchers employed a public survey of 1215 respondents to
model an RSC for Zhejiang Province in China. They found that in the
province people are more inclined to recycle their WEEE items through
informal WEEE recyclers.
Recycling was previously analyzed from manufacturers’ and sup-
pliers’ point of view, however Gonul Kochan et al. (2016) reported that
the customer perspective in recycling was analyzed for the first time in
their research that implies a holistic approach to develop a RL model.
To assess recycling behavior in line with the Theory of Reasoned Action
(TRA), the authors surveyed 327 university students. Structural-equa-
tion modeling was utilized for analysis and they found that attitudes
and moral norms act as driving forces in WEEE recycling. Perceived
convenience was also considered as an important factor that creates
more involvement in the process.
Dixit and Badgaiyan (2016) found that perceived behavioral con-
trol, subjective norms, moral norms and willingness to sacrifice unused
items act as antecedents to the return behavior of customers returning
their waste mobile phones. For analysis, the authors constructed a
structural-equation model where the Theory of Planned Behavior (TPB)
was implemented. The authors urged that government and non-gov-
ernment organizations (NGOs) have a great impact in changing the
social views and attitudes of customers, which may have a positive
impact on WEEE RL processes. Disposal behavior can positively influ-
ence the increased level of return, which can be capitalized on by RL
managers in acquiring more WEEE from customers.
Demajorovic et al. (2016) conducted an exploratory qualitative re-
search to identify major challenges and barriers to implementing an RL
model for computers and mobile phones in Brazil. The technological
gap in recycling industries, continental dimensions (as a developing
country), taxation challenges and conflicts between waste picker or-
ganizations and the industry were found as the major challenges in
developing a sustainable RL system. Dixit and Vaish (2013) examined
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68
the impact of demographic variables, namely age, gender, income and
place of residence, on post-consumption disposal choices of urban In-
dian consumers for their mobile wastes in identifying the antecedents of
consumer behavior that act to develop an effective RL system.
Hanafi et al. (2013) identified three performance indicators of a
waste mobile-phone collection pilot project in Indonesia, namely par-
ticipation rate, return rate, and cost. The authors found that, even
though a formal recycling channel was created in the city of Jakarta,
customers still felt reluctant to participate in the program because of
the high presence of informal-sector WEEE recycling. The performance
of the WEEE project in the developing country’s context can be boosted
through increasing publicity and building partnerships among elec-
tronic retailer, government and telecommunication companies.
Agarwal et al. (2012) studied the customer return behavior of WEEE
items at different financial incentive levels and attempted to in-
corporate the latest practices into their research. Initial data collection
was done by a sample survey. By developing an optimization model
using particle-swarm optimization (PSO) algorithms and the simulation
package ARENA, they identified that product and component reliability
were critical in developing a customer incentives policy.
Based on survey questionnaires for Taiwan’s electronics industry
Chiou et al. (2012), identified factors of RL implementation – environ-
mental regulations and directives, consumer’s environmental aware-
ness, competition among stakeholders. The factors were ranked using
the FAHP method with a focus on environmental management. Kissling
et al. (2012) illustrated a definition of a typological operating model of
reuse focusing on two WEEE items, namely Information and Commu-
nication Technologies (ICT) equipment and whitegoods, considering
four dimensions of reuse structure: supply chain, offer, customers and
finance. The authors developed the model to understand the complex
structure and dynamics of the reuse sector in Latin America, Africa,
North America and Europe, thus providing a concise description of
reuse activities and outcomes in the continents.
Lee et al. (2007) investigated the perception gap of RL service
quality for the mobile-phone industry in Taiwan using a PZB model,
which generally identifies the gaps between the service-quality ex-
pectation of customers and an organization’s performance on service
quality. Accurate pricing, motivation towards high recycling, free-of-
charge product upgrading within warranty period, convenient location
for product return and exchange, free repairing, and finally post-repair
notice were found to be crucial for a mobile-phone RL service model.
Hung Lau and Wang (2009) investigated whether the Chinese electro-
nics industry is performing RL activities according to the current RL
theories and models, mainly with the focus of promotion of corporate
image, fulfillment of obligation for environment protection, and im-
provement of customer service. The authors found that low public
awareness on environmental protection, underdevelopment of re-
cycling technologies and lack of enforceable regulations were the cri-
tical barriers for RL implementation.
