a study in the application of six sigma process improvement methodology
TRANSCRIPT
A Study in the Application of Six Sigma Process Improvement Methodology to a
Transactional Process
By
Blain Graphenteen
A thesis submitted in partial fulfillment of the requirements for the
Master of Science
Degree in Industrial Management
South Dakota State University
2003
UMI Number: 1415386
________________________________________________________ UMI Microform 1415386
Copyright 2003 by ProQuest Information and Learning Company.
All rights reserved. This microform edition is protected against
unauthorized copying under Title 17, United States Code.
____________________________________________________________
ProQuest Information and Learning Company 300 North Zeeb Road
PO Box 1346 Ann Arbor, MI 48106-1346
ii
A Study in the Application of Six Sigma Process Improvement Methodology to a
Transactional Process
This thesis is approved as a creditable investigation by candidate for the Master of
Science degree and is acceptable for meeting the thesis requirements for this degree.
Acceptance of this thesis does not imply that the conclusions reached by the candidate are
necessarily the conclusions of the major department.
______________________________________________________
Dr. Robert J. Lacher, Thesis Advisor Date
______________________________________________________
Dr. Ross P. Kindermann, Major Advisor Date
iii
Abstract
Title: A Study in the Application of Six Sigma Process Improvement Methodology to a
Transactional Process
Author: Blain Graphenteen
Date: March 14, 2003
Six Sigma process improvement techniques are described as a structured,
disciplined, and rigorous approach for improving business leadership and performance.
Six Sigma Methodology is designed to provide for the application of statistical tools in
the context of a process improvement structure summarized by the acronym DMAIC–
Define, Measure, Analyze, Improve, and Control. The DMAIC model provides a
framework to identify and eliminate sources of variation in a process, improve and
sustain performance with well-executed control plans, and promote one process
improvement language for all members of an organization to employ.
Six Sigma Methodology has been proven successful in improving operational
processes like machine performance and product quality. However, limited
documentation exists to demonstrate application of Six Sigma toolsets to improve
transactional business processes like inventory optimization. This research paper will
examine a transactional process improvement effort using the Six Sigma DMAIC model.
Highlighted for the reader will be a summary of the progress relating to the process
improvement effort and an analysis of the applicability of the Six Sigma tools used at
each stage of the DMAIC model.
iv
Table of Contents
Page
Abstract……………………………………………………………………… iii
List of Abbreviations ………………………………………………………... vi
List of Tables………………………………………………………………… ix
List of Figures………………………………………………………………… x
Chapter
1. Statement of Research Problem……………………………… 1
1.1 Introduction………………………………………….. 2
1.2 Business Case………………………………………… 6
1.3 Method and Procedure……………………………….. 8
1.4 Review of Literature…………………………………. 9
2. Background of the Study………………………………….…. 15
2.1 Initial Project Data Gathering………………………… 18
2.2 Identification of Assumptions………………………... 22
2.3 Process Definition……………………………………. 23
2.3.1 Process Map………………………………….. 24
2.4 Process Measurement………………………………… 29
2.4.1 Cause and Effects Matrix…………….. 29
2.4.2 Data Collection Plan.…………………. 37
2.4.3 Measurement System Analysis.……… 40
v
2.5 Process Analysis…………………………………….. 50
2.5.1 Failure Mode and Effects Analysis….. 51
2.5.2 Multivariate Analysis………………… 60
2.5.3 Designed Experiments……………….. 70
2.6 Process Improvement………………………………… 80
2.7 Process Control…………………………………….… 97
2.7.1 Project Controls……………………… 98
2.7.2 Process Capability…………………… 108
3. Results and Conclusions………………………………….…. 126
3.1 Research Problem Results…………………………… 127
3.2 Recommendations for Future Study………………… 132
Bibliography………………………………………………………… 135
Supplemental Research References…………………………………. 138
vi
List of Abbreviations
ANOVA: Analysis of Variance
C&E Matrix: Cause and Effects Matrix
CV : Coefficient of Variation
Cp : Process capability
Cpk: Process capability with centering
CSIP: Customer Service Interruption Point
DFD: Date Flow Diagram
DMAIC: Define, Measure, Analyze, Improve, Control
DoE: Design of Experiment
DOS: Days of Stock
vii
FMEA: Failure Mode and Effects Analysis
Gage R&R: Gage Repeatability and Reproducibility
I-MR: Individuals and Moving Range Chart
LCL: Lower Control Limit
LSL: Lower Specification Limit
MANOVA: Multivariate Analysis of Variance
MSA: Measurement System Analysis
OPQ: Optimal Production Quantity
PDCA: Plan, Do, Check, Act Process Improvement Model
P/T Ratio: Precision to Tolerance Ratio
Pp: Process performance
viii
Ppk: Process performance with centering
RACI Matrix: Responsible, Accountable, Consultant, Informed Matrix
RPN: Risk Priority Number
SKU: Stock Keeping Unit
UCL: Upper Control Limit
USL: Upper Specification Limit
ix
List of Tables
Table No. Table Name Page
