Six Sigma

Green Belt Project – The Define Phase

Complete a Project Charter with all of the required Information.
Please write the Problem Statement:
Please write the Goal Statement utilizing S.M.A.R.T. objectives (Specific, Measureable, Attainable, Relevant and Time Bound):
What is in Scope? What is out of Scope?
Who are Key Stakeholders?
What are key Milestones?
Please complete a High Level “As Is” Process Map.
Please create a SIPOC of the process based on the information that you know. Feel free to use your imagination for this.
Describe methods for collecting Voice of the Customer. (SEE APPENDIX A for VOC).
Please create an Affinity Diagram or List based on VOC so you can identify Customer “NEEDS” for CTQ Tree.
Please create a Critical to Quality Tree utilizing the Voice of the Customer. Identify the Needs, Drivers and Requirements or Metric to needed to meet these needs.

>DM

A I C

oadmap Lean

ix Sigma DMAIC Roadmap Purpo

e Key Tool

s Key

Outputs Define To establish a quantified problem statement, objective and business case that will become the foundation to your Six Sigma project. Conduct stakeholder analysis, select team members and kick

– of

f your project. Primary Metric Process

Map Project

Charter Project Plan

* Process Map
* Gather

VOC

* Translate VOC to

CTQ

‘s
* QFD/HOQ
* COPQ
* Primary & Secondary Metrics
* Establish Project Charter
* Stakeholder Analysis
* Team Selection
* Project Plan

Measure Refine your understanding of the process. Assess process capability relative to customer specifications. Validate measurement systems. Brainstorm potential x’s. C&E

SIPOC

FMEA Cpk

* Early

=f(x) Hypothesis
*

Detailed Process Map

* SIPOC
*

Cause

Effect

Diagram
*

Cause & Effect Matrix

* FMEA
* Basic Statistics
*

Normality Test

Capability Analysis

* Gage R&R

Analyze Conduct data collection and planned studies in order to eliminate non-critical x’s and validate critical x’s. Establish a stronger and quantified Y=f(x) equation.

Normality Test

VA 2 Sample t-test Equal Variances * Narrowed Y=f(x)
*

& 2 Sample t-tests
* 1 & 2 Proportions tests
* Equal variance tests
* Normality tests
*

ANOVA

* Moods Median
* Mann Whitney
* Paired t-test
*

Chi-Square

d test Improve Design, test and implement your new process or product under live operating conditions. Pilot solutions if feasible before broadly deploying expensive improvements or products. Pugh Matrix Linear

Regression Binary Logistic Regression DOE * Refined Y=f(x)
* Pugh Matrix
* Correlation
*

Simple Linear Regression

* Multiple Linear Regression
* Binary Logistic Regression
* Full Factorial DOE
* Fractional Factorial DOE Control Plan, communicate, train and implement your product or process solutions. Ensure control mechanisms are established. Use Poke Yoke, visual controls,

SOP’s

and

SPC

wherever possible. Control Plan

SOP’s

Communication Plan

SPC

* Control Plan
*

Training Plan

* Refined FMEA
* Communication Plan
* Standard Operating Procedures
* Five-S Audit
* Poke Yoke
* Visual Controls
* Statistical Process Control

VILLANOVA UNIVERSITY

DMAIC_Project_Checklist

D.M.A.I.C Project Checklist DEFINE IMPROVE 2

Projecct Charter

Potential Solutions Developed 2

Business Case

(why is this project important)

Potential Solutions Prioritized 3

Problem

Statement & Objective 2

Solution Selected 2

Baseline Data

(Primary Metric “Y”)

Improvement Pilot/Test Plan 2

Target

Improvement Pilot/Test Execution 2

COPQ Estimate

Improvement Verified 2

Project Team

New Process Capability 2

Project Scope

Updated Process Map 2

Project Timeline

Solution Implementation Plan 2

Project Constraints/Dependencies 2

Primary Metric Updated 2

High

Level Process Map 2

COPQ Revision 2

Customer Requirements Identified

Improve Phase Report 2

Define Phase Report MEASURE CONTROL 2 Detailed Process Map 2

Full Solution Implementation 2 SIPOC 2

Standard Operating Procedures Developed 3

Data Collection Plan

(Potential

‘s)

Communication Plan
2

Measurement Systems Analysis (Primary Y)

Training Plan
2

Process Capability Analysis

Audit Plan 2

List of Possible X’s

Control Charts 2

Prioritized List of X’s to be Analyzed

Control Plan
2 Primary Metric Updated 2 Primary Metric Updated
2 COPQ Revision 2 COPQ Revision
2

Measure Phase Report

Full Project Report ANALY

E 2

Sources of Variation Identified 2

Potential X’s

Eliminate

d 2

Root Causes Confirmed (Critical X’s Identified)

2 Primary Metric Updated
2 COPQ Revision

Analyze Phase Report

VILLANOVA UNIVERSITY

Lean Six Sigma Toolkit Table of Contents Project Charter

Six Sigma Process Map

SIPOC
VOC

Affinity Diagram

CTQ
PARETO CHART TEMPLATE

Data Collection Sheet

DPMO
Calculator

DPMO Data STEM AN

LEAF

Solution Selection Matrix

A3

Counter Measures Failure Modes and Effects Analysis

Severity

Definition

Occurrence

Definition

Detection

Definition

Score
card

Gantt Chart

Instructions Gantt Chart

Control Plan

RACI Cause and Effect Diagram

Communication Plan
Training Plan

Audit Checklist Pugh Matrix
C and E Matrix Risk Management Plan Sample Size

Calculator

Takt Time Calculator

Z Distribution Table T Distribution Table F Distribution Table Chi Square Distribution Table PROCESS

MAP TEMPLATE TREE DIAGRAM TEMPLATE

CORRELATION COEFFICIENT

HISTOGRAM (use EXCEL to Create BINs & HISTOGRAMS) USE EXCEL

HELP for instructions on creating BINS and HISTOGRAMS based on version of EXCEL you are using. SEE SSGB

12 0

TEXTBOOK Pp/Ppk SEE SSGB1

TEXTBOOK for Instruction

& formula STEM AND LEAF SEE SSGB

120

TEXTBOOK for Instruction
MEASURES

OF CENTRAL TENDENCY USE EXCEL HELP FOR FORMULA AND INSTRUCTIONS MEASURES OF DISPERSION

USE EXCEL HELP FOR FORMULA AND INSTRUCTIONS
PAIRED

t TEST USE EXCEL HELP FOR FORMULA AND INSTRUCTIONS OR USE SSGB120 TEXTBOOK t TEST USE EXCEL HELP FOR FORMULA AND INSTRUCTIONS OR USE SSGB120 TEXTBOOK
SCATTER PLOTT

USE EXCEL

Project_Charter

/Mentor

Business Case

Project Scope

Name Organization

Organization

Project Title:

GO HOME!!

Black Belt

Project Champion

Executive

Sponsor

Problem Statement

Project

Goal

Milestones

Constraints & Dependencies

Project Risks

Any additional information

Approval/Steering Committee

Stakeholders & Advisors

Project Team & SME’s

Name

Organization

Name

VILLANOVA UNIVERSITY

Six Sigma Process Map

SIX SIGMA

PROCESS MAP TEMPLATE

GO HOME!!

PROCESS

ANALYSIS COMPLETED BY DEPARTMENT(S) DATE

COMPLETED K E Y COPY AND PASTE
BLANK ICONS
BELOW LEARN MORE ABOUT SMARTSHEET FOR PROJECT MANAGEMENT

STEP

START / END

INPUT / OUTPUT

DOCUMENT

FLOWCHART LINK

CONNECTORS

https://goo.gl/wZizs0

SIPOC

S.I.P.O.C. Template

GO HOME!!

Suppliers Inputs

Process Outputs

Customers

VILLANOVA UNIVERSITY

Start

Step 1

Step 2

Step 3

Step

4 End

Voice of Customer Six Sigma

GO HOME!!

(S)

is the customer?

1
2
3
4
12

LEARN MORE ABOUT SMARTSHEET FOR PROJECT MANAGEMENT

VOICE OF CUSTOMER (VOC) SIX SIGMA TEMPLATE
	ID	CUSTOMER IDENTITY	VOICE OF THE CUSTOMER	KEY CUSTOMER	ISSUE	CRITICAL CUSTOMER REQUIREMENT
				#		Who	What did the customer say?	What does the customer need?	What resulting action is required?
																	5
														6
																7
															8
													9
												10
										11
											13
									14

https://goo.gl/p

jL8

Affinity Diagram

GO HOME!!

Affinity Diagram –

Dr Deasley

Staffing Issues

High Turnover

Frustrated Nurses

Data Collection Plan Template

DATA

COLLECTION PLAN TEMPLATE GO HOME!!

PROJECT NAME

DATE

PREPARED BY ID

PERFORMANCE MEASURE OPERATIONAL DEFINITION DATA SOURCE & LOCATION SAMPLE SIZE WHO WILL COLLECT DATA? WHEN WILL DATA BE COLLECTED? HOW WILL DATA BE COLLECTED? HOW WILL DATA BE USED? ADDITIONAL DATA TO BE COLLECTED AT SAME

TIME #

1
2
3
4
5
6
7
8
9
10
11
12
LEARN MORE ABOUT SMARTSHEET FOR PROJECT MANAGEMENT

https://goo.gl/qdc7cy

Stem and Leaf

GO HOME!!

Dr Deasley

9
8
7
6
5
4
3
2
1

Is it reasonably norm

y distributed?

, what shape is it? (Use the correct name for this shape, not a description.)

Project:

Deliverable:

Stem & Leaf

Student last name:

Your last name here

all

If it is

not normal

Based on this analysis, what is the next thing you would do?

Solution Selection Matrix

Solution Selection Matrix GO HOME!!
Project Goal

(less good)

(best)

1 2 3 4 5

)

Cost to Implement
(1 = $$
& 5 = $)

/No

10 9 8 7 5

5 3 2 4 5

Yes

5 4 4 3 1

Yes

5 4 4 3 1 144 Yes

5 5 2 3 1

Yes

5 4 5 5 5

Yes

2 3 2 3 1

Yes

4 4 3 3 2

Yes

3 3 5 3 5

Yes

Please rank each solution for each criteria
by using the 1-5 Scale as indicated below

Increase IISE SDD Membership Engagement by 1

Very

Low

Moderate

Very High

Potential Solution
(Provide Brief

Description

Potential to Meet Goal

Positive Customer

Impact

Stakeholder Buy-in

Time to Implement
(1 = Long
5 = Quick)

Total Score

Implement?

Yes

Weighted Criteria

IISE Sustainable Development Division Membership Engagement

Coffee talks with Lean topics

More interactive sessions, instead of standard panel discussions

Board meetings, problem solving discussion groups

Tracks for problem solving – interactive session less directive

Could we utilize the app to gain feedback?

IISE Connect?

Discussions with TVP’s and Track Chairs

Can we do this outside of the conference?

Survey – VOC

143

&”Arial,Bold”&10Solution Selection Matrix &”Arial,Regular”&8v

1.0

&”Arial,Regular”&8&G_x000D_Copyright 20

GoLeanSixSigma.com.

All

Rights Reserved.

GO HOME!!

Project XYZ

Plan

Action

5 12

2 9

6 13 20 27 6 13 20 27 3 10 17

1 8

5 12 19 26 3 10 17 24 31 7 14

4 11 18

2 9 16 23 30 6 13 20 27

0%
0%
0%
0%
0%
0%
0%
0%
0%

Project XYZ

Location

Date:

Project Lead

Tina Agustiady

Team Members

Strategic Project

Critical Project

Issue Resolution

1. Project Goal

Action

Owner

Due Date

2017 – Week Beginning

Dec

Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

2. Project/Problem Analysis (Project: Objectives; Problem: Root Cause, Barriers, Roadblocks)

Out of scope items:

4. Results (Impact on Targets to Improve)

For each line item determine % completion

Element

Item

% complete sitewide

Comment

5. Unresolved Issues – Risks:

Legend

Planned Timeline to Complete Action

Planned Due Date

Planned Action “ON TARGET”

Planned Action “OFF TARGET”

Planned Action “Past Due”

Planned Action Complete

CounterMeasureTemplate

Impact

Target

GO HOME!!

Jan

2 Feb
Mar
3 Apr
4

5 Aug
Sep
6 Oct
Nov
7 Dec

(Date Complete)

(P,D,C,A)

0.00%

Countermeasure for Project

Data Table

Plant:

Reasons
(Root Cause Short Description. This MUST come from a root cause analysis tool)

Month

Enter KPI

Savings Target

Gap Closure Target

Actual

Better

Worse

Date of Review:

Enter Date of Review

Start Month:

Enter 1st Month Counter Measure Form Is Used

May

June

July

Problem Statement:

Enter Problem Statement

Overall Impact (Note: Should Exceed “Gap to Close”)

0.0

Reasons
(Enter Reason Being Addressed from Above)

What
(Describe actions being taken to address this Root Cause)

Who
(Resp for action and impact)

When

Impact
(Target Benefit by the Complete Date)

Status

Planned Impact Improvement (Note: This must equal or exceed the gap closure target)

Enter Title

Better Jan Feb Mar Apr May June July Aug Sep Oct Nov Dec 0 0 0 0 0 0 0 0 0 0 0 0 Worse Jan Feb Mar Apr May June July Aug Sep Oct Nov Dec 0 0 0 0 0 0 0 0 0 0 0 0 Target Jan Feb Mar Apr May June July Aug Sep Oct Nov Dec Actual

0 0 0 0 0 0 0 0 0 0 0 0 Gap Closure

PFMEA

Process Controls

Person & Target Date

SEV OCC DET RPN GO HOME!!

1 0
2 0
3 0
4 0
5 0
6 0
7 0
8 0
9 0
10 0
11 0
12 0
13 0
14 0
15 0
16 0
17 0
18 0
19 0
20 0
21 0
22 0
23 0
24 0
25 0
26 0
27 0
28 0
29 0
30 0

30 0

31 0

Process Function (Step)

Potential Failure Modes (process defects)

Potential Failure Effects (KPOVs)

SEV

Class

Potential Causes of Failure (KPIVs)

OCC

Current

DET

RPN

Recommend Actions

Responsible

Taken Actions

Severity

Effect

GO HOME!!

Very High

of product may have to be scrapped. Vehicle / item inoperable, loss of primary function. Customer very dissatisfied.

High

disruption to production line. Product may have to be sorted and a portion (less than 100%) scrapped. Vehicle operable, but at a reduced level of performance. Customer dissatisfied.

Moderate

Low

Minor

Criteria: Severity of Effect Defined			Ranking
Hazardous: Without Warning	May endanger operator. Failure mode affects safe vehicle operation and / or involves noncompliance with government regulation. Failure will occur WITHOUT warning.
Hazardous: With Warning	May endanger operator. Failure mode affects safe vehicle operation and / or involves noncompliance with government regulation. Failure will occur WITH warning.
Major disruption to production line.		100
	Minor
Minor disruption to production line. A portion (less than 100%) may have to be scrapped (no sorting). Vehicle / item operable, but some comfort / convenience item(s) inoperable. Customers experience discomfort.
Minor disruption to production line. 100% of product may have to be reworked. Vehicle / item operable, but some comfort / convenience item(s) operable at reduced level of performance. Customer experiences some dissatisfaction.
	Very Low	Minor disruption to production line. The product may have to be sorted and a portion (less than 100%) reworked. Fit / finish / squeak / rattle item does not conform. Defect noticed by most customers.
Minor disruption to production line. A portion (less than 100%) of the product may have to be reworked on-line but out-of-station. Fit / finish / squeak / rattle item does not conform. Defect noticed by average customers.
Very Minor	Minor disruption to production line. A portion (less than 100%) of the product may have to be reworked on-line but in-station. Fit / finish / squeak / rattle item does not conform. Defect noticed by discriminating customers.
None	No effect.

Occurrence

Cpk Ranking GO HOME!!

,000

: Failure is unlikely. No failures ever associated with almost identical processes

Probability of Failure	Possible Failure Rates
Very High:	³ 1 in 2	<	0.3
Failure is almost inevitable	1 in 3	³ 0.33
High: Generally associated with processes similar to previous	1 in 8	³		0.5
processes that have often failed	1 in 20	³	0.6
Moderate: Generally associated with processes similar to	1 in			80	³	0.8
previous processes which have	1 in					40	³ 1.00
experienced occasional failures, but not in major proportions	1 in 2,000	³		1.1
Low: Isolated failures associated with similar processes	1 in 15,000	³		1.3
Very Low: Only isolated failures associated with almost identical processes	1 in 1							50	³	1.5
	Remote	£ 1 in 1,500,000	³		1.6

Detection

Ranking GO HOME!!

Remote

% of failures

Very Low

Low

% of failures

Moderate

% of failures

High

% of failures

Very High

Criteria: Liklihood the existence of a defect will be detected by test content before product advances to next or subsequent process
Almost Impossible	Test content detects < 80 % of failures
Very Remote	Test content must detect 80 % of failures
Test content must detect 8		2.5
Test content must detect 85 % of failures
Test content must detect 8		7.5
Test content must detect	90
Moderately High	Test content must detect 92.5 % of failures
Test content must detect	95
Test content must detect 97.5 % of failures
Almost Certain	Test content must detect 99.5 % of failures

Scorecard

GO HOME!!

Status

Current

Goal

Actual Goal Fcst Actual Goal Fcst Actual Goal Fcst Actual Goal Fcst Actual

$25.0

1311

$61.0 $58.0

$61.0 $59.0 $59.0 $61.0 $61.0

ERROR:#DIV/0! ERROR:#DIV/0!