Queiruga et al. (2008) evaluated the appropriateness of the WEEE
recycling sites in Spain using a discrete MCDM method- PROMETHEE
(Preference Ranking Organization METHod for Enrichment Evalua-
tions). The factors that were considered for selecting plant site locations
were economic objectives (e.g. land cost, personnel costs, energy price),
Infrastructural objectives (e.g. facility access, agglomeration effects,
proximity to inhabited areas, absence of other WEEE recycling plants
and availability of labor) and legal objectives (e.g. availability of a local
waste-processing program and environmental grant). Autry et al.
(2001) investigated RL performance and satisfaction from a catalog
retailing perspective which were influenced by sales volume, firm size,
customers’ satisfaction and disposition. The performance had an impact
on the sales volume, while industry effects (e.g. market structure) sig-
nificantly impacted satisfaction. On the other hand, the location of the
responsibility for disposition had no significant impact on performance
and satisfaction.
4. Analysis of research gap and future research directions
Several issues were identified as potential research avenues for the
future. The above description and detailed analysis of the articles cre-
ated a comprehensive knowledge base on the overall RL/CLSC of WEEE
sector. After careful consideration, research gaps were identified and
future research directions are given as below:
• Even though both RL and CLSC research focusing on WEEE is in-
creasing over the years, there is a lack in progress of the CLSC
network design. A more integrated approach, considering both the
FL and RL of WEEE is required. Although a few studies were con-
ducted in the CLSC area, most of them were based on a generic
framework, and often the authors of the articles urged for more
empirical research based on real-world scenarios.
• Among the main research fields in the studies, the designing and
planning of reverse distribution is the most researched topic, as it
contains some of the critical topics such as open-loop and closed-
loop network design. Future researchers should consider conducting
qualitative research in the field. Qualitative, especially survey-
based, research provides an in-depth understanding of the practical
problems that lead to theory development (Govindan et al., 2015).
In addition, there is a serious lack of specifying the source of WEEE
generation, which should be included in designing the RL network.
Generally, WEEE generation is characterized by three types of
sources – households, government organizations and institutions,
and the business sector. Source specific WEEE RL network design
could provide valuable policy implications for responsible autho-
rities managing their RL network with better economic and en-
vironmental performance. This was also evident from Fig. 11 when
considering source as one of the uncertainties in network designing.
• In the OLND, recycling is the most important disposition considered
by the articles, however, there is a scope for future researchers to
consider recycling in CLSC networks (using coordinated approach
with other firms using secondary raw materials), where economic
efficiency, environmental cost and environmental impacts need to
be included in the objective functions of RL modeling. Furthermore,
there is a need for investigation of other alternatives – reuse and
repair in the network. No single research was found that considered
recycling, remanufacturing, reuse and repair in an integrated
manner. On the other hand, MILP was the most utilized modeling
approach, with alternatively stochastic and fuzzy programming
approaches. However, in future, when MILP/MIP is being utilized,
strategic management, environment legislation, customer service,
and asset management can be included as modeling objectives for
RL network design. In real-world scenarios, a number of complex
and uncertain variables may arise in computation. When the
number of variables and constraints increases in modeling, meta-
heuristic algorithms like GA or heuristic integer programming, for
instance a scatter search, can be implemented (Amin and Zhang,
2012). In collaborative planning, application of GP in CLSC network
modeling could be an interesting research (Gupta and Evans, 2009).
In addition, for better computational performance in algorithm-
based research, heuristic, meta-heuristic, approximations and a
sampling-based solution approach can be employed for a large
number of scenario-based problems. EOL product management from
the RL/CLSC perspective is scarce in the literature. In developing
models, decomposition and heuristic approaches can be im-
plemented for this particular field. To improve the reusability and
recyclability of WEEE, the eco-design concept has the potential to
integrate into RL/CLSC network design. Additionally, simulation-
based collection processes in an RL network should be considered
for research in the area of DPRD studies.
• There is a clear deficit of implementing a multi-level and/or multi-
objective and/or multi-period modeling approach in RL networks.
Tactical objectives such as return forecasting, product return
M.T. Islam, N. Huda Resources, Conservation & Recycling 137 (2018) 48–75
69
handling and aggregate production planning; and operational level
objectives, for instance vehicle planning and scheduling, optimal
disassembly sequences of remanufacturing processes, should be in-
cluded more in modeling open-loop RL networks. Multi-period
nondeterministic modeling in WEEE product recovery networks
needs further investigation. Likewise, inventory management of
CLSC networks along with strategic safety stocks of RL considering
remanufacturing in particular, is another research direction. Multi-
objective programming considering risks and resource savings
should be included in RL network modeling.