1-1 Alternative Solutions to achieving Six Sigma Goals……. 11
2-1 Information Technology Feasibility Matrix……………... 85
2-2 Key Process Information Definitions……………………. 113
x
List of Figures
Figure Number Figure Name Page
1-1 The DMAIC Model…………………………………… 5
2-1 Semi-Finished Inventory Days-of-Stock
Baseline Measure……………………………………… 21
2-2 Manufacturing Planning and Control
High Level Process Map……………………………… 26
2-3 Process Inputs and Outputs…………………………… 27
2-4 Basic Cause & Effects Diagram………………………. 30
2-5 Cause and Effects Matrix………………….………….. 35
2-6 Current Inventory State Baseline I-MR Chart………… 44
2-7 Goal Inventory State I-MR Chart (simulation)………... 45
2-8 Optimal Inventory State I-MR Chart (simulation)……… 45
2-9 Service np Chart………………………………………. 48
2-10 Capacity Availability I-MR Chart…………………….. 48
2-11 Failure Mode and Effects Analysis Detection
Rating Scale…………………………………………… 54
2-12 Failure Mode and Effects Analysis Summary
Diagram……………………………………………….. 55
xi
Figure Number Figure Name Page
2-13 Sources of Material Requirements Planning
Variability…………………………………………… 56
2-14 Gateway Product Flow Diagram…………………….. 58
2-15 Inventory Cycle Count np Chart…………………….. 63
2-16 Gateway Inventory Cycle Count np Chart…………… 63
2-17 Demand Variability Box Plot………………………… 64
2-18 Item Schedule Attainment Box Plot………………….. 66
2-19 Baseline Cycle Frequency I-MR Chart………………. 67
2-20 Schedule Change Control Chart……………………… 68
2-21 Schedule Change Pareto Chart……………………….. 69
2-22 Parameter Simulation Model Example (Ha) – Inputs…. 74
2-23 Parameter Simulation Output Example (Ha)…………. 75
2-24 Test for Equal Variances……………………………… 78
2-25 Constraint-Anchored Planning (Ha) Test Results……. 79
2-26 Stakeholder Analysis Excerpt………………………… 81
2-27 Schedule Change Guidelines…………………………. 83
2-28 Optimal Order Quantity Model………………………. 88
2-29 Optimal Production Quantity Simulation Model……… 89
2-30 Group Technology Scheduling Plan……….…………. 90
2-31 Buffer Management Inventory Monitor………………. 93
xii
Figure Number Figure Name Page
2-32 Buffer Management Cycle Frequency
Individuals Chart……………………………………… 93
2-33 Buffer Management Demand Individuals Chart……… 94
2-34 Semi-Finished Inventory Control Plan………………... 98
2-35 Primary Control Plan Measures……………………….. 99
2-36 Control Plan Measurement Enablers………………….. 100
2-37 Counterbalance Control Plan Measures………………. 100
2-38 Responsible, Accountable, Consulted,
Informed (RACI) Matrix……………………………… 101
2-39 Supply Plan Attainment Detail Screen………………… 104
2-40 Schedule Attainment Detail Screen…………………… 104
2-41 Group Technology Cycle Frequency
Individuals Chart………………………………………. 107
2-42 Gateway Stock Keeping Unit 1 I-MR Chart…………… 115
2-43 Capability Results – Baseline Data for
Gateway 1……………………………………………… 119
2-44 Capability Results – Post-Improvement Data for
Gateway SKU 1………………………………………… 121
2-45 Days-of-Stock I-MR Chart – Gateway SKU 1……….. 122
2-46 Days-of-Stock I-MR Chart-Gateway Total…………… 123
2-47 Gateway Inventory Improvement Measure…………… 124
xiii
Figure Number Figure Name Page
2-48 Downstream Inventory Improvement Measure……….. 125
3-1 Quality Digest Survey Results………………………… 128
1
Chapter 1 Statement of Research Problem
This research paper examines a transactional process improvement effort using
the Six Sigma Define, Measure, Improve, and Control (DMAIC) model. A summary of
the progress relating to the process improvement effort and an analysis of the
applicability of the Six Sigma tools demonstrated will be discussed at each stage of the
DMAIC model.
My research is aimed at analyzing the functionality of the Six Sigma tools used
during the process improvement effort and reporting on the inventory optimization
solutions implemented. The attraction of this topic as a thesis paper stems not only from
my personal involvement as a Six Sigma Green Belt project leader for this business case
but also from the lack of similar business case research material found relating Six Sigma
with inventory reduction/optimization projects. Six Sigma topic searches using library
and Internet search engines resulted in examples of where Six Sigma methodology had
been successfully applied to improve operational performance such as product quality
and production yield. Very few examples were found in my literature search in which
Six Sigma tools were used to improve transactional process performance.
Not every Six Sigma tool will be analyzed for its applicability to this transactional
process improvement effort. As a Green Belt project leader, I was not trained on every
Six Sigma tool available. As the project work progressed through the DMIAC model,
use of every Six Sigma tool was not necessary to achieve results.
2
The business case focuses on identifying and implementing supply chain process
improvements that result in semi-finished inventory reduction without negatively
impacting customer delivery performance. The desired improvement result is the
freeing-up of cash for a corporation. By reducing inventory while maintaining acceptable
customer service goals, cash can be made available for reinvestment into corporate
growth strategies. This business case pertains only to the inventory asset category of
semi-finished inventory.