$10.0

$10.0 $10.0

$40.0

3 3

1 1

52,800 4,967

1 1

1,933 195

of Goal, Red <95% of Goal

Villanova Basic Scorecard
Calculate
Q1’15	Q2’15	Q3’15	Q4’15	Full Year 2015
FYF	Key Business Metrics					Fcst
1.16			0.9	Operating Expense Reduction	$15.0	$1	2.0	$8.0			$25.0	$29.0	$35.0	$				36	$2	4.0	$100.0	$97.0
0.	96	72									48				ERROR:#DIV/0!	Customer Satisfaction					$	61	$	58	$57.0	$59.0
Net Income
1.05	OWT				$10.0	$0.0	$1		3.1		$40.0	$					60	$		63
Operating Metrics
Recall Open Cases
Recall Open Case Dollars
Recall Cases w/Purchasing
Recall Case Dollars w/Purchasing
Legacy Open Cases
Legacy Open Case Dollars
Legacy Cases w/Purchasing
Legacy Case Dollars w/Purchasing
OWT Cumulative Parts Reviewed	31,200	3,802				52	800		4,	967
OWT Cumulative Recovery Groups w/TF	1,213	189		1,933		195
Status Rules: Current status based on forecast vs. goal for future periods and based on actual vs. goal for past period. FYF status based on full year forecast vs. Goal until the year completes.
Status Conditions: Green >=100% of Goal, Yellow 95%-	99%
$dollars represented in Millions

VILLANOVA UNIVERSITY

Gantt Chart Instructions

GO HOME!!

Project Plan Guide:
•To delete these instructions, select this text box and then hit

[

Delete].
Date Cells (H6:GU7)
These cells power much of the conditional formatting and allow the project plan to “float.” All the cells are indirectly referenced to cell G6, which can be set to a firm date (ex. 2/

1/2

010) or a reference (ex. =MIN([project dates])). Adjusting cell G6 will shift the entire calendar.
Task Cells (A10:F40)
The tasks have three levels, deliverable, task and sub task. Each has a different conditional format in the Gantt chart area.
Deliverable Sections
The deliverable section(s) (ex. 15:19) can be copied and pasted as rows to add new deliverable sections below the existing sections if needed.
Within each deliverable section you can add additional room for tasks by inserting a row above the light blue row (ex. 13). This way the appropriate conditional formatting is added and no formulas are compromised.
Task s
By entering a task in the B column the conditional formatting will make the associated bar a medium blue. By entering a task in the C column the associated bar will be light blue. The bar is shown via conditional formatting based on the dates entered (Cols D:E) cross referenced with the calendar across the top.
Task dependence/precedence can be managed by creating formulas between the data cells instead of firm dates (ex. =E11

5 vs.

2/1

0/2010).

Special Events

(B2:E7)
Functionality was added to allow for up to five “special events” that will highlight the background color. This was intended for non-task events that may need to be included.
Misc.
– There is a current date indicator that will show the current date on the Gantt chart.
– The Gantt chart bars and the task list will highlight according to % complete status.
– The Page Setup includes repeating rows of 2:9 and repeating columns of A:G. To print a small section of the chart simply select the area of the Gantt chart (ex. H10:AZ

) and set it as the Print Area.
– Hypothetically additional days can be added to the calendar by copy and inserting columns to the left of Column GU. Be sure to check that the formulas in 6:7 and 4:5 have been copied appropriately.

Gantt Chart

Special Events Start End

GO HOME!!

2/6 Dec Jan Feb Mar Apr May Jun Jul

1/2

2/1 2/2 2/3

2/6

2/25 2/26 2/27 2/28

3/2

4/6

Start End

1/13 1/26

1/16 1/23

1/26 3/5

1/28 2/17

Task 1 1/28 2/17

1/29 2/11

2/11 2/17 0%

2/17 3/15

Task 1 2/17 3/2 0%
Task 2 2/17 3/9 0%
Task 3 3/9 3/15 0%

5/10 4/18

Task 1 3/16 4/18 0%

3/29 4/18 0%

Task 2 5/10 5/30 0%
Task 3 5/31 6/20 0%

6/13 7/4 0%

7/5

Task 1 7/5

Task 2

8/1 0%

Task 3

Task 4

Project Plan Template

Time off

3/2

4/6

Holiday

2/6

2/2

2/25

2/26

2/27

2/28

12/29

2/3

12/31

1/1

1/3

1/4

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3/31

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4/30

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6/30

7/1

7/2

7/3

7/4

7/5

7/6

7/7

% Complete

Project Deliverable 1

Task 1

80%

Task 2

60%

Task 3

10%

Project Deliverable 2

25%

Sub task 1

40%

Sub task 2

Project Deliverable 3

Project Deliverable 4

Sub Task 1

Task 4

Project Deliverable 5

8/1

7/18

7/19

8/2

8/8

8/9

8/15

&”-,Bold”

Example

Project Plan Template

Page &P of &N

Project Plan Guide:
•To delete these instructions, select this text box and then hit [Delete].
Date Cells (H6:GU7)
These cells power much of the conditional formatting and allow the project plan to “float.” All the cells are indirectly referenced to cell G6, which can be set to a firm date (ex. 2/1/2010) or a reference (ex. =MIN([project dates])). Adjusting cell G6 will shift the entire calendar.
Task Cells (A10:F40)
The tasks have three levels, deliverable, task and sub task. Each has a different conditional format in the Gantt chart area.
Deliverable Sections
The deliverable section(s) (ex. 15:19) can be copied and pasted as rows to add new deliverable sections below the existing sections if needed.
Within each deliverable section you can add additional room for tasks by inserting a row above the light blue row (ex. 13). This way the appropriate conditional formatting is added and no formulas are compromised.
Task s
By entering a task in the B column the conditional formatting will make the associated bar a medium blue. By entering a task in the C column the associated bar will be light blue. The bar is shown via conditional formatting based on the dates entered (Cols D:E) cross referenced with the calendar across the top.
Task dependence/precedence can be managed by creating formulas between the data cells instead of firm dates (ex. =E11+5 vs. 2/10/2010).
Special Events (B2:E7)
Functionality was added to allow for up to five “special events” that will highlight the background color. This was intended for non-task events that may need to be included.
Misc.
– There is a current date indicator that will show the current date on the Gantt chart.
– The Gantt chart bars and the task list will highlight according to % complete status.
– The Page Setup includes repeating rows of 2:9 and repeating columns of A:G. To print a small section of the chart simply select the area of the Gantt chart (ex. H10:AZ41) and set it as the Print Area.
– Hypothetically additional days can be added to the calendar by copy and inserting columns to the left of Column GU. Be sure to check that the formulas in 6:7 and 4:5 have been copied appropriately.

Data Collection Sheet

DATE GO HOME!!

TIME

Eliminate

[

D s

2 ¡ [ ¨ D s
3 ¡ [ ¨ D s
4 ¡ [ ¨ D s
5 ¡ [ ¨ D s
6 ¡ [ ¨ D s
7 ¡ [ ¨ D s
8 ¡ [ ¨ D s
9 ¡ [ ¨ D s
10 ¡ [ ¨ D s
11 ¡ [ ¨ D s
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35 ¡ [ ¨ D s

FLOW CHART

SUBJECT

FORM NO.

FILE NO.

PAGE NO. OF PAGES

CHARTED BY

SUMMARY OF STEPS IN PROCESS

OPERATIONS

TRANSPORTS

INSPECTIONS

DELAYS

STORAGE

TOTAL STEPS

TOTAL DIST.

TOTAL MINS

PRESENT

PROPOSED

SAVINGS

LINE

DETAILS OF PRESENT/PROPOSED METHOD (CIRCLE ONE)

Operation

Transport

Inspection

Delay

Storage

DISTANCE

Possibilities

Notes

Simplify

Alt Sequence

Reg.

Combine

Other

Control_Plan

Control Plan
GO HOME!!

Process

Sample Size

Process:	Preparer:	Page:
Customer:	Email:	Reference No:
Stakeholder:	Phone:	Revision Date:
Business:	Owner:	Approval:
Process Step	CTQ/Metric	CTQ / Metric Equation	Specification/ Requirement	Measurement Method	Measure Frequency	Responsible for Metric	Link or Report Name	Corrective Action	Responsible for Action
LSL	USL

VILLANOVA UNIVERSITY

RA
CI
– People

Project Leader

Communication Plan

Functions

Responsible

GO HOME!!

Sponsor

I I I I I I I I I I I I I I I I I I SENIOR 0 0 0 0

A A C I I I I I I I I I I I I I I I 0 0 2 1

Americas

C C C I I I I I I I I I I I I I I I 0 0 0 3 15

ABLES

A A R C C C C C C C R R R R R R R R ACCOUNTABLES 0 9 2 7 0

MBB A A R C C C C C C C R R R R R R R R 0 9 2 7 0
MBB R R R I I I I I R A R R R C C C C C 0 7 1 5 5
MBB 0
HR Director R R A R R R R R R R C I I I I I I I 0 9 1 1 7

R R R A A A A A A R A C C C C C C C 0 4 7 7 0

I C I R R R R R R R R A A A A A A A 0 8 7 1 2

0 0 0 0 0
BB BB I C I C C C C C C C R R R R R R R R BB 0 8 0 8 2
BB I C I C C C C C C C R R R R R R R R 0 8 0 8 2
BB I C R R R R R R R R C C C C C C C C 0 8 0 9 1
BB I C R R R R R R R R C C C C C C C C 0

I I I I I I I I I I I I I R R R R R 0 5 0 0 13

CI I I I I I I I I I I I I I R R R R R 0 5 0 0 13

CI 0

CI I I I I I I I I I I I I I R R R R R 0 5 0 0 13

Team Members Team Members 0 0 0 0 0

0 0 0 0 0
0 0 0 0 0
0 0 0 0 0

Support Support 0 0 0 0 0

0 0 0 0 0

RACI Matrix

Count of Assignments

Project Structure

Organization Development

Daily Accountability

Lean Tools

Role

Names

Project Management

Roles & Responsibilities

Talent Selection

Goal Alignment

Structure

Support

4 – Tier Structure

Steering Team

Coaching Structure

Daily Tier Accountability

Leader Standard Work

Escalation Process

Problem Solving

5S + 1

Visual Management

Total Productive Maintenance

Value Stream Mapping/ Flow

Accountable

Consulting

Inform

SENIOR

VP of CI

HR Director

MBB

COUNT

Plant Manager

Lean Manager

Operations Mgr

Quality Manager

EH&S Manager

Responsible 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 SENIOR ACCOUNTABLES BB Team Members Support 0 0 0 9 9 7 9 4 8 0 8 8 8 5 5 5 5 0 0 0 0 0 0 0 Accountable 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 SENIOR ACCOUNTABLES BB Team Members Support 0 2 0 2 2 1 1 7 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Consulting 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 SENIOR ACCOUNTABLES BB Team Members Support 0 1 3 7 7 5 1 7 1 0 8 8 9 0 0 0 0 0 0 0 0 0 0 0

Cause_Effect_Diagram

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Problem

	Instructions:
STEP 1 : Define the problem. What is the product, process or service that has failed.
STEP 2 : Starting with ‘Materials’ or any other label, ask: is there anything about materials that
might contribute to the problem. Record it next to one of the arrows under Materials.
STEP 3 : Repeat asking “is there anything about materials that might contribute to the problem”
Record each result next to an arrow.
STEP 4 : Repeat Step 2 & 3 for each successive category.
STEP 5 : Identify the candidates that are the most likely Root Cause
STEP 6 : If further “screening” is necessary, assess the likely Root Causes using the “Impact”
and “Implement” matrix, selecting items marked 1, then 2 . . . 4 as priorities.

CTQ

GO HOME!!
Instructions:

Step 1: Define the problem. Place it at the top.

Step 2: Ask: ‘What causes this?” or “Why did this happen?”

Brainstorm all possible answers and write each below the problem

Step 3: Determine if all items from Step 2 are sufficient and necessary.

Ask: “are all items at this level necessary for the one on the level above?”

Step 4: Using each item from Step 2, repeat Step 2 & 3. In other words, treat

each response from Step 2 as the new problem and repeat Step 2 & 3

Step 5: Repeat the process until specific actions can be taken

Step 6: Identify Root Cause

Problem
Cause
Cause
Cause
Cause
Cause
Cause

Pareto
Chart
Template

PARETO CHART TEMPLATE GO HOME!!

COUNT

8 99%

3 100%

LEARN MORE ABOUT SMARTSHEET FOR PROJECT MANAGEMENT

The Pareto principle states that, for many events, roughly 80% of the effects come from 20% of the causes.
SORT DATA DESCENDING / HIGH-TO-LOW
C A U S E	E F F E C T	CUMULATIVE
CATEGORY / DESCRIPTION	PERCENTAGE
Issue 1	74	23%
Issue 2		42
Issue 3			49	57%
Issue 4	68%
Issue 5	76%
Issue 6	85%
Issue 7	91%
Issue 8	97%
Issue 9
Issue 10

0.2

334

4858044

64 0.4 164

0378548895901 0.5

1798

107

256 0.6

0788643

232 0.7

63406940063091

51 0.8

4258675078864 0.914

649842

291

0.96

529968454258674

0.990

53627760252361 1

COUNT Issue 1 Issue 2 Issue 3 Issue 4 Issue 5 Issue 6 Issue 7 Issue 8 Issue 9 Issue 10 74 58 49 33 28 26 22 16 8 3 CUMULATIVE PERCENTAGE Issue 1 Issue 2 Issue 3 Issue 4 Issue 5 Issue 6 Issue 7 Issue 8 Issue 9 Issue 10 0.2334384858044164 0.41640378548895901 0.57097791798107256 0.67507886435331232 0.76340694006309

151

0.8454258675078864 0.91482649842271291 0.9

29968454258674 0.99053627760252361 1

https://goo.gl/v5dcnZ

DPMO Calculator

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Six Sigma Calculator The calculation of a Sigma level, is based on the number of defects per million opportunities (DPMO). Six Sigma Calculator

Enter values in Gray cells only In order to calculate the DPMO, three distinct pieces of information are required: a) the number of

units

produced A. All values required to calculate Sigma level b) the number of defect opportunities per unit Defects

: 1,350 DPMO:

27
c) the number of defects Units: 1,000,000 Sigma Level

: 5.5

4 Opportunities

per Unit: 50
The actual formula is: DPMO = (Number of Defects X 1,000,000) B. Sigma calculated based on defects and number of opportunities Defects: 1,350 DPMO:

1350 ((Number of Defect Opportunities/Unit) x Number of Units) Number of Opportunities:

1,000,000 Sigma Level:

4.5

0 Example: C. Enter only the known Defects Per Million Opportunities A manufacturer of computer hard drives wants to measure their Six Sigma level. Enter DPMO

1,350 Sigma Level:

4.50 Over a given period of time, the manufacturer creates 83,934 hard drives. The manufacturer performs 8 individual checks to test quality of the drives. During testing 3,432 are rejected. Defects

3432

DPMO

5111.1587676031 Opportunities

83934

Sigma Level

4.1 Defect Opportunities per unit

8
Six Sigma Table:

690,000 2

308,000 3

66,800 4

6,210 5

320 6

3.4 Source for this file:

http://home.xtra.co.nz/hosts/smtconz/Quality/Simple%20Six%20Sigma%20Calculator.xls

DPMO_Sigma_Level

Sigma Level DPMO

DPMO Yield Defect Rate GO HOME!!