• In terms of uncertainty, the cost of remanufacturing/recycling, the
price of remanufactured product, revenue, volume of return
(quantity), time of returns, quality, capacity of facilities (e.g. treat-
ment, recycling, remanufacturing), WEEE generation rate (location
specific), the market need/demand for recycled products should be
introduced in RL network models. In particular, the product return
rate in multi-period CLSC networks with an interaction of demand
could be an interesting topic of research in future. In addition, the
price of remanufactured product based on market demand is an-
other research area in WEEE CLSC network design. Considering
demand as probabilistic function can be included in modeling.
Sensitivity analysis can be included in studies that deal with rela-
tively low-volume products in terms of return (e.g. products with
longer lifecycle). For products with a shorter lifecycle, fuzzy-set
theories can be implemented. Uncertainty in the WEEE recycling
network such as quantity in conjunction with transportation cost is a
potential area of research. In addition, during development of RL
network infrastructure, strategic planning tools, such as balanced
scorecard, and simulation tools can be implemented when such
uncertainties arise. Environmental influence and supplier selection
are two less considered uncertainty parameters (shown in Fig. 11) in
network modeling that could be an interesting topic for future re-
search.
• From Fig. 10, it is clear that sustainability dimensions – social,
economic and environmental – were considered by very few articles
(only 3 papers), whereas economic issues (e.g. cost, price, revenue
etc.) were the most prominent dimensions (considered by 75% of
the papers). In further research, WEEE RL/CLSC network design
should introduce all the three dimensions, in particular the social
impact to understand the overall intrinsic benefits of the network in
a particular region and/or country.
• It will be interesting if another new dimension – technology under
the sustainability context – is considered. This could provide a more
holistic insight of the RL/CLSC system itself as well as achieving an
overall goal of sustainable development. From technical standpoint,
specifically, the impact of RFID and ICT-based network support
systems implementing the concept of internet of things (IoT), for
inventory management and product-recovery information manage-
ment system development, could be a new area of research in this
context. This might provide better information flow among all ac-
tors. In all supply chain network of E-waste management, IoT has a
crucial role in resource savings at low cost (Nobre and Tavares,
2017). As social sustainability saw less research, new parameters
under this criteria, such as public health and safety, can be included
in developing the RL network model using a game-theory method
where the preferences and participation of customers and govern-
ment as actors can be included in models. Another important per-
spective that needs further research is customer participation in
determining recycling fees and quantity generation in an RL system,
creating a competitive EEE market.
• Further research should be carried out in the area of 3PRLP selection
and VRP. In the first area, large-scale empirical studies with multi-
WEEE product scenarios should be initiated. On the other hand, in
3PRLP studies, reverse channel choice by small and large companies
according to profits and cost were the highest priority in the past.
However, there is a lack of study in developing a comprehensive
framework under which several RL processes such as product ac-
quisition, repair, reuse and remanufacturing need to be performed
by 3PRLP. Furthermore, the impact of legislative initiatives on the
performance of 3PRLP considering all sustainability dimensions
needs further investigation. In addition, the negative impact of
3PRLP inclusion by OEMs and the interaction of small companies in
a sustainable CLSC system should be investigated, rather than only
RL operation. The collaboration between small and large companies
in RL management, in other words outsourcing, should be a future
research topic. As limited research was conducted in vehicle
routing, one of the research directions could be to observe the im-
pact of disassembly systems in vehicle routing. The environmental
performance of vehicle routing, for instance reduction of CO2
emission with distances during transportation and collection, was a
less-researched topic. Classical vehicle routing problems can use
Tabu search and scatter search with sensitivity analysis for holistic
analysis of a specific problem. Routing design is often concerned
with the length and number of tours, and can be solved by im-
plementing GRASP and MIP or even a global information system
(GIS) system.
• In the category of decision making and performance evaluation, the
product lifecycle perspective received less attention among the re-
searchers. RL processes such as disassembly and inspection demand
environmentally and economically optimum product design, by
which both time and cost in the overall RL system could be saved
and/or minimized. As seen earlier, most of the articles were con-
cerned with the economic aspects of the RL and CLSC of WEEE.