1.1 Introduction
The origin of six sigma as a measurement standard can be traced back to Carl
Frederick Gauss who introduced the concept of the normal curve. Walter Shewhart
expanded the use of six sigma as a measurement standard by demonstrating that three
sigma from the mean is the point where a process requires correction. [1]
The term sigma (σ) is used in statistics to describe variability, where a higher
sigma level indicates a process that is less likely to create defects. When used as a metric,
Six Sigma technically means having no more than 3.4 defects per million opportunities,
in any process, product or service. Statisticians noted that having specification limits six
standard deviations away from the average of an assumed normal distribution will not
result in 3.4 defects per million. The number is arrived at by assuming that, in addition to
random variability, the process average drifts over the long term by 1.5 standard
3
deviations, despite efforts to control it. This results in a one-sided integration under the
normal curve beyond 4.5 standard deviations - an area of approximately 3.4 defects per
million opportunities. [2]
An engineer at Motorola, the late Bill Smith, is widely accredited for coining the
term “Six Sigma.”(Six Sigma is actually a federally registered trademark of Motorola). [3]
Smith noted that system failure rates were substantially higher than predicted by final
product test and concluded that a much higher level of internal quality was required. He
convinced Motorola corporate management of the importance of setting Six Sigma as a
quality goal for achieving this higher level of quality. Smith’s holistic view of reliability
(as measured by mean time to failure) and quality (as measured by process variability and
defect rates) was new as was the Six Sigma quality objective. [4]
Six Sigma has evolved from its meager beginnings as a quality goal to become
labeled as a business process management system. The foundation of Six Sigma is the
application of statistical tools in the context of a disciplined and easy to follow
methodology. It is an approach to sustainable continuous improvement that fosters a
common language and cooperation using basic statistical and process understanding
tools. While the tools have most often been applied to improve operational performance
such as product quality and production yield, their application to transactional process
performance like customer service response time and hospital patient care is becoming
more prevalent.
Regardless of the process type, the goal of Six Sigma improvement is still the
same: To achieve breakthroughs in process performance using a structured process
4
improvement technique that identifies, quantifies and eliminates sources of variation and
provides a roadmap for sustaining performance with well-executed control plans. Many
Six Sigma consultants suggest the use of the DMAIC model (Define, Measure, Analyze,
Improve, and Control) as the structured roadmap to follow during the course of managing
a process improvement effort. At each step of the model, process definition and
statistical analysis tools are available as process understanding transitions from intuitive
and subjective to defined and objective.
Moving from a subjectively defined problem to an objectively defined problem
requires an effort to understand the process. This can be summarized in Six Sigma
terminology as identifying the critical process inputs having the most significant
influence on the performance of the process. The relationship between the process output
and process inputs is represented by y as a function of the x’s, where y represents a
process output and x represents a process input. (The formula y = f (x1,x2,…..xk) can be
used as a simplified representation of this relationship.)
The DMAIC roadmap attempts to lead the process improvement effort to the core
problem through the funneling from the trivial many process inputs to the critical few
process inputs determined to have the most influence on the capability of the process.
Once isolated, these critical inputs should be recognized as the primary sources of
variation in the process. The desired outcome from following the DMAIC roadmap is the
identification and implementation of control plans that will serve as the indicator for
process capability and control of the critical inputs. Figure 1-1is a graphical
representation of the most important tools used in the Six Sigma DMAIC process and the
5
desired effect these tools are designed to have in “funneling” the process input variables
from the trivial many to the vital few.
Figure 1-1. DMAIC Tools and the Funneling Effect
Define
- Project Scope & Boundary
- Leadership Approval
- Process Map
Measure
- Cause & Effects Matrix
- Data Collection Plan
- Measurement System Analysis
Analyze
- Failure Mode & Effects Analysis
- Mutltivariate Analysis
- Design of Experiments
Improve
- Identify Solutions
- Pilot Improvements
- Implementation
Control
- Documentation
- Monitor & Evaluate
- Standardize
- Transfer to Process Owners
New Project
Trivial Many Inputs
Critical FewInputs
Input
Funnel
Define
- Project Scope & Boundary
- Leadership Approval
- Process Map
Measure
- Cause & Effects Matrix
- Data Collection Plan
- Measurement System Analysis
Analyze
- Failure Mode & Effects Analysis
- Mutltivariate Analysis
- Design of Experiments
Improve
- Identify Solutions
- Pilot Improvements
- Implementation
Control
- Documentation
- Monitor & Evaluate
- Standardize
- Transfer to Process Owners
New Project
Trivial Many Inputs
Critical FewInputs
Input
Funnel
6
1.2 Business Case
This business case focuses on identifying and implementing supply chain process
improvements that result in sustained semi-finished inventory reduction without
negatively impacting customer delivery performance. The desired improvement result is
the freeing-up of cash for a corporation. By reducing inventory while maintaining
acceptable customer service goals, cash can be made available for reinvestment into
corporate growth strategies. This business case pertains only to the inventory asset
category of semi-finished inventory. Semi-Finished inventory can be defined as products
that have been stored uncompleted awaiting final operations that adapt them to different
uses or customer specifications. [2]
The company sponsoring the process improvement effort is a multinational firm
with product offerings in several market centers including: Aeronautical, Automotive,
Business Products, and Health Care. The company’s day-to-day manufacturing functions
are managed by site - with a few hundred-production facilities located worldwide. The
Sales, Marketing, and product development functions are centralized at three primary
corporate locations.
The specific production facility for this process improvement effort primarily
manufactures medical products serving both the Consumer and Health Care customer
segments. By all accounts, it is the largest medical products manufacturer in its corporate
Health Care Markets division. By virtue of its size, the facility is also the most influential
contributor to income statement and balance sheet performance in the division.