310000%

1.1

668000%

820%

139000%

3780%

1.3

1860%

487000%

3000%

3070%

1.5

614000%

3490%

1.6

98000%

1.7

270%

1.8

40000%

280%

568000%

432000%

500000%

300%

2.1

728000%

880%

806000%

290%

710%

00%

920%

2.5

860%

2.7

830%

2.8

380%

900%

3.1

660%

3.3

66000%

310%

3.4

3000%

160%

3.5

65000%

500%

18000%

640%

5000%

3.8 144

44000%

3.9 96

6000%

4 63

4.1 41

1000%

4.2 26

6000%

670%

7000%

50%

4.4 10

4.5 6

1349

0.13490%

967

0.96

336

232

0.34

90%

0.2

107

70%

20%

5.4 0.067

5.5

0.021

5.7

5.8 0.007

0.004

6 0.002

3.4

Without 1.5 sigma shift	With 1.5 sigma shift
	Yield		Defect Rate
3				173	68.2690000%	3		1.7	697612	30.23880%	69.76120%
271332	7		2.8	27.1332000%	660082	3		3.9	180	6	6.00
	1.2	230139	76.9861000%	2	3.0	621378	37.86220%	6		2.1
193601	80.6399000%	19.3601000%	581814	4		1.8	58.18140%
	1.4	161513	8		3.8		16.151	54							169	4	5.8	54.16930%
1	336	86.6386000%	1		3.3	50	1349	49.86510%	5			0.1
109598	89.0402000%	1	0.95	461139	53.88610%	46.11390%
89130	91.0870000%	8.9130000%	421427	57.85730%	4		2.14
71860	9	2.81	7.1860000%	382572	6		1.74	38.25720%
	1.9	57432	9		4.2		5.7	344915	65.50850%	3		4.4							150
45500	9	5.4	4.5500000%	308770	6	9.12	30.87700%
35728	96.4272000%			3.5	274412	7				2.55	27.44120%
	2.2	27806	97.2194000%			2.7	2	420	7	5.79	2	4.20
	2.3	21448	97.8552000%	2.14	480	211927	78.80730%	21.19270%
	2.4		163	98.3605000%	1.6395000%	184108	8	1.58	18.41080%
12419	98.7581000%	1.2419000%	158686	84.13140%	1	5.86
	2.6	9322	99.0678000%	0.9322000%	135686	86.43140%	13.56860%
6934	99.3066000%	0.6934000%	115083	88.49									170	1	1.50
5110	99.4890000%	0.5110000%	96809	90.31910%	9.68090%
	2.9	3731	99.6269000%	0.3731000%	80762	9				1.92	8.07620%
2699	99.7301000%	0.2699000%	66810	9	3.31	6.68100%
1935	99.8065000%	0.1935000%	54801	94.51990%	5.48010%
	3.2	1374	99.8626000%	0.1374000%	44566	95.54340%		4.45
966	99.9034000%		0.09	35931	96.40690%			3.59
673	99.9327000%			0.06	28716	97.12840%		2.87
465	99.9535000%		0.04	22750	97.72500%						2.27
	3.6	318	99.9682000%		0.03		178	98.21360%		1.78
	3.7	215	99.9785000%			0.02	13903	98.60970%	1.39030%
99.9856000%						0.01	10724	98.92760%	1.07240%
99.9904000%			0.009	8197	99.18030%	0.81970%
99.9937000%	0.0063000%	6209	99.37910%	0.62090%
99.9959000%		0.004	4661	99.53390%	0.46610%
99.9974000%		0.002	3467	99.65330%		0.34
	4.3	99.9983000%		0.001	2555	99.74450%				0.25
99.9990000%	0.0010000%	1865	99.81350%	0.18650%
99.9994000%	0.0006000%	99.86510%
	4.6	99.9996000%	0.0004000%	99.90330%	0.09670%
	4.7	99.9998000%		0.000200	687	99.93130%	0.06870%
	4.8	99.9999000%		0.000100	483	99.95170%	0.04830%
	4.9	99.9999040%		0.000096	99.96640%	0.03360%
0.574	99.9999426%		0.000057	99.97680%	0.02320%
5.1	99.9999660%		0.000034	159	99.98410%			0.015
5.2	99.9999800%		0.000020	99.98930%		0.010
5.3	0.116	99.9999884%			0.000011	99.99280%		0.007
99.9999933%				0.000006	99.99520%	0.00480%
0.038	99.9999962%								0.000003	99.99690%	0.00310%
5.6	99.9999979%												0.000002	99.99800%	0.00200%
				0.012	99.9999988%															0.000001	1	3.35	99.99867%	0.00134%
99.9999993%											0.000000		8.55	99.99915%	0.00086%
5.9	99.9999996%	0.0000004%	5.42	99.99946%	0.00054%
99.9999998%	0.0000002%	99.99966%	0.00034%

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Communication_Plan

Project Lead

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Location

Communication Plan Template
Process/Function Name		Project/Program Name		Project Sponsor/Champion
Communication Purpose:
Target Audience	Key Message	Message Dependencies	Delivery Date	Medium	Follow up Medium	Messenger	Escalation Path	Contact Information

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Training_Plan

Project Process Project Lead

Sponsor GO HOME!!

Who

When

Status

Training Plan Template
Business Division
Where	How Many	Key Change/Process	Training Medium	Supporting Docs	Technology Requirements	Other Requirements	Trainer

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Audit_Checklist

Audit Checklist

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, Procedure etc.

Observation

YES NO

Observation

YES NO

Observation

YES NO

Observation

YES NO

Observation

YES NO

Observation

YES NO

Observation

YES NO

Observation

YES NO

Observation

YES NO

Observation

YES NO

Observation

YES NO

ERROR:#DIV/0!

Target Area:

Statement of Audit Objective:

Auditor:

Audit Date:

Audit Technique

Auditable Item,

Observation

Individual Auditor Rating (Circle Rating)

Have all associates been trained?

YES

Is training documentation available?

Is training documentation current?

Are associates wearing proper safety gear?

Are SOP’s available?

Are SOP’s current?

Is quality being measured

Is sampling being conducted in random fashion

Is sampling meeting it’s sample size target?

Are control charts in control

Are control charts current?

Is the process capability index >1.0?

Number of Out of Compliance Observations

Total Observations

Audit Yield

Corrective Actions Required

Auditor Comments

VILLANOVA UNIVERSITY

Pugh_Matrix

+ Pugh Matrix
–

GO HOME!!

Baseline

7 – + – + + + +

7 – S + + + + +

1 + + + + + + +

1 + + + S + + –

9 + + + + + – +

5 + + – + + + +

3 + + + + + – +

3 + + + + + + +

6 7 6 7 8 6 7

2 0 2 0 0 2 1

0 1 0 1 0 0 0

22 29 24 35 36 24

14 0 12 0 0 12 1

8 29 12 35 36 12 34

Key

Pugh Matrix Template
Owner:
Measures\|CTQ’s\|Factors etc.	Importance Rating	Option 1	Option 2	Option 3	Option 4	Option 5	Option 6	Option 7
Hard Dollar Savings
Operating Expenses
Cost Avoidance
Ongoing Maintenance Expense
ROI (NPV)
Incremental Capital
Operational Stability
Brand/Reputation
Sum of +’s
Sum of -‘s
Sum of Sames
Weighted Sum of +’s
Weighted Sum of -‘s
Highest Score Wins
Baseline = “write your description of the baseline here”
Option1 = “description of option 1”
Option2 = “description of option 2”
Option3 = “description of option 3”
Option4 = “description of option 4”
Option5 = “description of option 5”
“+” = Better than baseline
“-” = Worse than baseline
“s” = Same as baseline

VILLANOVA UNIVERSITY

CandE_Matrix

Cause & Effect Matrix

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Date:
Project:

s (X’s)#

Score

Matrix Owner:

Output Measures (Y’s)*

Y10

Weighting (1-10):

Input

Variable

For each X, score its impact on each Y listed above (use a 0,3,5,7 scale)

X10

X11

X12

X13

X14

X15

X16

X17

X18

X19

X20

X21

X22

X23

X24

X25

X26

X27

X28

X29

X30

Matrix Premise: The Matrix or “Cause & Effect Matrix functions on the premise of the Y=f(x) equation.

*Rate each “Y” on a scale of 1 to 10 with 1 being the least important output measure

#For each X rate its impact on each Y using a 0,3,5,7 scale (0=No impact, 3=Weak impact, 5=Moderate impact, 7=Strong impact).

VILLANOVA UNIVERSITY

Risk_Mgt_Plan

Risk Management Plan

Project/Program Name Project Lead Project Sponsor/Champion

Responsible Status GO HOME!!

Company	Last Updated
Risk ID	Risk Category	Risk Description	Risk Impact	ImpactRating	Mitigation Action

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Sample_Size_Calculator

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Discrete

271

	Sample Size Calculator
Continuous		Data Type		Discrete
Enter Proportion Defective:	0.50
Acceptable Margin of Error:			0.05
Required Sample Size @ 99% CI	666
Required Sample Size @ 95% CI	385
Required Sample Size @ 90% CI

VILLANOVA UNIVERSITY

Takt Time Calculator

Takt Time Calculator GO HOME!!

/ day

1 shifts

480

30 minutes

0 minutes

420 minutes

Net Available time / shift

25200 seconds

800 units

Please enter data in boxes marked yellow
Working	shifts
Hours / shift	hours
Gross Available time / shift			minutes
Break time / shift
Lunch time / shift
Planned downtime / shift
	Net Available time / shift
	25200	seconds
Net Available time / day
Customer Demand / day
Takt Time =	seconds / unit

VILLANOVA UNIVERSITY

Z_Distribution_Table

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Z 0 0.01 0.02 0.03 0.04 0.05 0.06

0.09

0.0

0.1

0.2

936

0.3

0.4

0.5

595

0.6

629

086

846

627

0.7

0.8

855

0.9

056

023

1.0

170

1.1

1.2

070

835

042

1.3

1.4

145

1.5

1.6

551

1.7

1.8

1.9

2.0

675

2.1

629

2.2

903

911

2.3

894

2.4

2.5

868

2.6

2.7

2.8

2.9

3.0

3.1

3.2

3.3

3.4

3.5

0.000200

3.6

3.7

0.000100 0.000096

3.8

0.000057

3.9

0.000034

4.0

4.1

0.000020

0.000017

0.000015

4.2

0.000013

0.000012 0.000011 0.000011

0.000010

0.000009

4.3 0.000009

0.000008

0.000007 0.000007 0.000007 0.000006 0.000006 0.000006

4.4

0.000005 0.000005 0.000005

0.000004 0.000004 0.000004 0.000004 0.000004

4.5 0.000003 0.000003 0.000003 0.000003 0.000003 0.000003 0.000003 0.000002 0.000002 0.000002
4.6 0.000002 0.000002 0.000002 0.000002 0.000002 0.000002 0.000002 0.000002 0.000001 0.000001
4.7 0.000001 0.000001 0.000001 0.000001 0.000001 0.000001 0.000001 0.000001 0.000001 0.000001
4.8 0.000001 0.000001 0.000001 0.000001 0.000001 0.000001 0.000001 0.000001 0.000001 0.000001
4.9 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000

Table of Probabilities for the Standard Normal (Z) Distribution
Right Tailed Distribution
0.07	0.08
0.500000	0.496011	0.492022	0.488034		0.484								47	0.480061	0.476078	0.472097	0.468119	0.464144
0.460										172	0.456205	0.452242	0.448283	0.444330	0.440382	0.436441	0.432505	0.428576	0.424655
0.420740	0.4						168		0.412	0.409046	0.405			165	0.401294	0.397432	0.393580	0.389739	0.385908
0.382089	0.378280	0.374484	0.370700	0.366928	0.363169	0.359424	0.355691	0.351973	0.348268
0.344578	0.340903	0.337243	0.333598	0.329969	0.326355	0.322758	0.319178	0.315614	0.312067
0.308538	0.305026	0.30	153	0.298056	0.294599	0.291160	0.287740	0.284339	0.280957		0.277
0.274253	0.270931		0.267	0.264347		0.261						0.257			0.254	0.251429	0.248252	0.245097
0.241964	0.238852	0.235762	0.232695	0.229650	0.226627	0.223627	0.220650	0.2	176	0.214764
	0.211	0.208970	0.206108	0.203269	0.200454	0.197663	0.194895	0.192150	0.189430	0.186733
0.184060	0.181411	0.178786	0.176186	0.173609	0.					171	0.168528	0.	166	0.163543	0.161087
0.158655	0.156248	0.153864	0.151505	0.	149	0.146859	0.144572	0.142310	0.140071	0.137857
0.135666	0.133500	0.131357	0.129238	0.127143	0.125072	0.123024	0.121000	0.119000	0.117023
	0.115	0.113139	0.111232	0.109349	0.107488	0.105650		0.103		0.102	0.100273	0.098525
0.096800	0.095098	0.093418	0.09			175	0.090123	0.088508	0.086915	0.085343	0.083793	0.082264
0.080757	0.079270	0.077804	0.076359	0.074934	0.073529		0.072	0.070781	0.069437	0.068112
0.066807	0.065522	0.064255	0.063008	0.061780	0.060571	0.059380	0.058208	0.057053	0.055917
0.054799	0.053699	0.052616		0.051	0.050503	0.049471	0.048457	0.047460	0.046479	0.045514
0.044565	0.043633	0.042716	0.041815	0.040930	0.040059	0.039204	0.038364	0.037538	0.036727
0.035930	0.035148	0.034380	0.033625	0.032884	0.032157	0.031443	0.030742	0.030054	0.029379
0.028717	0.028067	0.027429	0.026803	0.026190	0.025588	0.024998	0.024419	0.023852	0.023295
0.022750	0.022216	0.021692	0.021178		0.020	0.020182	0.019699	0.019226	0.018763	0.018309
0.017864	0.0				174	0.017003	0.016586	0.016			177	0.015778	0.015386	0.015003			0.014	0.014262
		0.013	0.013553	0.013209	0.012874	0.012545	0.012224									0.011	0.011604	0.011304	0.011011
0.010724	0.010444	0.010170	0.009903	0.009642	0.009387	0.009137			0.008	0.008656	0.008424
0.008198	0.007976	0.007760	0.007549	0.007344	0.007143	0.006947	0.006756	0.006569	0.006387
0.006210	0.006037		0.005	0.005703	0.005543	0.005386	0.005234	0.005085	0.004940	0.004799
0.004661	0.004527	0.004396	0.004269	0.004145	0.004025	0.003907	0.003793	0.003681	0.003573
0.003467	0.003364	0.003264	0.003		167	0.003072	0.002980	0.002890	0.002803	0.002718	0.002635
0.002555	0.002477	0.002401	0.002327	0.002256	0.002186	0.002118	0.002052	0.001988	0.001926
0.001866	0.001807	0.001750	0.001695	0.001641	0.001589	0.001538	0.001489	0.001441	0.001395
0.001350	0.001306	0.001264	0.001223	0.001183	0.001144	0.001107	0.001070	0.001035	0.001001
0.000968	0.000935	0.000904	0.000874	0.000845	0.000816	0.000789	0.000762	0.000736	0.000711
0.000687	0.000664	0.000641	0.000619	0.000598	0.000577	0.000557	0.000538	0.000519	0.000501
0.000483	0.000466	0.000450	0.000434	0.000419	0.000404	0.000390	0.000376	0.000362	0.000349
0.000337	0.000325	0.000313	0.000302	0.000291	0.000280	0.000270	0.000260	0.000251	0.000242
0.000233	0.000224	0.000216	0.000208	0.000193	0.000185	0.000178	0.000172	0.000165
0.000159	0.000153	0.000147	0.000142	0.000136	0.000131	0.000126	0.000121	0.000117	0.000112
0.000108	0.000104	0.000092	0.000088	0.000085	0.000082	0.000078	0.000075
0.000072	0.000069	0.000067	0.000064	0.000062	0.000059	0.000054	0.000052	0.000050
0.000048	0.000046	0.000044	0.000042	0.000041		0.000039	0.000037	0.000036	0.000033
0.000032	0.000030	0.000029	0.000028	0.000027	0.000026	0.000025	0.000024	0.000023	0.000022
0.000021	0.000019	0.000018		0.000017	0.000016		0.000015	0.000014
	0.000013		0.000012		0.000010			0.000009
	0.000008				0.000007
			0.000005						0.000004
Standard Normal (Z) Distribution:

VILLANOVA UNIVERSITY

t_Distribution_Table

0.990

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3 0.277

5 0.267

9 0.261

11 0.260

13 0.259

15 0.258

16 0.258

17 0.257

18 0.257 0.534

19 0.257

20 0.257 0.533

21 0.257

0.532

23 0.256 0.532 0.858

24 0.256

25 0.256 0.531

26 0.256 0.531 0.856

27 0.256 0.531

28 0.256

0.855

29 0.256 0.530

30 0.256 0.530 0.854

40 0.255

60 0.254

120 0.254

0.845

Table of Probabilities for Student’s t-Distribution
	df	0.600	0.700	0.800	0.900	0.950		0.975		0.995
0.325	0.727	1.376					3.07	6.314	1				2.70	3		1.82	63.657
0.289	0.617	1.061		1.88			2.92		4.30	6.965	9.925
	0.584	0.978	1.638						2.35			3.18		4.54	5.841
0.271		0.56	0.941		1.53					2.13				2.77			3.74		4.60
		0.55	0.920		1.47						2.01					2.57		3.36		4.03
0.265			0.553	0.906	1.440				1.94		2.44			3.14		3.70
0.263		0.54	0.896	1.415					1.89			2.36		2.99			3.49
0.262								0.546	0.889	1.397			1.86				2.30		2.89	3.355
0.543	0.883	1.383		1.83			2.26			2.82	3.250
	0.260	0.542	0.879	1.372			1.81							2.22					2.76		3.16
0.540	0.876	1.363					1.79								2.20					2.71		3.10
	0.259	0.539	0.873		1.35	1.782				2.17			2.68			3.05
0.538	0.870	1.350			1.77					2.16			2.65						3.01
		0.258	0.537	0.868	1.345	1.761	2.145				2.62		2.97
0.536	0.866	1.341					1.75	2.131					2.60			2.94
0.535	0.865	1.337	1.746					2.12				2.58	2.921
	0.534	0.863	1.333	1.740							2.11		2.56	2.898
0.862	1.330		1.73						2.10	2.552	2.878
	0.533	0.861	1.328	1.729						2.09							2.53			2.86
0.860	1.325	1.725					2.08	2.528			2.84
		0.532	0.859			1.323	1.721	2.080							2.51			2.83
								0.256		0.858	1.321		1.71				2.07	2.508	2.819
1.319	1.714							2.06	2.500			2.80
			0.531	0.857	1.318	1.711	2.064						2.49				2.79
	0.856	1.316			1.70	2.060				2.48		2.78
1.315	1.706				2.05			2.47	2.779
	0.855	1.314	1.703	2.052	2.473	2.771
		0.530	1.313	1.701						2.04							2.46	2.763
	0.854	1.311		1.69	2.045	2.462			2.75
1.310	1.697	2.042								2.45	2.750
0.529	0.851	1.303		1.68			2.02								2.42	2.704
0.527	0.848	1.296	1.671		2.00				2.39						2.66
0.526	1.289		1.65				1.98	2.358				2.61
df (degrees of freedom) = number of samples – 1
1 – alpha (for one tail) or 1 – alpha/2 (for two tails)