However, when considering environmental aspects, there is a need
to consider the use of two specific modeling techniques: LCA and
MFA. A limited number of papers considered these approaches, and
future researchers should consider them. From the circular-economy
and efficient-resource-utilization perspectives, which top manage-
ment of recycling and remanufacturing firms struggle to consider,
using these tools (LCA and MFA) could tremendously assist in
minimizing the total cost and maximizing the environmental per-
formance of the RL and CLSC process. These tools are also able to
provide valuable information on the available critical raw materials
that can be recovered, and potential mitigation of greenhouse gas/
CO2 emission (as a measure of environmental performance) for ef-
ficient and effective RL operations. Moreover, the impact of un-
certainty parameters, such as the capacity level in facilities, cost and
collection rate, on the lifecycle performance in a CLSC environment
could be the most promising research direction for the future.
Another interesting future research topic could be implementing
game theory to investigate interactions in decision making among
different players within the RL or CLSC for WEEE.
• In the conceptual-framework based studies, relatively less attention
was given to RL processes – reuse and repair. The impact of these
two alternatives on overall RL management organized by manu-
facturers could be interesting future research direction. For the case
of recycling, there is a need for open-sourced online-based market
information system that can determine the recycling fees of a pro-
duct in an RL system where WEEE would be collected by OEMs or by
recycling firms. In addition, research could be considering the im-
pact of regulatory instruments such as EPR, with the interaction of
the formal and informal sectors on WEEE collection and recycling.
• Disposal rate (frequency), type of WEEE items disposed, average
lifetime of disposed product, storage time, customer awareness,
willingness to pay (WTP), top-management attitude (from com-
pany’s perspective) are some of the critical issues that need to be
addressed at a regional level to develop sustainable RL/CLSC sys-
tems. In such a context, qualitative studies could be a successful
research methodology which needs further implementation.
• There is a lack of product/case-specific WEEE RL network modeling
initiatives among the studies. Future research should consider more
product-oriented studies such as for waste batteries, IT equipment
M.T. Islam, N. Huda Resources, Conservation & Recycling 137 (2018) 48–75
70
(e.g. laptop computers, printers, cell phones, telephones, personal
digital assistant products (PDAs), ipads, and tablets), small con-
sumer electronics (e.g. portable music-players, toys) and white-
goods. Computer waste recycling, reuse and remanufacturing (in an
integrated manner) based RL/CLSC networks should have particular
attention.
• Future researchers should envision utilizing the concept of circular
economy in developing infrastructure and formulating sustainable
RL/CLSC activities at national level. Current body of literature often
fails to collaborate these issues. As the WEEE generation is growing
exponentially to almost every country in the world, the integrated
understanding of sustainability, circular economy and CLSC from
supply-chain management perspective is an important research
avenue to explore.
• There are also potential ways of improving this review article itself.
Taking a higher number of research articles from grey sources, such
as company reports, annual reports, white papers and online sources
could enrich the content. Another improvement could be categor-
ization of contents by geographical locations, qualitative vs. quan-
titative approaches and different modeling approaches and solution
methodologies.
5. Conclusion
This paper presents a comprehensive literature review of recent
papers published at different scientific journals in RL/CLSC issues that
considered WEEE or E-waste as an EOL product. A total of 157 papers
published in the international peer-reviewed journals, conference pro-
ceedings, and book chapters during the period 1999-2017 are selected,
categorized, analyzed and reviewed. After reviewing, several research
gaps were identified with important implications for future research.
The authors think that this review provides a holistic overview of the
whole system perspective on the research field, and identifying key
future research directions would be useful for researchers. The cate-
gorizations and citied references may be utilized as a broad frame of
references to advance concepts and models for the future research.
Empirical research focusing on CLSC network design considering
real-world scenarios is a suggested future research opportunity. To
understand the dynamics of source specific (i.e. households, organiza-
tions and businesses) WEEE generation and its management, qualita-
tive, especially survey-based research is recommended. All the dis-
position alternatives (i.e. recycling, remanufacturing, reuse and repair)
should be considered in an integrated manner in designing CLSC net-
work for future research. In RL and CLSC network designing, lack of
studies considering different modeling objectives, problem formulation
and solution approaches are discussed and future opportunities are
advised. Scope of utilizing multi-objective programming considering
different uncertainty parameters is highlighted and prescribed for fur-
ther research. In future RL and CLSC network modeling, attentions
should be given to specific WEEE items (IT equipment, small consumer
electronics etc.), environmentally friendly-3PRLP selection, technology
integrated network support system (by extensive implementation of
IoT) and application of LCA and MFA tools for attaining highest eco-
nomic and environmental performance. In holistic E-waste RL and CLSC
system development, integration of sustainability and circular economy
concepts is the broad future research area that will ensure sustainable
waste management, resource conservation, material recovery and mi-
tigation of environmental impact.