7
At this manufacturing site, semi-finished inventory is the largest category of
inventory assets as measured in dollars. This result is driven by four key factors. First,
for most all products manufactured, semi-finished inventory is the most flexible stocking
point. One supply of semi-finished inventory provides for many demands. The largest
population of semi-finished inventory is in roll form or jumbos. The adhesive-coated,
woven-coated, or extruded-film jumbo rolls typically run anywhere from 1,000 lineal
yards to 10,000 lineal yards of material. Smaller rolls (referred to as slit rolls) are
typically less than 1,000 lineal yards and are used in production of the finished product.
Conversion or commitment of the jumbo rolls to slit rolls is delayed as long as possible to
allow for conversion flexibility.
Another factor impacting this inventory asset category is the proliferation of semi-
finished good stock keeping units (SKU’s). The semi-finished inventory category
includes approximately 1900 active (with inventory movement) SKU’s representing 70
commodities manufactured across 117 work centers. Since one supply of semi-finished
inventory, as measured in a jumbo roll of material, provides for many demands with a
variety of size configurations, converting the entire jumbo to finished goods would
require additional converting resource time, additional storage space, and the potential for
shelf-life expiration for slower-moving SKU’s.
A third factor is the lack of synchronization between the semi-finished producing
resources and the downstream converting work centers. Due to various factors like
length of changeovers, minimum jumbo size requirements, product family scheduling
requirements, and run frequency, semi-finished supply resources produce more than the
8
consuming resource demands. These excess amounts of inventory could be termed
incidental buffers because they occur as a result of the process capability differences
rather than a safety stock buffer used to protect against demand and supply variability.
Additional synchronization issues include: various stocking strategies, build plans,
productivity goals, and operating expense goals.
The final key factor impacting the level of semi-finished inventory is the lack of
process understanding. Process understanding can be described as: 1) Knowing the level
of semi-finished inventory required to protect against supply and demand variability as
well as understanding the level of inventory that is an inherent result of the process
capability; 2) Quantifying the cost versus cash tradeoff. The Optimal Production Quantity
(OPQ) that strikes a balance between the costs of carrying inventory versus the costs of
producing it.
1.3 Method and Procedure
This research paper will examine a transactional process improvement effort
using the Six Sigma DMAIC model. Progress relating to the process improvement effort
will be presented as well as analysis of the applicability of the Six Sigma tools at each
stage of the DMAIC model. Augmenting the examples provided from the process
improvement project will be additional information or recommendations for the use or
applicability of Six Sigma toolsets discovered in the research. This process improvement
9
effort did not attempt to apply every Six Sigma tool available. Only the tools that were
used or tried will be covered in this paper.
1.4 Review of Literature
The purpose of this literature review is to summarize areas of controversy
surrounding the application of Six Sigma Process Improvement Methodology to process
improvement. The literature review type can be described as both quantitative research
(on the effectiveness of Six Sigma process improvement application) and methodology
research (on the type of processes where Six Sigma tools were applied).
Various types of research sources were explored. Research databases included:
InfoTrac, SDNET, JStor, ProQuest, MINITEX/WebSPIRS, OCLC FirstSearch, and
ProjectMUSE. The primary library resources included the Hilton M. Briggs Library
(South Dakota State University) and the Brookings Public Library (Brookings, South
Dakota) - using primarily the South Dakota Library Network. Several professional
journals were researched including: American Production and Inventory Control Society
(APICS) Journal, Quality Digest, Harvard Business Review, Academy of Management
Journal, Management Science, Journal of Management Studies, Journal of Organizational
Change Management, Strategic Management Journal, MIT Sloan Management Review,
and the Strategic Management Journal. Research textbooks include “The Six Sigma
Way” (Pande, Neuman, Cavanagh (2000)) and “Implementing Six Sigma” (Breyfogle
10
(1999)). Several consulting companies and other miscellaneous Six Sigma worldwide
web sights were explored via the Internet. Several key words or phrases were searched
including: Six Sigma, 6 Sigma, DMAIC, Inventory Optimization, Lean Manufacturing,
Process Improvement, Deming, Quality Function Deployment, QFD, Failure Modes and
Effects Analysis, FMEA, Cause and Effects Matrix, Cause and Effects Diagram, C&E,
COPQ, RTY, transactional processes, operational processes, DPMO (defects per million
opportunities), etc.
Several areas of controversy were discovered during the course of research. One
criticism of the Six Sigma methodology is that it has little to offer that cannot be found
elsewhere. Six Sigma may sound new, but critics view it as fundamentally the same as
statistical process control and/or Total Quality Management. Much of the Six Sigma
methodology is based on tools that have been useful in previous quality initiatives. [5][6]
E.H. Stamatis (2000) described that quality professionals seem mesmerized with
Six Sigma for at least two reasons. “First, it offers easy money, because both the training
and qualification are controlled as though the concepts are unique and innovative and can
only be understood, taught and implemented in one way. In reality, many consultants
who promote the Six Sigma methodology lack consistency in their training materials and
course content, and they themselves lack a knowledge base to build on. Second, Six
Sigma sounds impressive because some major corporations claim exceptional returns on
their Six Sigma investments. Although it's true that some companies--and they constitute
a small percentage of the whole--have had exceptional returns on investment, they only
experienced such a tremendous turnaround because they attacked the simplest, easiest-to-
11
solve problems first, and their quality levels were so low that anything they tried would
have been a success.”[6] Stamatis supported his claim that the Six Sigma breakthrough is
nothing more than a repackaging of the automotive methodologies of advanced product
quality planning (APQP), problem solving and statistical process control (SPC) by
providing a comparison (Table 1-1) of alternative solutions to achieving Six Sigma goals.