VILLANOVA UNIVERSITY

F_Distribution_Table

0.05

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 20 24 30 40 60 120

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19.40

19.42

19.45

9.12

8.55

6.00

5.86

5.79

4.50

4.53

4.03

3.74 3.70

4.74

3.87

4.46

3.84

3.44

3.35 3.31

3.01 2.97

3.18 3.14 3.10 3.07 3.05

3.01 2.94

2.86 2.83 2.79 2.75

4.10

3.48

3.22 3.14 3.07

2.98 2.94

2.89 2.86

2.77

2.70 2.66 2.62 2.58

3.98 3.59 3.36

3.01

2.90 2.85 2.82 2.79 2.76 2.74

2.65 2.61 2.57 2.53 2.49 2.45

3.49 3.26

2.91 2.85 2.80 2.75 2.72

2.66

2.62

2.51 2.47

3.81 3.41 3.18 3.03 2.92 2.83 2.77 2.71

2.60 2.58 2.55 2.53 2.46 2.42 2.38 2.34 2.30

14 4.60 3.74 3.34 3.11

2.85 2.76 2.70 2.65 2.60 2.57 2.53 2.51 2.48 2.46 2.39 2.35

2.27 2.22

15 4.54 3.68 3.29

2.90 2.79 2.71 2.64

2.54 2.51 2.48 2.45 2.42

2.25 2.20 2.16 2.11

3.63 3.24 3.01 2.85 2.74 2.66 2.59 2.54 2.49 2.46 2.42 2.40

2.35

2.11 2.06

17 4.45 3.59 3.20 2.96 2.81 2.70 2.61 2.55 2.49 2.45

2.38 2.35 2.33 2.31

2.19 2.15 2.10 2.06 2.01

3.55 3.16

2.77 2.66 2.58 2.51 2.46 2.41 2.37 2.34 2.31 2.29 2.27 2.19 2.15 2.11 2.06 2.02

2.90 2.74 2.63 2.54 2.48 2.42 2.38 2.34 2.31 2.28 2.26 2.23 2.16 2.11 2.07

1.98

20 4.35 3.49 3.10 2.87 2.71 2.60 2.51 2.45 2.39 2.35 2.31 2.28 2.25 2.22 2.20 2.12 2.08 2.04

3.07 2.84 2.68 2.57 2.49 2.42 2.37

2.28 2.25 2.22 2.20 2.18 2.10 2.05 2.01

1.92

22 4.30 3.44 3.05 2.82 2.66 2.55 2.46 2.40 2.34 2.30 2.26 2.23 2.20 2.17 2.15 2.07 2.03 1.98 1.94 1.89

23 4.28

3.03 2.80 2.64 2.53 2.44 2.37 2.32 2.27 2.24 2.20 2.18 2.15 2.13 2.05 2.01 1.96

1.86 1.81

24 4.26

3.01 2.78 2.62 2.51 2.42 2.36 2.30 2.25 2.22 2.18 2.15 2.13 2.11 2.03 1.98 1.94 1.89 1.84 1.79

3.39 2.99 2.76 2.60 2.49 2.40 2.34 2.28 2.24 2.20 2.16 2.14 2.11 2.09 2.01 1.96 1.92 1.87 1.82 1.77

3.37 2.98 2.74 2.59 2.47 2.39 2.32 2.27 2.22 2.18 2.15 2.12 2.09 2.07 1.99 1.95 1.90

1.75

27 4.21 3.35 2.96

2.57 2.46 2.37 2.31 2.25 2.20 2.17 2.13 2.10 2.08 2.06 1.97 1.93 1.88 1.84 1.79 1.73

28 4.20 3.34 2.95 2.71 2.56 2.45 2.36 2.29 2.24 2.19 2.15 2.12 2.09 2.06 2.04 1.96 1.91 1.87 1.82 1.77 1.71
29

3.33 2.93 2.70 2.55 2.43 2.35 2.28 2.22 2.18 2.14 2.10 2.08 2.05 2.03 1.94 1.90 1.85 1.81 1.75 1.70

2.92 2.69 2.53 2.42 2.33 2.27

2.16 2.13 2.09 2.06 2.04 2.01 1.93 1.89 1.84 1.79 1.74 1.68

3.23 2.84 2.61 2.45 2.34 2.25 2.18 2.12 2.08 2.04 2.00 1.97 1.95 1.92 1.84 1.79 1.74 1.69

1.58

60 4.00 3.15 2.76 2.53 2.37 2.25 2.17 2.10 2.04 1.99 1.95 1.92 1.89 1.86 1.84 1.75 1.70 1.65

1.53 1.47

120

3.07 2.68 2.45 2.29 2.18 2.09 2.02 1.96 1.91 1.87 1.83 1.80 1.78 1.75

1.50

1.35

Table of Probabilities for the F Distribution
Alpha =
D/N
161.45	199.50	215.71	224.58	230.16	233.99	236.77	238.88	240.54	241.88	24		2.98	243.91	244.69	245.36	245.95	248.01	249.05	250.10	251.14	252.20	253.25
18.51	19.00	19.16	19.25	19.30	19.33	19.35	19.37	19.38		19.40	19.41		19.42	19.43		19.45	19.46	19.47	19.48	19.49
10.13	9.55	9.28	9.01	8.94	8.89	8.85	8.81	8.79	8.76	8.74	8.73	8.71	8.70	8.66	8.64	8.62	8.59	8.57
7.71	6.94	6.59	6.39	6.26	6.16	6.09	6.04	5.96	5.94	5.91	5.89	5.87	5.80	5.77	5.75	5.72	5.69	5.66
6.61	5.41	5.19	5.05	4.95	4.88	4.82	4.77		4.74	4.70	4.68	4.66	4.64	4.62	4.56		4.53		4.46	4.43	4.40
5.99	5.14	4.76	4.39		4.28		4.21	4.15		4.10	4.06		4.00		3.98	3.96	3.94		3.87		3.84		3.81	3.77
5.59		4.35	4.12	3.97	3.79	3.73		3.68	3.64	3.60	3.57		3.55	3.53	3.51			3.44		3.41	3.38			3.34	3.30	3.27
5.32	4.07	3.69	3.58	3.50		3.39	3.28		3.26		3.24		3.22		3.15	3.12	3.08	3.04
5.12		4.26	3.86		3.63		3.48		3.37		3.29		3.23			3.03				2.90
4.96	3.71		3.33	3.02		2.91					2.85					2.74
4.84		3.20	3.09		2.95		2.72
4.75	3.89		3.11	3.00		2.69			2.64				2.54		2.43				2.38							2.34
4.67	2.67		2.63								2.25
		2.96						2.31								2.18
3.06			2.59				2.40			2.33				2.29
4.49						2.37						2.28				2.24				2.19							2.15
	2.41			2.23
4.41		2.93			1.97
4.38	3.52	3.13				2.03			1.93
		1.99				1.95			1.90
4.32	3.47			2.32					1.96				1.87
					1.84
3.42			1.91
3.40
4.24
4.23		1.85		1.80
2.73
4.18
4.17	3.32	2.21
4.08	1.64
1.59
3.92	1.66	1.61	1.55	1.43
Right Tailed, D/N = df in denominator = down the rows, df in numerator = across the columns	Note: Table is for an alpha of 0.05
Table of Probabilities for F Distribution

VILLANOVA UNIVERSITY

Chi_Square_Distribution_Table

df 0.995 0.990 0.975 0.95 0.9

0.5 0.25 0.1 0.05 0.25 0.01 0.005 0.001 GO HOME!!

1 0.000039

0.102

1.323

2 0.010 0.020 0.051 0.103 0.211

2.773

3 0.072 0.115

0.584

4.108

0.484

5.385

5 0.412

6.626

7.841

9.037

10.219

11.389

12.549

3.816

13.701

14.845

15.984

17.117

18.245

19.369

20.489

21.605

22.718

23.828

24.935

26.039

27.141

28.241

29.339

30.435

16.151

31.528

32.620

33.711

34.800

45.616

56.334

66.981

77.577

88.130

98.650

100

109.141

Table of Probabilities for the Chi-Squared Distribution
Alpha Risk
0.75
0.000157	0.000982	0.00393	0.0158	0.455	2.706	3.841	6.635	7.879				10.8
0.575	1.386		2.773	4.605	5.991	9.210			10.5	1	3.816
0.216	0.352	1.213	2.366		4.108	6.251	7.815	11.345	12.838	16.266
0.207	0.297	0.711	1.064	1.923	3.357		5.385	7.779	9.488	13.277	14.860	18.467
	0.554	0.831	1.145	1.610	2.675	4.351		6.626	9.236	11.070	15.086	16.750	20.515
0.676	0.872	1.237	1.635	2.204	3.455	5.348		7.841			10.6	12.592	16.812	18.548	22.458
0.989	1.239	1.690	2.167	2.833	4.255	6.346		9.037	12.017	14.067	18.475	20.278	24.322
1.344	1.646	2.180	2.733	3.490	5.071	7.344		10.219	13.362	15.507	20.090	21.955	26.124
1.735	2.088	2.700	3.325	4.168	5.899	8.343		11.389	14.684	16.919	21.666	23.589	27.877
2.156	2.558	3.247	3.940	4.865	6.737	9.342		12.549	15.987	18.307	23.209	25.188	29.588
2.603	3.053	4.575	5.578					7.58	10.341		13.701	17.275	19.675	24.725	26.757	31.264
3.074	3.571	4.404	5.226	6.304	8.438	11.340		14.845	18.549	21.026	26.217	28.300	32.909
3.565	4.107	5.009	5.892	7.042	9.299	12.340		15.984	19.812	22.362	2			7.6	29.819	34.528
4.075	4.660	5.629	6.571	7.790	10.165	13.339		17.117	21.064	23.685	29.141	31.319	36.123
4.601	5.229	6.262	7.261	8.547	11.037	14.339		18.245	22.307	24.996	30.578	32.801	37.697
5.142	5.812	6.908	7.962	9.312	11.912	15.338		19.369	23.542	26.296	32.000	34.267	39.252
5.697	6.408		7.56	8.672	10.085	12.792	16.338		20.489	24.769	27.587	33.409	35.718	40.790
6.265	7.015	8.231	9.390							10.86	13.675	17.338		21.605	25.989	28.869	34.805	37.156	42.312
6.844			7.63	8.907	10.117	11.651	14.562	18.338		22.718	27.204	30.144	36.191	38.582	43.820
7.434	8.260	9.591				10.85	12.443	15.452	19.337		23.828	28.412	31.410	37.566	39.997	45.315
8.034	8.897	10.283	11.591	13.240	16.344	20.337		24.935	29.615	32.671	38.932	41.401	46.797
8.643	9.542			10.9	12.338	14.041	17.240	21.337		26.039	30.813	33.924	40.289	42.796	48.268
9.260	10.196	11.689	13.091	14.848	18.137	22.337		27.141	32.007	35.172	41.638	44.181	49.728
9.886	10.856	12.401	13.848	15.659	19.037	23.337		28.241	33.196	36.415	42.980	45.559	51.179
10.520	11.524	13.120	14.611	16.473	19.939	24.337		29.339	34.382	3		7.65	44.314	46.928	52.620
11.160	12.198	13.844	15.379	17.292	20.843	25.336		30.435	35.563	38.885	45.642	48.290	54.052
11.808	12.879	14.573	18.114	21.749	26.336		31.528	36.741	40.113	46.963	49.645	55.476
12.461	13.565	15.308	16.928	18.939	22.657	27.336		32.620	37.916	41.337	48.278	50.993	56.892
13.121	14.256	16.047	17.708	19.768	23.567	28.336		33.711	39.087	42.557	49.588	52.336	58.301
13.787	14.953	16.791	18.493	20.599	24.478	29.336		34.800	40.256	43.773	50.892	53.672	59.703
20.707	22.164	24.433	26.509	29.051	33.660	39.335		45.616	51.805	55.758	63.691	66.766	73.402
27.991	29.707	32.357	34.764	3	7.68	42.942	49.335		56.334	63.167	67.505	76.	154	79.490	86.661
35.534	3	7.48	40.482	43.188	46.459	52.294	59.335		66.981	74.397	79.082	88.379	91.952	99.607
43.275	45.442	48.758	51.739	55.329	61.698	69.334		7				7.57	85.527	90.531	100.425	104.215	112.317
51.172	53.540	57.153	60.391	64.278	71.145	79.334		88.130	96.578	101.879	112.329	116.321	124.839
59.196	61.754	65.647	69.126	73.291	80.625	89.334		98.650	107.565	113.145	124.116	128.299	137.208
67.328	70.065	74.222	77.929	82.358	90.133	99.334		109.141	118.498	124.342	135.807	140.169	149.449
Right Tailed Distribution, df = degrees of freedom = (#Rows – 1) x (#Columns – 1)
Chi Square Table of Probabilities:

VILLANOVA UNIVERSITY

PROCESS MAP TEMPLATE

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PROCESS ANALYSIS COMPLETED BY DEPARTMENT(S)

K E Y COPY AND PASTE
BLANK ICONS
BELOW

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DATE COMPLETED

STEP
START / END
INPUT / OUTPUT
DOCUMENT
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MEASURES

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OBJECTIVE /	PRIMARY MEANS /	SECONDARY MEANS /	TERTIARY MEANS /	FOURTH LEVEL /
VISION	LONG-TERM	SHORT-TERM	TARGETS

DATA
DATA
DATA
DATA
DATA
DATA
DATA
DATA
DATA
DATA
DATA
DATA
DATA
DATA
DATA
DATA
DATA
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https://goo.gl/PpiO3g

CORRELATION COEFFICIENT

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Description

FOR CORRELATION COEFFICIENT USE”PEARSON” FUNCTION IN THE “

For PEARSON formula: FORMULAS> MORE FUNCTIONS > PEARSON

FOR HELP: USE “HELP” ACROSS THE EXCEL TOOL BAR. TYPE “CORRELATION COEFICIENT” > SELECT PEARSON

Returns the Pearson product moment correlation coefficient, r, a dimensionless index that ranges from -1.0 to 1.0 inclusive and reflects the extent of a linear relationship between two data sets.

PEARSON(array1, array2) : Array 1 requires a set of Independent Values; Array2 requires a set of Dependent Values

Source: Khan Academy

The correlation coefficient r measures the direction and strength of a linear relationship.

Here are some facts about r:

•It always has a value between -1and 1.

•Strong positive linear relationships have values of r closer to 1.

•Strong negative linear relationships have values of r closer to -1.

•Weaker relationships have values of r closer to 0.

https://www.khanacademy.org/math/probability/scatterplots-a1/creating-interpreting-scatterplots/v/correlation-coefficient-intuition-examples

APPENDIX B

using Pareto Diagram. These focus areas will then be monitored as defined in Data Collection Plan.

ISSUE COUNT

17
22

Based on VOC data to be used to construct CTQ’s. Project Team will identify key focus areas in Doctor’s	Office
Time the Doctor was spending with Patients
Number of times Dr arrives late
Proper Medical Devices not Available
Number of times patient is left in the hallway
Rooms Available at Doctor’s Office
Number of times staff arrive late
Staffing of Doctor’s Office
Number of times scheduling changes were made for patient testing
Number of times patient had to be rescheduled for Dr visit
	Arrival Time of Patients

APPENDIX C

DATE

Arrival Time of Patients

Office

172

10.82

169 34

10.86

0.546 177 23

7.65

170

174

10.85

175

10.86 7.6

167

10.87

171

168 27

10.8

0.5522 172 31

7.52

168

10.89

163 27

10.81 7.52

174 61

10.9 7.61

169 17

10.87

171

10.86 7.57

172 50

10.85 7.59

172 11

10.85 7.55

168 53

10.86 7.61 0.553 169 18

10.86 7.54 0.55 166

7.57

172 27

10.89

168 36

7.63

174 40

7.5 0.541 175 30

10.86 7.58

164 23

10.9 7.55

173 15

10.83 7.51

168 15

10.82 7.5

170 35

10.87 7.59

173

7.58 0.541 170 25

173 42

7.48

167

7.57 0.5532 169 23

10.7

172 53

10.67 7.53

165 50

10.65 7.6

170

10.6 7.49

169 41

7.65

170 7

7.55

165 31

7.55 0.5566 172 18

7.51

168 53

10.66 7.49

173 34

7.49

172 37

7.49

170

7.56 0.5491 176 19

10.67 7.59

175 26

10.62

0.5491 170 13

10.62 7.58

169 18

10.63 7.55 0.556 177 36

10.65 7.47 0.5428 178 7

7.63

172 34

10.68 7.47 0.5531 171 28

10.63 7.68

171 44

10.68 7.55

171 18

7.47

177 23

10.59 7.59

172 17

10.64 7.57

170 25

10.64 7.53

169 15

10.68 7.58

164 23

10.6 7.6

174

0.56 180 60

10.5 7.45 0.54 165 0

Target

7.55 0.55 170 20

Data set to be used to construct 5		Histogram
Proper Medical Devices												N/A	Rooms Available at Dr.	Time Dr. Spends with Patients
Staffing at Dr. Office
7/1/19	10.82		7.45	0.5502
7/2/19								7.55		0.552
7/3/19	7.67
7/4/19				10.87	0.5462
7/5/19	10.84	7.62					0.549
7/6/19					7.59				0.548
7/7/19		0.5428
7/8/19			7.52		0.5532
7/9/19			10.89					7.49			0.547
7/10/19		7.54
7/11/19		10.81	0.5494
7/12/19			7.61		0.551
7/13/19	0.5509
7/14/19			0.541
7/15/19			7.53	0.5518
7/16/19	0.5523
7/17/19	0.5415
7/18/19	0.5477
7/19/19
7/20/19
7/21/19		10.83	0.5437
7/22/19			7.51	0.5463
7/23/19		10.7			0.556
7/24/19	10.78
7/25/19	0.5542
7/26/19	0.5569
7/27/19	0.5432
7/28/19	0.5487
7/29/19	0.5537
7/30/19	10.88
7/31/19			10.67	7.64	0.5554
8/1/19	10.72	0.5521
8/2/19			10.65
8/3/19	7.46	0.5563
8/4/19	0.5508
8/5/19	0.5527
8/6/19	0.5546
8/7/19		10.66	0.5478
8/8/19	10.61	0.5468
8/9/19	10.69
8/10/19	10.71		0.5531
8/11/19	0.5482
8/12/19			10.64	0.5473
8/13/19			10.62	0.5442
8/14/19			10.63
8/15/19	0.5596
8/16/19				7.47
8/17/19	0.5507
8/18/19
8/19/19
8/20/19				10.68	0.5488
8/21/19
8/22/19	0.5483
8/23/19	0.5431
8/24/19	10.58					0.545
8/25/19	0.5392
8/26/19	0.5512
8/27/19	0.5465
8/28/19	0.5479
8/29/19	0.5452
Upper Spec	7.66
Lower Spec
10.75

APPENDIX D

16
16
17
37
47
32
48
21
18
75
15
17
13
47
11
47

17
20

19
16
26
17

15
17
48
16
44
48
45
50
49

17
22
10
18

14
80
6
49
48
47
48
52

48
47
20

47
50

47
20
50
35
21
46
48
20
64
16
44

Data represents Wait Time in minutes beyond their scheduled Appointment Time for the last 70 patients. Use to create Stem and Leaf Plots.