Acknowledgement
The authors would like to thank Dr Keith Imrie for proof-reading
this paper. The authors like to thank anonymous reviewers for their
constructive and valuable comments for improving the manuscript. The
first author acknowledges the financial support from Macquarie
University under the scholarship scheme “International Macquarie
University Research Training Program (iMQRTP)” for conducting this
research.
Appendix A
List of AbbreviationsANPAnalytic network processBSCBalanced
ScorecardCLSCClosed-loop supply chainCRMCritical raw materials-
CLNDClosed-loop network designCCsCollection centersCLDSCClosed-
loop distribution supply chainCDMClean Development Mechanism-
DPRLDesigning and planning of reverse distributionDEAData
Envelopment AnalysisDfEDesign-for-the-environmentDSSDecision sup-
port systemEOLEnd-of-lifeEPRExtended producer responsibilityE-
wasteElectronic wasteECAElection campaign algorithmEPCElectronic
Product CodeFLForward logisticsFSCForward supply chainFAHPFuzzy
analytic hierarchy processGAGenetic algorithmGAMSGeneral Algebraic
Modeling SystemGRASPGreedy Randomized Adaptive Searching
ProcedureGPGoal-programmingGRSCGlobal reverse supply
chainILPInteger linear programmingIPInteger programming-
ITInformation technologyICTInformation and Communication
TechnologiesLPLinear programmingLINMAPLinear programming tech-
nique for multi-dimensional analysis of preferenceLCALifecycle
assessmentLCCsLifecycle costsLCECLifecycle energy consumption-
MILPMixed-integer linear programmingMCDMMulti-Criteria Decision
MakingMIPMixed integer programmingMINLPMixed integer non-
linear programmingMEUMaximum expected utilityprogramming-
MICMACMatriced’ Impacts Croise’s Multiplication Appliquée a UN
ClassementNSGANon-dominated Sorting Genetic AlgorithmNL
PANon-linear programming algorithmOECDOrganisation for Economic
Co-operation and DevelopmentOLNDOpen-loop network designOEMs-
Original equipment manufacturersPLCProduct lifecyclePSO
Particle swarm optimizationRLReverse logisticsRSCReverse
supply chainRPRecycling plantRCsRecycling centersRNRecovery
networkRLNDReverse logistics network designRNARecovery network
arrangementRCPSPResource constrained project scheduling
problemRFIDRadio-frequency identificationSCsSorting centersSAA-
Sample average approximationSDPStochastic dynamic programm
ing3PRLPThird-party reverse logistics providerTRATheory of Reasoned
ActionTSsTransfer stationsTFsTreatment facilitiesTOPSISThe
Technique for Order of Preference by Similarity to Ideal SolutionTFT-
LCDThin-film-transistor liquid- crystal displayVRVehicle routing-
VRPVehicle routing problemVWVirtual warehousingWEEEWaste elec-
trical and electronic equipmentZOGPZero-one goal programming
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Introduction
Research methodology
Material collection
Descriptive analysis
Category selection
Material evaluation
In-depth analyses of the literature
Analyzing papers on DPRD
Open-loop network design (OLND)
Location-allocation problem
Product recovery (PR)
Cost
Secondary market
After-sales service
Closed-loop network design (CLND)
Location-allocation problem
Cost
Analyzing third-party reverse-logistics provider (3PRLP) selection
Vehicle routing problem (VRP)
Analyzing the decision-making and performance-evaluation studies
RL/CLSC process perspectives
Organizational and business perspectives
Product lifecycle perspective
Analyzing conceptual framework studies
RL/CLSC system and/or process focused studies
Remanufacturing-focused
Recycling-focused
Organizational perspective
Formal and informal sector
Product return
Global reverse supply chain and climate change
Analyzing the qualitative studies
Analysis of research gap and future research directions
Conclusion
Acknowledgement
Appendix A
References
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Reverse logistics in e-business: Optimal price and return policy
Mukhopadhyay, Samar K;Setoputro, Robert
International Journal of Physical Distribution & Logistics Management; 2004; 34, 1/2; ProQuest
pg. 70
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