Table 1-1. Alternative Solutions to achieving Six Sigma Goals [6]
A second criticism is more statistically technical. Critics argue that assuming a
process mean to be 1.5sigma off-target is somewhat ridiculous. Perhaps 1.5sigma is a bit
large but even more ridiculous is the assumption that one could keep the process mean
exactly on target. Furthermore, sigma, as defined in process capability studies, is the
12
short-term capability within sample variability. Thus the 1.5-sigma shift allows for
variation of the mean about the target. Any process’s long-term variation is often larger
than its short-term variation due to other sources of variability introduced by operator,
materials and operating conditions. (Motorola determined, through years of process and
data collection, that every process varies and drifts over time. Motorola referred to this
phenomenon as the Long-Term Dynamic Mean Variation. This variation typically falls
between 1.4 and 1.6 sigma. ) [3]
Although the structured approach to Six Sigma implementation has been viewed
as a positive, it has also been criticized as a weakness. The speed of implementing the Six
Sigma structure was reported as an issue with Six Sigma. There are other approaches that
can drive process improvement at a faster implementation rate and at a comparable short-
term success rate and return on investment. [5][7][16][17]
In addition to the issue of implementation speed, Martin (2001) found smaller
companies tend to subscribe to other process improvements methodologies due to the
significant costs associated with Six Sigma training. [8]
Costanzo observed that some companies find the statistical nature of Six Sigma
tools do not always translate well to transactional processes and often find it difficult to
know when a process improvement project should not require adherence to the rigorous
Six Sigma methodology. [9] Not every improvement needs to be a Six Sigma project in
order to be successfully implemented. The belief that every improvement effort needs to
be a Six Sigma project can paralyze an organization from making the less difficult and
13
more obvious process improvements as well as inundate the workforce in collecting data
that may not be necessary.
U.S. Bancorp studied Six Sigma as a potential approach to improve customer
service and decided that mapping out every service situation an employee might
encounter to develop a best-in-class response would prove to be a time consuming effort
with minimal return on the investment in time. Because Six Sigma is so statistical, U.S
Bancorp determined “it (Six Sigma) does not correlate well to customer service and is
viewed as missing the human element as the leading statement for customer service
delivery.”[9]
Mel Bergstein, the chairman and chief executive office of the Chicago consulting
firm Diamond Cluster International Inc. wrote that Six Sigma “doesn’t work well at
finding innovative ideas because it was designed for fine-tuning existing products and
processes. Six Sigma appeals to a manager’s need to exert control–often over processes
beyond their control. As great as Six Sigma’s statistical analysis tools are in many
situations, they simply won’t stretch as far as many would have us believe.” [9]
Clifford presents a compelling argument that while Six Sigma process
improvement efforts implemented by a committed CEO and management team have
proven successful in reducing variability and defects, its results do not necessarily
guarantee stock market success. [10] Reducing defects does not seem to matter a great deal
if the company is making a product no one wants to buy. So while many Six Sigma
implementers may be saving money with their error reduction programs, others are
14
spending valuable time and resources for something that may never have any tangible
return on investment for shareholders.
Although Six Sigma improvement techniques may have some merit in identifying
sources of variation in safety practices or processes, Gyorki observed Six Sigma metrics
may be not be adequate for measuring safety. Machine and process safety for employees
and product safety for customers deserve better than six sigma results. [11]
An information deficiency seems to exist in the specific coverage of Six Sigma
applications to transactional process improvement – like inventory optimization, market
growth, supplier performance optimization, and improvement in customer response time.
Hahn, Hill, Hoerl, and Zingraf noted that Allied Signal and General Electric embarked on
commercialization programs centered around Six Sigma concepts, voice of the customer,
value chain analysis, and customer satisfaction. [12] However, no examples were given to
demonstrate how Six Sigma was applied in those commercial processes.
Very few transactional process examples were found that demonstrated the
application of the Six Sigma methodology. Three specific transactional process
improvement examples discovered included the following: “Deployment of Six Sigma
Methodology in Human Resource Function: A Case Study”, [13] “Use of Six Sigma to
Improve the Safety and Efficacy of Acute Anticoagulation with Heparin”, [14] and “Six
Sigma Method Application in Reducing ED Wait Time.”[15] Although the primary focus
of each example was the process improvement benefits, each article demonstrated the
application of different aspects of the Six Sigma methodology. None of these articles
presented a case for which Six Sigma tools were effective or not effective in their process
15
improvement project. One could speculate there is a lack of Six Sigma process
improvement examples because divulging them would detract from the ability of
consultants to solicit business in this arena.
Based upon the results from this Literature Review, there is sufficient evidence to
suggest a research void exists in published examples that demonstrate the specific
application of Six Sigma tools to a transactional process improvement effort.
Chapter 2 Background of the Study
Six Sigma Methodology has been criticized for not contributing anything new to
the area of process improvement. [5][6] Six Sigma concepts have been described as a
compilation of several process improvement techniques - but seems to most resemble
Deming’s “Plan, Do, Check, Act” (PDCA) model. [16] Regardless of the process
improvement model used, examples of application to transactional processes are difficult
to find.