PATIENT WAITING TIME

APPENDIX E

Data

0.009
0.01
0.011

0.011
0.01
0.011
0.011

0.013
0.008
0.012

0.01
0.013

0.014

0.012
0.009
0.014
0.011

0.015

0.011
0.012
0.015
0.011
0.011
0.012
0.008

Data set for determining performance for Medical Assistant #2. The historical mean for Medical Assistant #1 was .0126.

MEDICAL ASSISTANT #1 DATA
HISTORIC MEAN

0.0126

APPENDIX F

154 0.554
153 0.553

0.552

152 0.551
151 0.549

151 0.549

151 0.548

151 0.548
151 0.548

151 0.547

151 0.547
151 0.547
151 0.547
151 0.547
151 0.547

151 0.546
150 0.546

150 0.546
150 0.546
150 0.546
150 0.546

150 0.545

150 0.545
150 0.545

149 0.545

This is the data set for evaluating Correlation between Room Availability and Patient Arrival
Room # Availability	Patient Arrival Time
	152

Tool to Use

Tool

What does it do? Why use? When use?

Data Type

P < .05 indicates Picture

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1-Sample t-Test Compares mean to target The 1-sample t-test is useful in identifying a significant difference between a sample mean and a specified value when the difference is not readily apparent from graphical tools. Using the 1-sample t-test to compare data gathered before process improvements and after is a way to prove that the mean has actually shifted. The 1-sample t-test is used with continuous data any time you need to compare a sample mean to a specified value. This is useful when you need to make judgments about a process based on a sample output from that process. Continuous X & Y Not equal

1
1-Way ANOVA ANOVA tests to see if the difference between the means of each level is significantly more than the variation within each level. 1-way ANOVA is used when two or more means (a single factor with three or more levels) must be compared with each other. One-way ANOVA is useful for identifying a statistically significant difference between means of three or more levels of a factor. Use 1-way ANOVA when you need to compare three or more means (a single factor with three or more levels) and determine how much of the total observed variation can be explained by the factor. Continuous Y, Discrete

Xs At least one group of data is different than at least one other group.

0
2-Sample t-Test A statistical test used to detect differences between means of two populations. The 2-sample t-test is useful for identifying a significant difference between means of two levels (subgroups) of a factor. It is also extremely useful for identifying important Xs for a project Y. When you have two samples of continuous data, and you need to know if they both come from the same population or if they represent two different populations

Continuous X & Y

There is a difference in the means

0
ANOVA GLM ANOVA General Linear Model (GLM) is a statistical tool used to test for differences in means. ANOVA tests to see if the difference between the means of each level is significantly more than the variation within each level. ANOVA GLM is used to test the effect of two or more factors with multiple levels, alone and in combination, on a dependent variable. The General Linear Model allows you to learn one form of ANOVA that can be used for all tests of mean differences involving two or more factors or levels. Because ANOVA GLM is useful for identifying the effect of two or more factors (independent variables) on a dependent variable, it is also extremely useful for identifying important Xs for a project Y. ANOVA GLM also yields a percent contribution that quantifies the variation in the response (dependent variable) due to the individual factors and combinations of factors. You can use ANOVA GLM any time you need to identify a statistically significant difference in the mean of the dependent variable due to two or more factors with multiple levels, alone and in combination. ANOVA GLM also can be used to quantify the amount of variation in the response that can be attributed to a specific factor in a designed experiment. Continuous Y & all X’s

At least one group of data is different than at least one other group. 0
Benchmarking Benchmarking is an improvement tool whereby a company: Measures its performance or process against other companies’ best in class practices, Determines how those companies achieved their performance levels, Uses the information to improve its own performance. Benchmarking is an important tool in the improvement of your process for several reasons. First, it allows you to compare your relative position for this product or service against industry leaders or other companies outside your industry who perform similar functions. Second, it helps you identify potential Xs by comparing your process to the benchmarked process. Third, it may encourage innovative or direct applications of solutions from other businesses to your product or process. And finally, benchmarking can help to build acceptance for your project’s results when they are compared to benchmark data obtained from industry leaders. Benchmarking can be done at any point in the Six Sigma process when you need to develop a new process or improve an existing one

all N/A 1
Best Subsets Tells you the best X to use when you’re comparing multiple X’s in regression assessment. Best Subsets is an efficient way to select a group of “best subsets” for further analysis by selecting the smallest subset that fulfills certain statistical criteria. The subset model may actually estimate the regression coefficients and predict future responses with smaller variance than the full model using all predictors Typically used before or after a multiple-regression analysis. Particularly useful in determining which X combination yields the best R-sq value.

Continuous X & Y N/A 0
Binary Logistic Regression

Binary logistic regression is useful in two important applications: analyzing the differences among discrete Xs and modeling the relationship between a discrete binary Y and discrete and/or continuous Xs. Binary logistic regression is useful in two applications: analyzing the differences among discrete Xs and modeling the relationship between a discrete binary Y and discrete and/or continuous Xs. Binary logistic regression can be used to model the relationship between a discrete binary Y and discrete and/or continuous Xs. The predicted values will be probabilities p(d) of an event such as success or failure-not an event count. The predicted values will be bounded between zero and one (because they are probabilities). Generally speaking, logistic regression is used when the Ys are discrete and the Xs are continuous Defectives Y / Continuous & Discrete X The goodness-of-fit tests, with p-values ranging from 0.312 to 0.724, indicate that there is insufficient evidence for the model not fitting the data adequately. If the p-value is less than your accepted a level, the test would indicate sufficient evidence for a conclusion of an inadequate fit.

0
Box Plot A box plot is a basic graphing tool that displays the centering, spread, and distribution of a continuous data set. In simplified terms, it is made up of a box and whiskers (and occasional outliers) that correspond to each fourth, or quartile, of the data set. The box represents the second and third quartiles of data. The line that bisects the box is the median of the entire data set-50% of the data points fall below this line and 50% fall above it. The first and fourth quartiles are represented by “whiskers,” or lines that extend from both ends of the box. a box plot can help you visualize the centering, spread, and distribution of your data quickly. It is especially useful to view more than one box plot simultaneously to compare the performance of several processes such as the price quote cycle between offices or the accuracy of component placement across several production lines. A box plot can help identify candidates for the causes behind your list of potential Xs. It also is useful in tracking process improvement by comparing successive plots generated over time You can use a box plot throughout an improvement project, although it is most useful in the Analyze phase. In the Measure phase you can use a box plot to begin to understand the nature of a problem. In the Analyze phase a box plot can help you identify potential Xs that should be investigated further. It also can help eliminate potential Xs. In the Improve phase you can use a box plot to validate potential improvements

Continuous X & Y N/A 1
Box-Cox Transformation

used to find the mathematical function needed to translate a continuous but nonnormal distribution into a normal distribution. After you have entered your data,

Minitab

tells you what mathematical function can be applied to each of your data points to bring your data closer to a normal distribution.

Many tools require that data be normally distributed to produce accurate results. If the data set is not normal, this may reduce significantly the confidence in the results obtained. If your data is not normally distributed, you may encounter problems in Calculating Z values with continuous data. You could calculate an inaccurate representation of your process capability. In constructing control charts…. Your process may appear more or less in control than it really is. In Hypothesis testing… As your data becomes less normal, the results of your tests may not be valid.

Continuous X & Y N/A 1

Brainstorming Brainstorming is a tool that allows for open and creative thinking. It encourages all team members to participate and to build on each other’s creativity Brainstorming is helpful because it allows your team to generate many ideas on a topic creatively and efficiently without criticism or judgment. Brainstorming can be used any time you and your team need to creatively generate numerous ideas on any topic. You will use brainstorming many times throughout your project whenever you feel it is appropriate. You also may incorporate brainstorming into other tools, such as QFD, tree diagrams, process mapping, or FMEA.

all N/A 0
c Chart a graphical tool that allows you to view the actual number of defects in each subgroup. Unlike continuous data control charts, discrete data control charts can monitor many product quality characteristics simultaneously. For example, you could use a c chart to monitor many types of defects in a call center process (like hang ups, incorrect information given, disconnections) on a single chart when the subgroup size is constant. The c chart is a tool that will help you determine if your process is in control by determining whether special causes are present.

The presence of special cause variation indicates that factors are influencing the output of your process. Eliminating the influence of these factors will improve the performance of your process and bring your process into control Control phase to verify that your process remains in control after the sources of special cause variation have been removed. The c chart is used for processes that generate discrete data. The c chart monitors the number of defects per sample taken from a process. You should record between 5 and 10 readings, and the sample size must be constant. The c chart can be used in both low- and high- volume environments Continuous X,

Attribute

N/A 0
CAP Includes/Excludes A group exercise used to establish scope and facilitate discussion. Effort focuses on delineating project boundaries. Encourages group participation. Increases individual involvement and understanding of team efforts. Prevents errant team efforts in later project stages (waste). Helps to orient new team members.

Define all N/A 0
CAP Stakeholder Analysis Confirms management or stakeholder acceptance and prioritization of Project and team efforts. Helps to eliminate low priority projects. Insure management support and compatibility with business goals.

Define all N/A 0
Capability Analysis

Capability analysis is a MinitabTM tool that visually compares actual process performance to the performance standards. The capability analysis output includes an illustration of the data and several performance statistics. The plot is a histogram with the performance standards for the process expressed as upper and lower specification limits (USL and LSL). A normal distribution curve is calculated from the process mean and standard deviation; this curve is overlaid on the histogram. Beneath this graphic is a table listing several key process parameters such as mean, standard deviation, capability indexes, and parts per million (ppm) above and below the specification limits. When describing a process, it is important to identify sources of variation as well as process segments that do not meet performance standards. Capability analysis is a useful tool because it illustrates the centering and spread of your data in relation to the performance standards and provides a statistical summary of process performance. Capability analysis will help you describe the problem and evaluate the proposed solution in statistical terms. Capability analysis is used with continuous data whenever you need to compare actual process performance to the performance standards. You can use this tool in the Measure phase to describe process performance in statistical terms. In the Improve phase, you can use capability analysis when you optimize and confirm your proposed solution. In the Control phase, capability analysis will help you compare the actual improvement of your process to the performance standards.

Continuous X & Y N/A 1
Cause and Effect Diagram

A cause and effect diagram is a visual tool that logically organizes possible causes for a specific problem or effect by graphically displaying them in increasing detail. It is sometimes called a fishbone diagram because of its fishbone shape. This shape allows the team to see how each cause relates to the effect. It then allows you to determine a classification related to the impact and ease of addressing each cause A cause and effect diagram allows your team to explore, identify, and display all of the possible causes related to a specific problem. The diagram can increase in detail as necessary to identify the true root cause of the problem. Proper use of the tool helps the team organize thinking so that all the possible causes of the problem, not just those from one person’s viewpoint, are captured. Therefore, the cause and effect diagram reflects the perspective of the team as a whole and helps foster consensus in the results because each team member can view all the inputs You can use the cause and effect diagram whenever you need to break an effect down into its root causes. It is especially useful in the Measure, Analyze, and Improve phases of the DMAIC process

all N/A 0
Chi Square–Test of Independence The chi square-test of independence is a test of association (nonindependence) between discrete variables. It is also referred to as the test of association. It is based on a mathematical comparison of the number of observed counts against the expected number of counts to determine if there is a difference in output counts based on the input category. Example: The number of units failing inspection on the first shift is greater than the number of units failing inspection on the second shift. Example: There are fewer defects on the revised application form than there were on the previous application form The chi square-test of independence is useful for identifying a significant difference between count data for two or more levels of a discrete variable Many statistical problem statements and performance improvement goals are written in terms of reducing DPMO/DPU. The chi square-test of independence applied to before and after data is a way to prove that the DPMO/DPU have actually been reduced. When you have discrete Y and X data (nominal data in a table-of-total-counts format, shown in fig. 1) and need to know if the Y output counts differ for two or more subgroup categories (Xs), use the chi square test. If you have raw data (un-totaled), you need to form the contingency table. Use Stat > Tables > Cross Tabulation and check the Chisquare analysis box. discrete (category or count) At least one group is statistically different.

0
Control Charts

Control charts are time-ordered graphical displays of data that plot process variation over time. Control charts are the major tools used to monitor processes to ensure they remain stable. Control charts are characterized by A centerline, which represents the process average, or the middle point about which plotted measures are expected to vary randomly. Upper and lower control limits, which define the area three standard deviations on either side of the centerline. Control limits reflect the expected range of variation for that process. Control charts determine whether a process is in control or out of control. A process is said to be in control when only common causes of variation are present. This is represented on the control chart by data points fluctuating randomly within the control limits. Data points outside the control limits and those displaying nonrandom patterns indicate special cause variation. When special cause variation is present, the process is said to be out of control. Control charts identify when special cause is acting on the process but do not identify what the special cause is. There are two categories of control charts, characterized by type of data you are working with: continuous data control charts and discrete data control charts. Control charts serve as a tool for the ongoing control of a process and provide a common language for discussing process performance. They help you understand variation and use that knowledge to control and improve your process. In addition, control charts function as a monitoring system that alerts you to the need to respond to special cause variation so you can put in place an immediate remedy to contain any damage. In the Measure phase, use control charts to understand the performance of your process as it exists before process improvements. In the Analyze phase, control charts serve as a troubleshooting guide that can help you identify sources of variation (Xs). In the Control phase, use control charts to : 1. Make sure the vital few Xs remain in control to sustain the solution – 2. Show process performance after full-scale implementation of your solution. You can compare the control chart created in the Control phase with that from the Measure phase to show process improvement -3. Verify that the process remains in control after the sources of special cause variation have been removed

all N/A 0
Data Collection Plan

Failing to establish a data collection plan can be an expensive mistake in a project. Without a plan, data collection may be haphazard, resulting in insufficient, unnecessary, or inaccurate information. This is often called “bad” data. A data collection plan provides a basic strategy for collecting accurate data efficiently Any time data is needed, you should draft a data collection plan before beginning to collect it.

all N/A 0
Design Analysis Spreadsheet The design analysis spreadsheet is an MS-Excel™ workbook that has been designed to perform partial derivative analysis and root sum of squares analysis. The design analysis spreadsheet provides a quick way to predict the mean and standard deviation of an output measure (Y), given the means and standard deviations of the inputs (Xs). This will help you develop a statistical model of your product or process, which in turn will help you improve that product or process. The partial derivative of Y with respect to X is called the sensitivity of Y with respect to X or the sensitivity coefficient of X. For this reason, partial derivative analysis is sometimes called sensitivity analysis. The design analysis spreadsheet can help you improve, revise, and optimize your design. It can also: Improve a product or process by identifying the Xs which have the most impact on the response. Identify the factors whose variability has the highest influence on the response and target their improvement by adjusting tolerances. Identify the factors that have low influence and can be allowed to vary over a wider range. Be used with the Solver** optimization routine for complex functions (Y equations) with many constraints. ** Note that you must unprotect the worksheet before using Solver. Be used with process simulation to visualize the response given a set of constrained Partial derivative analysis is widely used in product design, manufacturing, process improvement, and commercial services during the concept design, capability assessment, and creation of the detailed design. When the Xs are known to be highly non-normal (and especially if the Xs have skewed distributions), Monte Carlo analysis may be a better choice than partial derivative analysis. Unlike root sum of squares (RSS) analysis, partial derivative analysis can be used with nonlinear transfer functions. Use partial derivative analysis when you want to predict the mean and standard deviation of a system response (Y), given the means and standard deviations of the inputs (Xs), when the transfer function Y=f(X1, X2, ., Xn) is known. However, the inputs (Xs) must be independent of one another (i.e., not correlated).