Six Sigma Methodology has demonstrated success in improving operational
processes. “Operational” processes can broadly be defined as those activities relating to
the production of tangible goods. Other terms used to describe operational processes
include “manufacturing”, “production”, “engineering”, and “plant floor.”[16]
The application of Six Sigma methodologies to transactional process
improvement efforts is not as well documented as its operational counterpart. A
“transactional” process can broadly be defined as any function of a company not directly
16
involved in producing tangible goods. Other terms used to describe transactional
processes include “service”, “commercial”, “non-technical”, “support”, and
“administrative.” [16]
The disparity in the number of published Six Sigma case study examples between
operational and transactional processes is one primary driver for this study. A smaller
number of Transactional case study examples were available. Transactional processes
exhibit characteristics that make the application of Six Sigma methodologies more
challenging. Transactional processes are typically invisible work processes with
evolving workflows and procedures, possess a lack of facts and data, and – relative to
capability – typically do not have specifications. [16] Given these challenging
characteristics, transactional process improvement is not impossible. Companies like
General Electric, Allied Signal, and Motorola have been reported as succeeding in their
Six Sigma efforts around transactional processes. However, the majority of transactional
activities seem to not have been touched by the Six Sigma methodology.
The second driver of this study stems from the opportunity to present the results
of a process improvement effort using Six Sigma methodologies. Typically Six Sigma
projects share some common characteristics. A gap exists between current and desired
process performance, they are process-focused and include complex relationships, and
process improvement solutions are not easy and clear. [16] The essence of these
characteristics is captured via goals and parameters in what is usually called the Six
Sigma Project Charter.
17
The project selected for presentation in this paper is inventory optimization. The
project charter was co-written by Corporate Manufacturing Directors who had overall
responsibility for the performance of the production facility studied. The project charter
was not well defined as it lacked adequate metrics and failed to provide any insight into
potential constraints or assumptions surrounding achievement of the project goal.
The presentation of this transactional process improvement effort will follow
closely the process improvement roadmap recommended by many Six Sigma consultants
and advocated by the company represented in this case. [10][12] This roadmap can be
summarized by the acronym DMAIC - Define, Measure, Analyze, Improve, and Control
(see Figure 1-1 on page 5).
The project discussed in this paper will demonstrate and question the application
of Six Sigma tools to a transactional process improvement effort. Following the
description of each DMAIC step, an analysis will address the content relative to the
business case and offer opinions and recommendations concerning the Six Sigma tools
used. This approach will provide readers basic insight into the tools that may or may not
work for other transactional processes and provide specific application examples used in
this business case.
18
2.1 Initial Project Data Gathering
The initial project charter developed by management indicated their collective
understanding of semi-finished inventory was inadequate to identify the level of
inventory required to protect against supply and demand variability and the level of
inventory that is an inherent result of the process performance. Those responsible for
identifying semi-finished inventory reduction as a Six Sigma project also seemed to
believe that semi-finished inventory was a process. As data gathering progressed, the
project team concluded that inventory is an outcome of several processes. Therefore, the
first challenge of the Six Sigma project team was to gather data relating to the processes
that contribute to the outcome defined as semi-finished inventory.
Gathering data for this project included defining the sources of semi-finished
inventory data, categorizing and segmenting the data, and attempting to correlate process
effects to this inventory asset. In addition to gathering data, a secondary objective was to
evaluate existing inventory measures and then select and/or develop measures that would
best detect progress in providing and sustaining the project goals.
Defining the sources of semi-finished inventory data entailed a review of the
systems and software programs used to record and report inventory transactions. This
review was not meant to re-validate the processes associated with recording inventory or
to measure the capability of this process. The purpose of this exercise was to verify the
sources of information could confidently be used as an input source for measuring
19
performance. Both the systems and software code had previously been validated and
documented by the Information Technology group.
The inventory balance integrity was reviewed using physical inventory cycle
count information. The activity of cycle counting compares the computer system
balances with the physical floor location balances. This comparison is reported as a
percentage and as absolute adjustment dollars. (Additional physical inventory accuracy
data will be presented in greater detail in the Multivariate Analysis section.)
A software review was completed on an inventory usage program critical to
calculating a Days-of-Stock (DOS) measure. The data associated with material usage is
derived from the plant production reporting process. Specific operation codes are used to
report and categorize machine time, labor time, material consumption, and output
production. The Information Technology group had previously validated the material
usage program. A random sampling of data was used to re-confirm data integrity. The
data sources to be used for reporting inventory data were validated and deemed to be
reliable for use in inventory measurement systems.
Semi-finished inventory data is archived in a database by week going back three
years. Other information such as primary work centers, market codes, analyst codes,
material forms, last material activity date, usage data, etc. are available in other database
tables and can be linked to the inventory database via a common Item Master table.
Categorizing and segmenting the inventory data assisted in proving or disproving
previously held assumptions about inventory distribution as well as a means for analyzing
the inventory from various perspectives. The ability to view inventory using a variety of
20
Pareto techniques was accomplished by using Microsoft Query to access the previously
validated inventory databases, Microsoft Excel to organize and view the data, and
MINITABTM to analyze the data statistically.
The final phase of data gathering consisted of gaining an understanding of the
current state of this process outcome called semi-finished inventory. Historically the
only measure used to monitor semi-finished inventory levels was the dollar value in
stock. The inventory dollar value failed in most instances to describe the performance of
a process. When customer service levels were high for a sustained period of time,
inventories were scrutinized for reduction even though inventory metrics like days-of-
stock and inventory dollars were meeting expectations. When service levels were
deemed too low inventory levels were scrutinized for mix instead of recognizing the
contributions of demand variability or short-term, intermittent capacity constraints.
The project team concluded measuring inventory in terms of days-of-stock would
be the primary measure used to represent the impact of process improvement efforts. The
measure of inventory dollars would be used as a secondary process measure.
Figure 2-1 represents an example of the baseline semi-inventory days-of-stock
Individuals and Moving Range (I-MR) charts and the project team’s first attempt at
measuring the current state of semi-finished inventory performance.
21
Figure 2-1. Semi-Finished Inventory Days-of-Stock Baseline Measure
At this stage of the project, it is too early to infer any quantitative improvement
information pertaining to the processes contributing to the level of semi-finished
inventory. The baseline measurement data led the project team to observe two interesting
phenomenon: the semi-finished inventory days-of-stock metric appears to be trending
upward and the first data point is out of control relative to the lower specification limit.
This kind of general analysis can be useful in gaining insight into the current state of
performance, determining a more realistic process improvement goal, and in deciding the
type of statistical tools to use.
2 01 0S u b g ro u p 0
1 7
1 6
1 5
Ind
ivid
ua
lV
alu
e
1
Me a n = 1 6 .0 6
UC L= 1 7 .0 5
LC L= 1 5 .0 7
1 .0
0 .5
0 .0
Mo
vin
gR
an
ge
R = 0 .3 7 1 4
UC L= 1 .2 1 4
LC L= 0
2 01 0S u b g ro u p 0
1 7
1 6
1 5
Ind
ivid
ua
lV
alu
e
1
Me a n = 1 6 .0 6
UC L= 1 7 .0 5
LC L= 1 5 .0 7
1 .0
0 .5
0 .0
Mo
vin
gR
an
ge
R = 0 .3 7 1 4
UC L= 1 .2 1 4
LC L= 0
22
The inputs that may have contributed to the days-of-stock measurement results
could include factors such as: forecast error, inventory builds, and constraint equipment
protection. These preliminary measurement observations will be used to identify
Measurement System Analysis issues later on in the Analyze phase of the DMAIC
process.
2.2 Identification of Assumptions
In this section are four key assumptions relating to this study and the business
case. One key assumption addresses the question to be answered from this paper: Can
Six Sigma Methodology be Successfully Applied to Transactional Processes? The
remaining key assumptions pertain specifically to the process improvement effort of this
business case.
The first assumption is that tools exist within the Six Sigma methodology that can
be successfully applied to this transactional process improvement effort. The company
represented in the business case had very little experience in using Six Sigma to improve
transactional processes and there is a minimal amount of published research to support
this assumption.
The second assumption is that there is an opportunity to reduce the amount of
cash investment in the semi-finished inventory category. Perhaps an opportunity exists
23
for optimization of the inventory but an optimized inventory may not lead to inventory
reduction, just a redistribution of the assets.
The third assumption is that there is sufficient Information Technology
infrastructure available to support implementation, measurement, and control of the
project solution. Information Technology infrastructure includes hardware, software, and
programming resources.
The final assumption is that there are sufficient personnel resources to support the
Six Sigma project. The faster projects are generated and the more people that are
involved, the less resources that are available to staff the project teams. The Six Sigma
project leaders also require a commitment from management to have some portion, or all
their current responsibilities, reassigned to other employees to be able to devote sufficient
time to the project.
2.3 Process Definition
The purpose of the Six Sigma Define phase is to seek an understanding of the
process including: identifying the process problem, determining the project goal, and (if
applicable) identifying the customers to be impacted by the process. The initial project
direction is typically set by the management team in the form of a “project charter.” A
good project charter includes (at a minimum) a statement of the problem, a statement of
the goal, and a summary of constraints and assumptions. The project is typically aligned
24
with a critical business strategy and, when possible, includes definition of the customer
specifications or process control limits.
2.3.1 Process Map
Although simplistic by nature compared to many other Six Sigma tools, the
process map is among the most essential project tools of Six Sigma. A process map is a
pictorial representation of the steps in a given process. The steps are presented
graphically in sequence so team members can examine the order presented and arrive at a
common understanding of how the process operates. Some of the most enlightening
information leading to process improvement comes from the actual process map creation
sessions as cross functional team members begin to hear about how work is done and the
process is managed in other parts of the business. [16] The process map serves as a
primary building block for the input variables to the Cause and Effect Matrix and the
Failure Mode and Effects Analysis.
The desired results of process mapping are to identify systems needing
measurement studies, process step disconnects, bottlenecks, redundancies, and potential
non-value added process steps. What makes these results possible is the classification of
the key input variables. Input variables are classified as controlled, uncontrolled, and
critical. Controlled inputs are input variables that can be changed and have a direct and
obvious effect on the output variables.
25
Uncontrolled inputs are input variables that also impact the output variables but
are difficult or impossible to control. Minimal effort should be spent in dealing with
uncontrolled input variables since the return on investment in time is very low.
Critical inputs are input variables that have been statistically shown to have a
major impact on the performance of the output variables. Critical input variables may be
controlled or uncontrolled in the current process flow and are typically defined using the
Cause and Effects Matrix and Failure Mode and Effects Analysis.
A criticism of Six Sigma is that its process improvement structure can paralyze an
organization from implementing obvious and less complicated solutions.[9] The project
team agreed that improvement opportunities defined as easy to identify, quick to
implement, and having controllable solutions would not be delayed by adhering to all of
the Six Sigma process steps. The team obtained approval from process owners to
proceed with this approach with understanding that a control plan would be developed
and implemented to manage process performance.
High level and detailed process maps were developed to facilitate communication
with various levels of management and process owners. The project team decided early
on that the maps would be kept as uncomplicated as possible. To accomplish this end the
maps use a minimal number of symbols and include descriptive labels to emphasize
important flows of data or physical inventory. Color was also used in the map to identify
transitions between different segments of manufacturing planning processes. Additional
detailed process map work included identifying the critical inputs and outputs for each
process step and a determination of whether the input is controlled or uncontrolled.