Continuous X & Y N/A 0
Design of Experiment (DOE) Design of experiment (DOE) is a tool that allows you to obtain information about how factors (Xs), alone and in combination, affect a process and its output (Y). Traditional experiments generate data by changing one factor at a time, usually by trial and error. This approach often requires a great many runs and cannot capture the effect of combined factors on the output. By allowing you to test more than one factor at a time-as well as different settings for each factor-DOE is able to identify all factors and combinations of factors that affect the process Y. DOE uses an efficient, cost-effective, and methodical approach to collecting and analyzing data related to a process output and the factors that affect it. By testing more than one factor at a time, DOE is able to identify all factors and combinations of factors that affect the process Y In general, use DOE when you want to Identify and quantify the impact of the vital few Xs on your process output Describe the relationship between Xs and a Y with a mathematical model Determine the best configuration

Continuous Y & all X’s N/A 0
Design Scorecards Design scorecards are a means for gathering data, predicting final quality, analyzing drivers of poor quality, and modifying design elements before a product is built. This makes proactive corrective action possible, rather than initiating reactive quality efforts during pre-production. Design scorecards are an MS-Excel™ workbook that has been designed to automatically calculate Z values for a product based on user-provided inputs of for all the sub-processes and parts that make up the product. Design scorecards have six basic components: 1 Top-level scorecard-used to report the rolled-up ZST prediction 2. Performance worksheet-used to estimate defects caused by lack of design margin 3. Process worksheet-used to estimate defects in process as a result of the design configuration 4.Parts worksheet-used to estimate defects due to incoming materials Software worksheet-used to estimate defects in software 5. Software worksheet-used to estimate defects in software 6. Reliability worksheet-used to estimate defects due to reliability Design scorecards can be used anytime that a product or process is being designed or modified and it is necessary to predict defect levels before implementing a process. They can be used in either the DMADV or DMAIC processes.

all N/A 0
Discrete Data Analysis Method The Discrete Data Analysis (DDA) method is a tool used to assess the variation in a measurement system due to reproducibility, repeatability, and/or accuracy. This tool applies to discrete data only. The DDA method is an important tool because it provides a method to independently assess the most common types of measurement variation-repeatability, reproducibility, and/or accuracy. Completing the DDA method will help you to determine whether the variation from repeatability, reproducibility, and/or accuracy in your measurement system is an acceptably small portion of the total observed variation. Use the DDA method after the project data collection plan is formulated or modified and before the project data collection plan is finalized and data is collected. Choose the DDA method when you have discrete data and you want to determine if the measurement variation due to repeatability, reproducibility, and/or accuracy is an acceptably small portion of the total observed variation

discrete (category or count) N/A 0
Discrete Event

Simulation

(Process ModelTM) Discrete event simulation is conducted for processes that are dictated by events at distinct points in time; each occurrence of an event impacts the current state of the process. Examples of discrete events are arrivals of phone calls at a call center. Timing in a discrete event model increases incrementally based on the arrival and departure of the inputs or resources ProcessModelTM is a process modeling and analysis tool that accelerates the process improvement effort. It combines a simple flowcharting function with a simulation process to produce a quick and easy tool for documenting, analyzing, and improving business processes. Discrete event simulation is used in the Analyze phase of a DMAIC project to understand the behavior of important process variables. In the Improve phase of a DMAIC project, discrete event simulation is used to predict the performance of an existing process under different conditions and to test new process ideas or alternatives in an isolated environment. Use ProcessModelTM when you reach step 4, Implement, of the 10-step simulation process.

Continuous Y, Discrete Xs N/A 0
Dot Plot Quick graphical comparison of two or more processes’ variation or spread

Quick graphical comparison of two or more processes’ variation or spread

Comparing two or more processes’ variation or spread

Continuous Y, Discrete Xs N/A
Failure Mode and Effects Analysis A means / method to Identify ways a process can fail, estimate the risks of those failures, evaluate a control plan, prioritize actions related to the process Complex or new processes. Customers are involved.

all N/A 0
Gage R & R–ANOVA Method Gage R&R-ANOVA method is a tool used to assess the variation in a measurement system due to reproducibility and/or repeatability. An advantage of this tool is that it can separate the individual effects of repeatability and reproducibility and then break down reproducibility into the components “operator” and “operator by part.” This tool applies to continuous data only. Gage R&R-ANOVA method is an important tool because it provides a method to independently assess the most common types of measurement variation – repeatability and reproducibility. This tool will help you to determine whether the variation from repeatability and/or reproducibility in your measurement system is an acceptably small portion of the total observed variation. Measure -Use Gage R&R-ANOVA method after the project data collection plan is formulated or modified and before the project data collection plan is finalized and data is collected. Choose the ANOVA method when you have continuous data and you want to determine if the measurement variation due to repeatability and/or reproducibility is an acceptably small portion of the total observed variation.

Continuous X & Y 0
Gage R & R–Short Method Gage R&R-Short Method is a tool used to assess the variation in a measurement system due to the combined effect of reproducibility and repeatability. An advantage of this tool is that it requires only two operators and five samples to complete the analysis. A disadvantage of this tool is that the individual effects of repeatability and reproducibility cannot be separated. This tool applies to continuous data only Gage R&R-Short Method is an important tool because it provides a quick method of assessing the most common types of measurement variation using only five parts and two operators. Completing the Gage R&R-Short Method will help you determine whether the combined variation from repeatability and reproducibility in your measurement system is an acceptably small portion of the total observed variation. Use Gage R&R-Short Method after the project data collection plan is formulated or modified and before the project data collection plan is finalized and data is collected. Choose the Gage R&R-Short Method when you have continuous data and you believe the total measurement variation due to repeatability and reproducibility is an acceptably small portion of the total observed variation, but you need to confirm this belief. For example, you may want to verify that no changes occurred since a previous Gage R&R study. Gage R&R-Short Method can also be used in cases where sample size is limited.

Continuous X & Y 0
GRPI GRPI is an excellent tool for organizing newly formed teams. It is valuable in helping a group of individuals work as an effective team-one of the key ingredients to success in a DMAIC project GRPI is an excellent team-building tool and, as such, should be initiated at one of the first team meetings. In the DMAIC process, this generally happens in the Define phase, where you create your charter and form your team. Continue to update your GRPI checklist throughout the DMAIC process as your project unfolds and as your team develops

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Histogram

A histogram is a basic graphing tool that displays the relative frequency or occurrence of data values-or which data values occur most and least frequently. A histogram illustrates the shape, centering, and spread of data distribution and indicates whether there are any outliers. The frequency of occurrence is displayed on the y-axis, where the height of each bar indicates the number of occurrences for that interval (or class) of data, such as 1 to 3 days, 4 to 6 days, and so on. Classes of data are displayed on the x-axis. The grouping of data into classes is the distinguishing feature of a histogram it is important to identify and control all sources of variation. Histograms allow you to visualize large quantities of data that would otherwise be difficult to interpret. They give you a way to quickly assess the distribution of your data and the variation that exists in your process. The shape of a histogram offers clues that can lead you to possible Xs. For example, when a histogram has two distinct peaks, or is bimodal, you would look for a cause for the difference in peaks. Histograms can be used throughout an improvement project. In the Measure phase, you can use histograms to begin to understand the statistical nature of the problem. In the Analyze phase, histograms can help you identify potential Xs that should be investigated further. They can also help eliminate potential Xs. In the Improve phase, you can use histograms to characterize and confirm your solution. In the Control phase, histograms give you a visual reference to help track and maintain your improvements.

Continuous Y & all X’s N/A 1
Homogeneity of Variance Homogeneity of variance is a test used to determine if the variances of two or more samples are different, or not homogeneous. The homogeneity of variance test is a comparison of the variances (sigma, or standard deviations) of two or more distributions. While large differences in variance between a small number of samples are detectable with graphical tools, the homogeneity of variance test is a quick way to reliably detect small differences in variance between large numbers of samples. There are two main reasons for using the homogeneity of variance test:1. A basic assumption of many statistical tests is that the variances of the different samples are equal. Some statistical procedures, such as 2-sample t-test, gain additional test power if the variances of the two samples can be considered equal.2. Many statistical problem statements and performance improvement goals are written in terms of “reducing the variance.” Homogeneity of variance tests can be performed on before and after data, as a way to prove that the variance has been reduced.

Continuous Y, Discrete Xs

(Use Levene’s Test) At least one group of data is different than at least one other group

1
I-MR Chart The I-MR chart is a tool to help you determine if your process is in control by seeing if special causes are present.

The Measure phase to separate common causes of variation from special causes The Analyze and Improve phases to ensure process stability before completing a hypothesis test The Control phase to verify that the process remains in control after the sources of special cause variation have been removed

Continuous X & Y N/A 1
Kano Analysis Kano analysis is a customer research method for classifying customer needs into four categories; it relies on a questionnaire filled out by or with the customer. It helps you understand the relationship between the fulfillment or nonfulfillment of a need and the satisfaction or dissatisfaction experienced by the customer. The four categories are 1. delighters, 2. Must Be elements, 3. One – dimensionals, & 4. Indifferent elements. There are two additional categories into which customer responses to the Kano survey can fall: they are reverse elements and questionable result. –The categories in Kano analysis represent a point in time, and needs are constantly evolving. Often what is a delighter today can become simply a must-be over time. Kano analysis provides a systematic, data-based method for gaining deeper understanding of customer needs by classifying them Use Kano analysis after a list of potential needs that have to be satisfied is generated (through, for example, interviews, focus groups, or observations). Kano analysis is useful when you need to collect data on customer needs and prioritize them to focus your efforts.

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Kruskal-Wallis Test Compare two or more means with unknown distributions non-parametric (measurement or count) At least one mean is different

0
Matrix Plot Tool used for high-level look at relationships between several parameters. Matrix plots are often a first step at determining which X’s contribute most to your Y. Matrix plots can save time by allowing you to drill-down into data and determine which parameters best relate to your Y. You should use matrix plots early in your analyze phase.

Continuous Y & all X’s N/A
Mistake Proofing Mistake-proofing devices prevent defects by preventing errors or by predicting when errors could occur. Mistake proofing is an important tool because it allows you to take a proactive approach to eliminating errors at their source before they become defects. You should use mistake proofing in the Measure phase when you are developing your data collection plan, in the Improve phase when you are developing your proposed solution, and in the Control phase when developing the control plan. Mistake proofing is appropriate when there are :1. Process steps where human intervention is required2. Repetitive tasks where physical manipulation of objects is required3. Steps where errors are known to occur4. Opportunities for predictable errors to occur

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Monte Carlo Analysis Monte Carlo analysis is a decision-making and problem-solving tool used to evaluate a large number of possible scenarios of a process. Each scenario represents one possible set of values for each of the variables of the process and the calculation of those variables using the transfer function to produce an outcome Y. By repeating this method many times, you can develop a distribution for the overall process performance. Monte Carlo can be used in such broad areas as finance, commercial quality, engineering design, manufacturing, and process design and improvement. Monte Carlo can be used with any type of distribution; its value comes from the increased knowledge we gain in terms of variation of the output Performing a Monte Carlo analysis is one way to understand the variation that naturally exists in your process. One of the ways to reduce defects is to decrease the output variation. Monte Carlo focuses on understanding what variations exist in the input Xs in order to reduce the variation in output Y.

Continuous Y & all X’s N/A 0
Multi-Generational Product/Process Planning Multigenerational product/process planning (MGPP) is a procedure that helps you create, upgrade, leverage, and maintain a product or process in a way that can reduce production costs and increase market share. A key element of MGPP is its ability to help you follow up product/process introduction with improved, derivative versions of the original product. Most products or processes, once introduced, tend to remain unchanged for many years. Yet, competitors, technology, and the marketplace-as personified by the ever more demanding consumer-change constantly. Therefore, it makes good business sense to incorporate into product/process design a method for anticipating and taking advantage of these changes. You should follow an MGPP in conjunction with your business’s overall marketing strategy. The market process applied to MGPP usually takes place over three or more generations. These generations cover the first three to five years of product/process development and introduction.

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Multiple Regression method that enables you to determine the relationship between a continuous process output (Y) and several factors (Xs). Multiple regression will help you to understand the relationship between the process output (Y) and several factors (Xs) that may affect the Y. Understanding this relationship allows you to1. Identify important Xs2. Identify the amount of variation explained by the model3. Reduce the number of Xs prior to design of experiment (DOE )4. Predict Y based on combinations of X values5. Identify possible nonlinear relationships such as a quadratic (X12) or an interaction (X1X2)The output of a multiple regression analysis may demonstrate the need for designed experiments that establish a cause and effect relationship or identify ways to further improve the process. You can use multiple regression during the Analyze phase to help identify important Xs and during the Improve phase to define the optimized solution. Multiple regression can be used with both continuous and discrete Xs. If you have only discrete Xs, use ANOVA-GLM. Typically you would use multiple regression on existing data. If you need to collect new data, it may be more efficient to use a DOE.

Continuous X & Y

A correlation is detected

0
Multi-Vari Chart A multi-vari chart is a tool that graphically displays patterns of variation. It is used to identify possible Xs or families of variation, such as variation within a subgroup, between subgroups, or over time A multi-vari chart enables you to see the effect multiple variables have on a Y. It also helps you see variation within subgroups, between subgroups, and over time. By looking at the patterns of variation, you can identify or eliminate possible Xs

Continuous Y & all X’s N/A 0
Normal Probability Plot Allows you to determine the normality of your data. To determine the normality of data. To see if multiple X’s exist in your data. cont (measurement) Data does not follow a normal distribution

1
Normality Test

A normality test is a statistical process used to determine if a sample, or any group of data, fits a standard normal distribution. A normality test can be done mathematically or graphically. Many statistical tests (tests of means and tests of variances) assume that the data being tested is normally distributed. A normality test is used to determine if that assumption is valid. There are two occasions when you should use a normality test:
1. When you are first trying to characterize raw data, normality testing is used in conjunction with graphical tools such as histograms and box plots.
2. When you are analyzing your data, and you need to calculate basic statistics such as Z values or employ statistical tests that assume normality, such as t-test and ANOVA.

cont (measurement) not normal 0
n

p Chart a graphical tool that allows you to view the actual number of defectives and detect the presence of special causes. The np chart is a tool that will help you determine if your process is in control by seeing if special causes are present. The presence of special cause variation indicates that factors are influencing the output of your process. Eliminating the influence of these factors will improve the performance of your process and bring your process into control. You will use an np chart in the Control phase to verify that the process remains in control after the sources of special cause variation have been removed. The np chart is used for processes that generate discrete data. The np chart is used to graph the actual number of defectives in a sample. The sample size for the np chart is constant, with between 5 and 10 defectives per sample on the average.

Defectives Y / Continuous & Discrete X N/A 1
Out-of-the-Box Thinking Out-of-the-box thinking is an approach to creativity based on overcoming the subconscious patterns of thinking that we all develop. Many businesses are successful for a brief time due to a single innovation, while continued success is dependent upon continued innovation Root cause analysis and new product / process development

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p Chart

a graphical tool that allows you to view the proportion of defectives and detect the presence of special causes. The p chart is used to understand the ratio of nonconforming units to the total number of units in a sample. The p chart is a tool that will help you determine if your process is in control by determining whether special causes are present. The presence of special cause variation indicates that factors are influencing the output of your process. Eliminating the influence of these factors will improve the performance of your process and bring your process into control You will use a p chart in the Control phase to verify that the process remains in control after the sources of special cause variation have been removed. The p chart is used for processes that generate discrete data. The sample size for the p chart can vary but usually consists of 100 or more

Defectives Y / Continuous & Discrete X N/A 1
Pareto Chart

A Pareto chart is a graphing tool that prioritizes a list of variables or factors based on impact or frequency of occurrence. This chart is based on the Pareto principle, which states that typically 80% of the defects in a process or product are caused by only 20% of the possible causes . It is easy to interpret, which makes it a convenient communication tool for use by individuals not familiar with the project. The Pareto chart will not detect small differences between categories; more advanced statistical tools are required in such cases. In the Define phase to stratify

Voice of the Customer

data…In the Measure phase to stratify data collected on the project Y…..In the Analyze phase to assess the relative impact or frequency of different factors, or Xs

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Process Mapping Process mapping is a tool that provides structure for defining a process in a simplified, visual manner by displaying the steps, events, and operations (in chronological order) that make up a process As you examine your process in greater detail, your map will evolve from the process you “think” exists to what “actually” exists. Your process map will evolve again to reflect what “should” exist-the process after improvements are made. In the Define phase, you create a high-level process map to get an overview of the steps, events, and operations that make up the process. This will help you understand the process and verify the scope you defined in your charter. It is particularly important that your high-level map reflects the process as it actually is, since it serves as the basis for more detailed maps. In the Measure and Analyze phases, you create a detailed process map to help you identify problems in the process. Your improvement project will focus on addressing these problems. In the Improve phase, you can use process mapping to develop solutions by creating maps of how the process “should be.”

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Pugh Matrix

the tool used to facilitate a disciplined, team-based process for concept selection and generation. Several concepts are evaluated according to their strengths and weaknesses against a reference concept called the datum. The datum is the best current concept at each iteration of the matrix. The Pugh matrix encourages comparison of several different concepts against a base concept, creating stronger concepts and eliminating weaker ones until an optimal concept finally is reached provides an objective process for reviewing, assessing, and enhancing design concepts the team has generated with reference to the project’s CTQs. Because it employs agreed-upon criteria for assessing each concept, it becomes difficult for one team member to promote his or her own concept for irrational reasons. The Pugh matrix is the recommended method for selecting the most promising concepts in the Analyze phase of the DMADV process. It is used when the team already has developed several alternative concepts that potentially can meet the CTQs developed during the Measure phase and must choose the one or two concepts that will best meet the performance requirements for further development in the Design phase

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Quality Function Deployment a methodology that provides a flow down process for CTQs from the highest to the lowest level. The flow down process begins with the results of the customer needs mapping (VOC) as input. From that point we cascade through a series of four Houses of Quality to arrive at the internal controllable factors. QFD is a prioritization tool used to show the relative importance of factors rather than as a transfer function. QFD drives a cross-functional discussion to define what is important. It provides a vehicle for asking how products/services will be measured and what are the critical variables to control processes. The QFD process highlights trade-offs between conflicting properties and forces the team to consider each trade off in light of the customer’s requirements for the product/service. Also, it points out areas for improvement by giving special attention to the most important customer wants and systematically flowing them down through the QFD process. QFD produces the greatest results in situations where1. Customer requirements have not been clearly defined 2. There must be trade-offs between the elements of the business 3. There are significant investments in resources required

all N/A 0
Regression

see Multiple Regression

Continuous X & Y A correlation is detected 0
Risk Assessment The risk-management process is a methodology used to identify risks, analyze risks, plan, communicate, and implement abatement actions, and track resolution of abatement actions. Any time you make a change in a process, there is potential for unforeseen failure or unintended consequences. Performing a risk assessment allows you to identify potential risks associated with planned process changes and develop abatement actions to minimize the probability of their occurrence. The risk-assessment process also determines the ownership and completion date for each abatement action. In DMAIC, risk assessment is used in the Improve phase before you make changes in the process (before running a DOE, piloting, or testing solutions) and in the Control phase to develop the control plan. In DMADV, risk assessment is used in all phases of design, especially in the Analyze and Verify phases where you analyze and verify your concept design.

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Root Sum of Squares Root sum of squares (RSS) is a statistical tolerance analysis method used to estimate the variation of a system output Y from variations in each of the system’s inputs Xs. RSS analysis is a quick method for estimating the variation in system output given the variation in system component inputs, provided the system behavior can be modeled using a linear transfer function with unit (± 1) coefficients. RSS can quickly tell you the probability that the output (Y) will be outside its upper or lower specification limits. Based on this information, you can decide whether some or all of your inputs need to be modified to meet the specifications on system output, and/or if the specifications on system output need to be changed. Use RSS when you need to quantify the variation in the output given the variation in inputs. However, the following conditions must be met in order to perform RSS analysis: 1. The inputs (Xs) are independent. 2. The transfer function is linear with coefficients of +1 and/or – 1. 3. In addition, you will need to know (or have estimates of) the means and standard deviations of each X.

Continuous X & Y N/A 0
Run Chart A run chart is a graphical tool that allows you to view the variation of your process over time. The patterns in the run chart can help identify the presence of special cause variation. The patterns in the run chart allow you to see if special causes are influencing your process. This will help you to identify Xs affecting your process run chart. used in many phases of the DMAIC process. Consider using a run chart to 1. Look for possible time-related Xs in the Measure phase 2. Ensure process stability before completing a hypothesis test 3. Look at variation within a subgroup; compare subgroup to subgroup variation

cont (measurement) N/A 1
Sample Size Calculator

The sample size calculator simplifies the use of the sample size formula and provides you with a statistical basis for determining the required sample size for given levels of a and b risks The calculation helps link allowable risk with cost. If your sample size is statistically sound, you can have more confidence in your data and greater assurance that resources spent on data collection efforts and/or planned improvements will not be wasted

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Scatter Plot a basic graphic tool that illustrates the relationship between two variables. The variables may be a process output (Y) and a factor affecting it (X), two factors affecting a Y (two Xs), or two related process outputs (two Ys). Useful in determining whether trends exist between two or more sets of data. Scatter plots are used with continuous and discrete data and are especially useful in the Measure, Analyze, and Improve phases of DMAIC projects.

all N/A 0
Simple Linear Regression

Simple linear regression is a method that enables you to determine the relationship between a continuous process output (Y) and one factor (X). The relationship is typically expressed in terms of a mathematical equation, such as Y = b + mX, where Y is the process output, b is a constant, m is a coefficient, and X is the process input or factor Simple linear regression will help you to understand the relationship between the process output (Y) and any factor that may affect it (X). Understanding this relationship will allow you to predict the Y, given a value of X. This is especially useful when the Y variable of interest is difficult or expensive to measure You can use simple linear regression during the Analyze phase to help identify important Xs and during the Improve phase to define the settings needed to achieve the desired output.

Continuous X & Y

indicate that there is sufficient evidence that the coefficients are not zero for likely Type I error rates (a levels)… SEE MINITAB

0
Simulation

Simulation is a powerful analysis tool used to experiment with a detailed process model to determine how the process output Y will respond to changes in its structure, inputs, or surroundings Xs. Simulation model is a computer model that describes relationships and interactions among inputs and process activities. It is used to evaluate process output under a range of different conditions. Different process situations need different types of simulation models. Discrete event simulation is conducted for processes that are dictated by events at distinct points in time; each occurrence of an event impacts the current state of the process. Process Model is GE Company’s standard software tool for running discrete event models. Continuous simulation is used for processes whose variables or parameters do not experience distinct start and end points. Crystal Ball is GE’s standard software tool for running continuous models Simulation can help you: 1. Identify interactions and specific problems in an existing or proposed process 2. Develop a realistic model for a process 3. Predict the behavior of the process under different conditions 4. Optimize process performance Simulation is used in the Analyze phase of a DMAIC project to understand the behavior of important process variables. In the Improve phase of a DMAIC project, simulation is used to predict the performance of an existing process under different conditions and to test new process ideas or alternatives in an isolated environment

all N/A 0
Six Sigma Process Report A Six Sigma process report is a Minitab tool that provides a baseline for measuring improvement of your product or process It helps you compare the performance of your process or product to the performance standard and determine if technology or control is the problem A Six Sigma process report, used with continuous data, helps you determine process capability for your project Y. Process capability is calculated after you have gathered your data and have determined your performance standards

Continuous Y & all X’s N/A 0
Six Sigma Product Report calculates DPMO and process short term capability

It helps you compare the performance of your process or product to the performance standard and determine if technology or control is the problem

used with discrete data, helps you determine process capability for your project Y. You would calculate Process capability after you have gathered your data and determined your performance standards.

Continuous Y, Discrete Xs N/A 0
Stepwise Regression Regression tool that filters out unwanted X’s based on specified criteria.

Continuous X & Y N/A 0
Tree Diagram

A tree diagram is a tool that is used to break any concept (such as a goal, idea, objective, issue, or CTQ) into subcomponents, or lower levels of detail. Useful in organizing information into logical categories. See “When use?” section for more detail A tree diagram is helpful when you want to 1. Relate a CTQ to subprocess elements (Project CTQs) 2. Determine the project Y (Project Y) 3. Select the appropriate Xs (Prioritized List of All Xs) 4. Determine task-level detail for a solution to be implemented (Optimized Solution)

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u Chart A u chart, shown in figure 1, is a graphical tool that allows you to view the number of defects per unit sampled and detect the presence of special causes The u chart is a tool that will help you determine if your process is in control by determining whether special causes are present. The presence of special cause variation indicates that factors are influencing the output of your process. Eliminating the influence of these factors will improve the performance of your process and bring your process into control You will use a u chart in the Control phase to verify that the process remains in control after the sources of special cause variation have been removed. The u chart is used for processes that generate discrete data. The u chart monitors the number of defects per unit taken from a process. You should record between 20 and 30 readings, and the sample size may be variable.

N/A 1
Voice of the Customer

The following tools are commonly used to collect VOC data: Dashboard ,Focus group, Interview, Scorecard, and Survey.. Tools used to develop specific CTQs and associated priorities. Each VOC tool provides the team with an organized method for gathering information from customers. Without the use of structured tools, the data collected may be incomplete or biased. Key groups may be inadvertently omitted from the process, information may not be gathered to the required level of detail, or the VOC data collection effort may be biased because of your viewpoint. You can use VOC tools at the start of a project to determine what key issues are important to the customers, understand why they are important, and subsequently gather detailed information about each issue. VOC tools can also be used whenever you need additional customer input such as ideas and suggestions for improvement or feedback on new solutions

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Worst Case Analysis A worst case analysis is a nonstatistical tolerance analysis tool used to identify whether combinations of inputs (Xs) at their upper and lower specification limits always produce an acceptable output measure (Y). Worst case analysis tells you the minimum and maximum limits within which your total product or process will vary. You can then compare these limits with the required specification limits to see if they are acceptable. By testing these limits in advance, you can modify any incorrect tolerance settings before actually beginning production of the product or process. You should use worst case analysis : To analyze safety-critical Ys, and when no process data is available and only the tolerances on Xs are known. Worst case analysis should be used sparingly because it does not take into account the probabilistic nature (that is, the likelihood of variance from the specified values) of the inputs.

all N/A 0
Xbar-R Chart The Xbar-R chart is a tool to help you decide if your process is in control by determining whether special causes are present.

Xbar-R charts can be used in many phases of the DMAIC process when you have continuous data broken into subgroups. Consider using an Xbar-R chart· in the Measure phase to separate common causes of variation from special causes,· in the Analyze and Improve phases to ensure process stability before completing a hypothesis test, or· in the Control phase to verify that the process remains in control after the sources of special cause variation have been removed.

Continuous X & Y N/A 1
Xbar-S Chart An Xbar-S chart, or mean and standard deviation chart, is a graphical tool that allows you to view the variation in your process over time. An Xbar-S chart lets you perform statistical tests that signal when a process may be going out of control. A process that is out of control has been affected by special causes as well as common causes. The chart can also show you where to look for sources of special cause variation. The X portion of the chart contains the mean of the subgroups distributed over time. The S portion of the chart represents the standard deviation of data points in a subgroup The Xbar-S chart is a tool to help you determine if your process is in control by seeing if special causes are present. The presence of special cause variation indicates that factors are influencing the output of your process. Eliminating the influence of these factors will improve the performance of your process and bring it into control An Xbar-S chart can be used in many phases of the DMAIC process when you have continuous data. Consider using an Xbar-S chart……in the Measure phase to separate common causes of variation from special causes, in the Analyze and Improve phases to ensure process stability before completing a hypothesis test, or in the Control phase to verify that the process remains in control after the sources of special cause variation have been removed. NOTE – Use Xbar-R if the sample size is small.

Continuous X & Y N/A 1

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Minitab

Tool

Example

Y Xs

GO HOME!!

ANOVA

Variable Attribute At least one group of data is different than at least one other group.

Response data must be stacked in one column and the individual points must be tagged (numerically) in another column. Variable Attribute N/A

All All N/A

Chi-Square

Discrete Discrete At least one group is statistically different.

Dot Plot Quick graphical comparison of two or more processes’ variation or spread

Variable Attribute N/A

Variable

At least one group of data is different than at least one other group.

Histogram

Variable Attribute N/A

Homogeneity of Variance

Response data must be stacked in one column and the individual points must be tagged (numerically) in another column. Variable Attribute (Use Levene’s Test) At least one group of data is different than at least one other group

Kruskal-Wallis Test

Response data must be stacked in one column and the individual points must be tagged (numerically) in another column. Variable Attribute At least one mean is different

Variable Attribute N/A

Response data must be stacked in one column and the individual points must be tagged (numerically) in another column. Variable Attribute N/A

Input one column of data Variable N/A Not equal

Pareto

Variable Attribute N/A

Process Mapping

N/A

N/A N/A N/A

Regression

Input two columns of equal length Variable Variable A correlation is detected

Variable N/A N/A

Scatter Plot

Variable Variable N/A

Input two columns of equal length Variable Variable There is a difference in the means

Use When	Minitab Format	Data Format	p < 0.05 indicates
Determine if the average of a group of data is different than the average of other (multiple) groups of data	Compare multiple fixtures to determine if one or more performs differently	Stat ANOVA Oneway				Response data must be stacked in one column and the individual points must be tagged (numerically) in another column.
Box & Whisker Plot	Compare median and variation between groups of data. Also identifies outliers.	Compare turbine blade weights using different scales.	Graph Boxplot
Cause & Effect Diagram/ Fishbone	Brainstorming possible sources of variation for a particular effect	Potential sources of variation in gage r&r	Stat Quality Tools Cause and Effect	Input ideas in proper column heading for main branches of fishbone. Type effect in pulldown window.
Determine if one set of defectives data is different than other sets of defectives data.	Compare DPUs between GE90 and CF6	Stat Tables Chi-square Test	Input two columns; one column containing the number of non-defective, and the other containing the number of defective.
Compare length of service of GE90 technicians to CF6 technicians	Graph Character Graphs Dotplot	Input multiple columns of data of equal length
General Linear Models	Determine if difference in categorical data between groups is real when taking into account other variable x’s	Determine if height and weight are significant variables between two groups when looking at pay	Stat ANOVA General Linear Model	Response data must be stacked in one column and the individual points must be tagged (numerically) in another column. Other variables must be stacked in separate columns.	Attribute/ Variable
View the distribution of data (spread, mean, mode, outliers, etc.)	View the distribution of Y	Graph Histogram or Stat Quality Tools Process Capability		Input one column of data
Determine if the variation in one group of data is different than the variation in other (multiple) groups of data	Compare the variation between teams	Stat ANOVA Homogeneity of Variance
Determine if the means of non-normal data are different	Compare the means of cycle time for different delivery methods	Stat Nonparametrics Kruskal-Wallis
Multi Vari Analysis (See also Run Chart / Time Series Plot)	Helps identify most important types or families of variation	Compare within piece, piece to piece or time to time making of airfoils leading edge thickness	Graph Interval Plot	Response data must be stacked in one column and the individual points must be tagged (numerically) in another column in time order.
Notched Box Plot	Compare median of a given confidence interval and variation between groups of data	Compare different hole drilling patterns to see if the median and spread of the diameters are the same	Graph Character Graphs Boxplot
One-sample t-test	Determine if average of a group of data is statistically equal to a specific target	Manufacturer claims the average number of cookies in a 1 lb. package is 250. You sample 10 packages and find that the average is 235. Use this test to disprove the manufacturer’s claim.	Stat Basic Statistics 1 Sample t
Compare how frequently different causes occur	Determine which defect occurs the most often for a particular engine program	Stat Quality Tools Pareto Chart			Input two columns of equal length
Create visual aide of each step in the process being evaluated	Map engine horizontal area with all rework loops and inspection points	Use rectangles for process steps and diamonds for decision points
Determine if a group of data incrementally changes with another group	Determine if a runout changes with temperature	Stat Regression Regression
Run Chart/Time Series Plot	Look for trends, outliers, oscillations, etc.	View runout values over time	Stat Quality Tools Run Chart or Graph Time Series Plot	Input one column of data. Must also input a subgroup size (1 will show all points)
Look for correlations between groups of variable data	Determine if rotor blade length varies with home position	Graph Plot or Graph Marginal Plot or Graph Matrix Plot (multiples)	Input two or more groups of data of equal length
Two-sample t-test	Determine if the average of one group of data is greater than (or less than) the average of another group of data	Determine if the average radius produced by one grinder is different than the average radius produced by another grinder	Stat Basic Statistics 2 Sample t

A Lean Six Sigma Case Study

If you want to prosper for a year, grow rice. If you want to prosper for a decade, plant trees. If

you want to prosper for a century, grow people — a wise old farmer reflecting back on a life

of toil in the soil

PROJECT DESCRIPTION

The following Lean Six Sigma case study will reflect a real-life healthcare problem with

Continuous Improvement and Lean Six Sigma Tools to show how some of the tools are put into

place in the real world. You will be required to complete the project along with some analysis

for each section.

Case Study:

Student Case Study

Process Improvement –
Reduction in Wait Time for
Patients in a Doctor Office

Executive Summary
Dr. Deasley is a popular Doctor in Tampa, Florida specializing in primary care. He spends a great deal of
time with each of his patients, typically, 45 minutes to one (1) hour. Dr. Deasley’s patients and staff love
him for his patience and attention. However, there are many other patients waiting in the waiting room
who become impatient at the long wait time. Dr. Deasley’s office hours are 7:30 AM to 5:30 PM Monday
through Friday. He conducts patient call backs between patients, during his lunch hour and after office
hours. We triage the calls so he gets back to more seriously sick patients first. However, sometimes he
doesn’t call back non-emergencies until the next AM. Dr. Deasley becomes overbooked because he likes
to have 10 patients scheduled per day. However, he frequently needs to rebook patients he is unable to
see due to time constraints. As a result, several long-term patients have been leaving his practice.

This has resulted in a decrease in revenue for the office. In addition, his office is experiencing a rather
high rate of staff turnover. Staff are responsible for booking patients and managing the workflow in the
office. When backlogs occur and patients become annoyed about wait times, the staff usually
experience the brunt of the patient dissatisfaction, which effects staff morale. Each time the office hires
replacement staff, it takes a significant amount of time to train new employees and it is costly to
advertise and recruit competent staff. Dr. Deasley is very concerned about both his patients and staff.

His Office Manager, Ms. Smith, who recently was employed at Memorial Hospital of Tampa, participated
in several Continuous Improvement Projects at the hospital. She is a certified Lean Six Sigma Green Belt.
As a result, Ms. Smith has suggested a plan to the doctor to conduct a Lean Six Sigma project with the
objective of Reducing Patient Wait Time and Improving Office Workflow. Ms. Smith explained the
project improvements and objectives. Dr. Deasley has approved the project. As an initial step, the Office
Manager has established her team. Each employee has a role in the project. Based on patient
complaints and the doctor’s requirements, they have some initial Voice of Customer (VOC). Patients
would like to see the Doctor within 10 minutes of arriving and spend no more than 30 minutes in the
office total for routine visits. The Doctor would like to see 15 patients per day. These changes need to
be made within 3 months in order to minimize patient dissatisfaction, stop patients leaving the practice
due to long wait times and rescheduling and improve employee morale and retention.

Define
 Please fill out the project charter. Write the Goal Statement utilizing S.M.A.R.T. objectives

(Specific, Measurable, Attainable, Relevant and Time Bound):
 Please complete a High Level “As Is” Process Map.
 Please create a SIPOC of the process based on the information that you know. Feel free to use

your imagination for this.
 Describe methods for collecting Voice of the Customer. (SEE APPENDIX A for VOC)
 Please create an Affinity Diagram or List based on VOC so you can identify Customer “NEEDS”

for CTQ Tree
 Please create a Critical to Quality Tree utilizing the Voice of the Customer. Identify the Needs,

Drivers and Requirements or Metric to needed to meet these needs

Conclusion of Define: The output of the DEFINE stage is a PROJECT CHARTER (PC) and identified
stakeholders. The PC shall include a Problem Statement with Goals utilizing S.M.A.R.T.
methodology to address the problems identified. The Goal will be aligned with the customer
CTQ Requirements. A clearly defines SCOPE is included in the PC. What is IN SCOPE and What is
OUT OF SCOPE? Your Team is identified, and Roles & Responsibilities are defined. A SIPOC Map
is completed. An “As Is” Process Map is completed in order to better visualize the Workflow in
the current process. The DEFINE Phase provides for identification of the VOC and CTQs, their
needs, drivers and requirements. The student will have evaluated and Affinitized the VOC. CTQ
trees were created to identify key requirements for meeting the customer’s needs. The Project
Team should have a list of external Key stake Holders, if applicable, e.g., Hospital Radiology, who
may be impacted by process changes within the Doctor’s medical practice. If the Doctor’s staff
schedule testing appointments for patients and are required to make frequent changes, this has
an impact on the department or entity conducting the testing. The Project Team will have met
with Dr. Deasley for his approval to proceed and now has a baseline to begin the Measure
phase.

Measure
 Based on Customer requirements the project team collected initial data. Use Pareto Analysis of

# occurrences to determine the 5 factors which are causing over 75% of the problem with wait
time. You need to determine the biggest contributors to the problem. One tool to accomplish
this is the Pareto Chart. You need to know if it is reasonable to assume that these five
‘parameters’ are normally distributed. (SEE APPENDIX B)

 Based on Pareto Analysis what are the focus areas? What are the Key Performance Indicators
(KPI’s)?

 Define your Data Collection Plan. Include the types of data you will be collecting (Discrete or
Continuous), Why? (In many instances you will have a mix of both types of data depending on
the Data source.

 Based on the data collected Construct FIVE (5) histograms for the below data sets. (SEE
APPENDIX C) for data sets

 Interpret each of the histograms to determine whether the assumption of normality is
reasonable.

 If the data are not approximately normally distributed, why not?
 The team also believed there was a Motorola shift during the process. Please describe the

Motorola Shift and potential causes that they could have experienced the shift.
 Calculate the DPMO for the entire process considering the 5 main opportunities for defects.

Determine the baseline sigma with the Motorola shift.
 Calculate the Process Performance, Pp and Ppk, based on the time the Doctor spends with the

patient. Student will be able to compare current Process performance to Capability Study
performed for process improvements. Tint: drawing a picture of the data based on a Normal
Curve may help student visualize if data is skewed when evaluating population distribution. Use
UCL = 60 minutes and LCL = 0 Minutes. In Healthcare LCL will frequently be “O”
Pp = (Upper Spec – Target Value)/(6*Standard Deviation)
Ppk = (Upper Spec – Mean)/(3*Standard Deviation)

Conclusion of Measure: A Data Collection Plan was created. Data was taken of as many
parameters as possible before changing any variables. Key Data has been provided for your
use as directed in the instructions above. Pareto charts have been created based on the VOC.
The 5 Largest Contributing Factors have been Identified. These should have aligned with the
data provided. A method for tracking data to capture for analysis should have been identified
even if the actual data is already provided. Then from the categories and data “collected”, 5
Histograms should have been created along with the narrative for Analysis, specifically
related to determination if data was normally distributed. An explanation of the Motorola
Shift is provided. DPMO is calculated. Pp/Ppk are calculated and current process Sigma Level
is defined. It was found that Dr. Deasley was spending more time with his patients than
necessary. The process needs to be analyzed based on the data.

Analyze
 Create a Stem and Leaf Plot that were captured from the patient wait times in the waiting

rooms. (SEE APPENDIX D for data set)
 Calculate the measures of central Tendency. What can you interpret from these measures?

Please document a conclusion (SEE APPENDIX D for data set)

Two individual staff members were being observed performing identical activities in the Doctor’s
office. 25 random samples were taken. One of the Medical Assistants is a new employee. Medical
Assistant #1 has been with Dr. Deasley for several years. Medical Assistant #2 is a new employee
and has been with this medical practice for 9 months. We want to determine how Medical Assistant
#2 performs when compared to Medical Assistant #1 since she is a new employee. (SEE APENDIX E
for data sets)

Assume this is a one-sided t-test and the historical average of Medical Assistant #1 is .0126

Medical Assistant #1 data will be considered the population mean

 Please provide the following information based on your analysis of the two Medical Assistants
• Medical Assistant #2 Average
• Medical Assistant #2 Standard Deviation
• Null Hypothesis
• Alternative Hypothesis
• T-Test Statistic
• Critical Value
• Statistical Conclusion for the null and alternative hypothesis.

Conclusion of Analyze: Stem and Leaf Plots were created; Measures of Central Tendency were also
determined, and an interpretation of the results were made. Data was analyzed to review if different
staff members were performing similarly or not. Students should have established a Null Hypothesis
and Alternative Hypothesis from the data for the 2 staff members. A one-sided T-Test was performed,
and conclusions made based on the outcome.

IMPROVE
A staff member has been stating for months that there is a correlation between the Room Availability
and the Patient arrival time. Should the Office Manager have listened to this staff member’s
observation? Refer back to the Pareto to serve as guidance.

 Construct a scatter diagram and calculate the correlation coefficient to see if she is correct. SEE
APPENDIX F for data set

o Is there strong correlation between room availability and patient arrival time?
o IF there is strong correlation, is it positive or negative? (Answer with positive, negative

or N/A)
o What is the correlation coefficient between the two variables? (Use 6 decimal places)

 Discuss the 8 Deadly Wastes (MUDA) of the process.
 Create a Fishbone Diagram. List Potential Root Causes. Narrow Potential Root Causes to Key

Root Causes. Explain some of the key Root causes.
 Discuss Improvements that you would suggest based on findings from FISHBONE Analysis.

Conclusion of Improve: A Scatter Plot was constructed, and a Correlation was completed. The
determination of whether the 2 factors Correlate based on a Correlation Coefficient determination is
stated and comments on whether the correlation is Positive or Negative are included. 8 Wastes were
evaluated and identified where applicable. A FISHBONE DIAGRAM was created, and many ideas were
brainstormed for Potential Root Cause. These were then narrowed to the critical few Root Causes.
Many improvement suggestions were made.

CONTROL
An I-MR chart was plotted for the Doctor’s office to ensure the specifications were performing as
planned and the patients and Doctors were satisfied.

 Please indicate if the control chart is stable and if any Shewhart Rules have occurred.

 A normality test was conducted. Please advise if the data is normal.

 A capability study was completed. Please advise if the process is stable and any analysis you
find is relevant.

 Please complete a Control and Monitoring Plan for the project.
 Please state your conclusions of Dr Deasley’s office

Conclusion of Control: A conclusion regarding the stability of the Control Chart was made and any
violations of the Shewhart Rules were noted. Students then observed the Normality of the data. A
Capability Study was done presumably using data from improvements made and analysis of the
output was discussed. A Control and Monitoring Plan was created to ensure monitoring of
improvements for Sustainability. Finally, a control plan was developed to be used for staff to visually
track their performance and for discussion with Dr. Deasley. We have collected data after making
many improvements to see if the process is now stable. We will continue to monitor our progress and
follow the control plan.

>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>

APPENDIX A: VOICE OF THE CUSTOMER

Feedback from Patients:

I wait too long. I only have an hour for Lunch. I make my appointments specifically at Lunch
time because I can’t come after work.

I like to come very early and be one of Dr. D’s first patients. If I am not his 1st, I end up waiting
and am late for work. My company is very strict about being on time.

I wouldn’t mind if the doctor spent less time with me. I only usually come for an Annual
Checkup and a Flu shot. If I feel really sick, I call the office. When I broke my arm last year, the
doctor sent me right to the hospital. You guys made the arrangements for my X-Ray, so I didn’t
need to wait.

I can’t be late when I come in the afternoon. I need to pick my daughter up from school. If I
come in the afternoon, can you make it a short visit?

The doctor spends so much time asking me questions, can’t he look at my chart before I get
into the exam room?

The last time I was here, you put me in a room with someone else’s clothes. The woman had
gone to the Ladies’ room and came back to get dressed. I had to wait in the hallway.

Feedback from Staff

We need to organize the exam rooms. Dr. Deasley is always looking for something and I need to
go find it.

We can’t have multiple people at the Front desk assigning patients to rooms. They don’t always
assign patients to the right room and equipment is not available

Dr. D keeps taking equipment with him from room to room,

The patients are not getting here early enough to get them ready for the doctor. He like to have
their Blood Pressure, Weight and Temperature done before he comes in.

Patients keep arriving the last minute, then they get angry because they miss their appointment
and need to wait.

I hope I never have to reschedule Mrs. Smyth for a new appointment because the doctor
couldn’t see her. She was practically screaming at me.

We had 2 patients, Mrs. Jones and Mr. Thomas ask for their records to be sent to a new
doctor’s office. That is the 4th time that has happened this year and we are only ½ way through
the year.

The new Medical Assistant was complaining because she said there is too much chaos here. I
think she might be sorry she came her. I hope she doesn’t go back to the hospital. It takes so
much time to find good people and train them.

Feedback from Doctor

I don’t always have the instruments I need in the Exam Room. I need to have my Assistant go
find what I need. I’ve started taking Instruments with me to my next patient only to find 3 of
the same instrument I am carrying in the next Exam Room.

I have seen several patients waiting in the hall outside the Exam Room. I don’t like that
situation. We need to stop this practice.

I see some staff running around like crazy and others sitting around appearing to have nothing
to do.

I am not one of these “hands off’ doctors, I like to spend time with my patients. But sometimes
a patient will sit there with nothing to say and another patient will have a long list of issues.

If this improvement project is successful, I would like to see 15 Patients a day. We need to keep
operating costs in mind. We need to keep our equipment up to date and I need to ensure we
plan for salaries and bonuses at year end.

I notice we have had 3 people leave within the past 18 months. I would like to understand why.
It is very expensive to recruit staff and it takes time before they are proficient in their jobs. The
team we have now is very good. I would like to keep all of them. We do monitor salaries and
compare with market standards, so I know our salaries and benefits are competitive.

Feedback from Other Sources

Radiology Department is complaining because they state we make too many changes to the
patient appointments.

The Laboratory department is complaining because our patients are coming for testing outside
their assigned appointment time and too late in the day.

APPENDIX B: Based on VOC data to be used to construct CTQ’s. Project Team will
identify key focus areas in Doctor’s Office using Pareto Diagram. These focus
areas will then be monitored as defined in Data Collection Plan.

Time the Doctor was spending with Patients – 79

Number of times Dr arrives late – 4

Proper Medical Devices not Available – 30

Number of times patient is left in the hallway – 17

Rooms Available at Doctor’s Office -22

Number of times staff arrive late – 3

Staffing of Doctor’s Office -41

Number of times scheduling changes were made for patient testing – 15

Number of times patient had to be rescheduled for Dr visit – 10

Arrival Time of Patients – 52

APPENDIX C: Data set to be used to construct 5 Histograms

1. Percent of Rooms fully equipped with Proper Medical Devices

• This varies between 10.5 and 11. This is the number of devices or
number of times devices were not available in the rooms.

2. Rooms available –

• Varies from 7.45 -7.66. This is the percentage of rooms available

3. Staffing at Dr. Office

• Varies from 0.54-0.56. Effort per day (which is a value used depicting that
people that had multiple duties so you could have a fraction of a person
available).

4. Arrival Time of Patients

• Minutes late

5. Time Dr. Spends with Patients

• Minutes

Date

% of
Rooms

fully
equipped

with
Proper
Medical
Devices

% Rooms
Available

at Dr.
Office

Staffing at
Dr. Office
Percent

time
spent

Minutes
late

Time Dr.
Spends

with
Patients

4-Jul 10.82 7.45 0.5502 172 48

5-Jul 10.82 7.55 0.5522 169 34

6-Jul 10.86 7.67 0.546 177 23

7-Jul 10.87 7.65 0.5462 170 32

8-Jul 10.84 7.62 0.5491 174 19

9-Jul 10.85 7.59 0.5486 175 37

10-Jul 10.86 7.6 0.5428 167 20

11-Jul 10.87 7.52 0.5532 171 47

12-Jul 10.89 7.49 0.5472 168 27

13-Jul 10.8 7.54 0.5522 172 31

14-Jul 10.81 7.52 0.5494 168 44

15-Jul 10.89 7.61 0.5519 163 27

16-Jul 10.81 7.52 0.5509 174 61

17-Jul 10.9 7.61 0.5412 169 17

18-Jul 10.87 7.53 0.5518 171 26

19-Jul 10.86 7.57 0.5523 172 50

20-Jul 10.85 7.59 0.5415 172 11

21-Jul 10.85 7.55 0.5477 168 53

22-Jul 10.86 7.61 0.553 169 18

23-Jul 10.86 7.54 0.55 166 75

24-Jul 10.83 7.57 0.5437 172 27

25-Jul 10.89 7.51 0.5463 168 36

26-Jul 10.76 7.63 0.5566 174 40

27-Jul 10.78 7.5 0.541 175 30

28-Jul 10.86 7.58 0.5542 164 23

29-Jul 10.9 7.55 0.5569 173 15

30-Jul 10.83 7.51 0.5432 168 15

31-Jul 10.82 7.5 0.5487 170 35

1-Aug 10.87 7.59 0.5537 173 45

2-Aug 10.88 7.58 0.541 170 25

3-Aug 10.67 7.64 0.5554 173 42

4-Aug 10.72 7.48 0.5521 167 64

5-Aug 10.65 7.57 0.5532 169 23

6-Aug 10.7 7.46 0.5563 172 53

7-Aug 10.67 7.53 0.5508 165 50

8-Aug 10.65 7.6 0.5527 170 16

9-Aug 10.6 7.49 0.5546 169 41

10-Aug 10.66 7.65 0.5478 170 7

11-Aug 10.61 7.55 0.5468 165 31

12-Aug 10.69 7.55 0.5566 172 18

13-Aug 10.71 7.51 0.5531 168 53

14-Aug 10.66 7.49 0.5482 173 34

15-Aug 10.64 7.49 0.5473 172 37

16-Aug 10.62 7.49 0.5442 170 80

17-Aug 10.63 7.56 0.5491 176 19

18-Aug 10.67 7.59 0.5596 175 26

19-Aug 10.62 7.47 0.5491 170 13

20-Aug 10.62 7.58 0.5507 169 18

21-Aug 10.63 7.55 0.556 177 36

22-Aug 10.65 7.47 0.5428 178 7

23-Aug 10.68 7.63 0.5488 172 34

24-Aug 10.68 7.47 0.5531 171 28

25-Aug 10.63 7.68 0.5483 171 44

26-Aug 10.68 7.55 0.5431 171 18

27-Aug 10.58 7.47 0.545 177 23

28-Aug 10.59 7.59 0.5392 172 17

29-Aug 10.64 7.57 0.5512 170 25

30-Aug 10.64 7.53 0.5465 169 15

1-Sept 10.68 7.58 0.5479 164 23

2-Sept 10.6 7.6 0.5452 174 21

Upper Spec 11 7.66 0.56 180 60
Lower Spec 10.5 7.45 0.54 165 0

Target 10.75 7.55 0.55 170 20

APPENDIX D: Data represents Wait Time in minutes beyond their scheduled
Appointment Time for the last 70 patients. Use to create Stem and Leaf Plots.

PATIENT
WAITING

TIME

PATIENT
WAITING
TIME
PATIENT
WAITING
TIME
PATIENT
WAITING
TIME
PATIENT
WAITING
TIME
PATIENT
WAITING
TIME
PATIENT
WAITING
TIME

16 15 19 48 14 47 21
16 17 16 45 80 20 46
17 13 26 50 6 71 48
37 47 17 49 49 47 20
47 11 65 63 48 50 64
32 47 15 17 47 95 16
48 38 17 22 48 47 44
21 17 48 10 52 20 82
18 20 16 18 46 50 51
75 49 44 51 48 35 58

APPENDIX E: Data set for determining performance for Medical Assistant #2. The
historical mean for Medical Assistant #1 was .0126.

MEDICAL ASSISTANT #2
Data

% time/hour

0.009

0.010

0.011

0.011
0.010
0.011
0.011

0.013

0.008

0.012

0.010
0.013

0.014

0.012
0.009
0.014
0.011

0.015

0.011
0.015

0.011

0.012

0.008

APPENDIX F: This is the data set for evaluating Correlation between Room
Availability and Patient Arrival

Room # Availability Patient Arrival Time
154 0.554
153 0.553
152 0.552
152 0.551
151 0.549
151 0.549
151 0.548
151 0.548
151 0.548
151 0.547
151 0.547
151 0.547
151 0.547
151 0.547
151 0.547
151 0.546
150 0.546
150 0.546
150 0.546
150 0.546
150 0.546
150 0.545
150 0.545
150 0.545
149 0.545