26
Figures 2-2 represents an example of the high-level process map and Figure 2-3 is an
example of the critical inputs and outputs for the Material Requirements Planning
Process.
Figure 2-2. Manufacturing Planning and Control High Level Process Map
27
Figure 2-3. Process Inputs and Outputs
Although tedious at times, the learning that was accomplished as a result of the
Six Sigma process mapping exercise was beneficial. The completion of the exercise led
to the following first impressions of the project focus and boundary:
1. Semi-Finished inventory is not a process but the result of many
processes.
2. The process improvement approach will be horizontal (across a
resource or resources) versus vertical (through a product line).
28
3. There is an apparent lack of control in the Material Requirements
Planning process.
4. An opportunity exists to synchronize dependent operations of
constrained resources if a constraint-based plan can be implemented.
5. Planning parameters and scheduling rules have an influence at each
planning and scheduling process step.
6. The current project charter is much too broad in definition and must be
reduced to a more manageable focus.
The Six Sigma process mapping approach is very similar to classical
flowcharting. Based upon personal experience, the activity of process mapping is value-
added in gaining insight into how a process works.
Six Sigma Methodology does add a dimension to conventional process mapping
that enhanced the ability of this project team to analyze the process. The identification
and documentation of the critical process inputs and outputs for each process step
provided a higher level of understanding around the identification of areas where
variability may have the greatest potential impact on process performance.
29
2.4 Process Measurement
The purpose of the Measurement Phase is to pinpoint the location or source of
variation by building a deeper understanding of existing process conditions and problems.
That knowledge will assist in narrowing the range of potential causes to investigate in the
Analyze Phase. The key tools this project team used in the Measurement Phase included:
• Cause and Effects Matrix
• Data Collection Planning
• Measurement System Analysis
The desired outcomes for the measurement phase included:
• Definition and prioritization of critical inputs
• Definition of measurement systems
• Definition of baseline process capability
• Documentation and communication of charter revisions (as necessary)
2.4.1 Cause and Effects Matrix
The Cause and Effects (C&E) Matrix is not a new tool to process improvement.
The C&E Matrix has also been called the fishbone or Ishikawa Diagram. Karoru
30
Ishikawa (1969) is credited for developing and using the cause and effects methodology
in the 1960s. Figure 2-4 depicts a basic Cause and Effects Diagram.
Figure 2-4. Basic Cause and Effects Diagram
The Cause and Effects (C&E) Matrix is a graphics tool used to explore and
display opinion about sources of variation in a process. Its purpose is to arrive at a few
key sources that contribute most significantly to the problem being examined. These
sources are then targeted for improvement. The C&E Matrix also illustrates the
relationships among the wide variety of possible contributors to the effect. The
conclusions reached from the C&E matrix exercise feed directly into the Failure Mode
and Effects Analysis (FMEA).
31
The main possible causes or effects of the problem are identified and then
categorized. The "Four M" categories are typically used as a starting point. The "Four
M’s" can be defined as follows: [16]
• Materials – consumables or raw inputs used in the process
• Machines – equipment, including computers and non-consumable tools
• Manpower – those who participate in and/or affect the process
• Methods – procedures, processes, work instructions
Different category names can be chosen to fit the process problem or these
general categories can be revised. Six Sigma consultants recommend the use of three to
six main categories that encompass all possible influences. [16] Brainstorming is typically
done to add possible causes to the main effect and more specific causes to the causes.
This subdivision into increasing specificity continues as long as the problem areas can be
further subdivided. The practical maximum depth of this diagram is usually about four or
five levels. [18]
The C&E Matrix builds on the work completed in the Cause and Effects Diagram
by assigning ratings of importance to both the process inputs and outputs. The first step in
constructing a C&E Matrix is to list the key output variables horizontally on the C&E
Matrix grid. The selection of critical outputs for the C&E Matrix is derived from a
combination of the critical outputs identified in the project charter and any additional
outputs the project team would like to ensure are not compromised by improvement
efforts. A rating scale is used to determine the degree of importance of the critical
outputs to process performance. The critical output rating scale for this business case
32
ranged from a high ranking of 10 to a low ranking of 6. The higher the ranking value the
more critical the output variable. The resulting critical output scale was developed to
differentiate the importance of each output and evolved through the consolidation and
elimination of a list of output variables.
The key input variables that may cause variability or nonconformance to one or
more of the key process output variables are then listed vertically on the left side of the
C&E Matrix. The input variables identified for this project were assigned 1 of 4 possible
values. A value of 9 was assigned for inputs having a significant or strong impact on the
output. A value of 3 was assigned for inputs having a moderate impact on the output. A
value of 1 was assigned for inputs having a weak impact on the output. A value of 0 was
assigned for inputs having no impact on the output. The scale used for rating the effect of
the input variable was developed to differentiate the importance of each input on an
output. Six Sigma consultants generally recommend a scale of 0,1,3,5 or 0,1,3,9. [16][18]
The next step is to determine the result for each process input variable by first
multiplying the key process output priority by the consensus of the effect for the key
process input variable and then summing the products. Each input is scored
independently relative to each output. A low rating number indicates that changes in the
input variable are perceived to have a small effect on the output variable. A high rating
indicates changes in the input variable can greatly affect the output variables.
The final version of the C&E Matrix should contribute towards reducing the
critical inputs from the trivial many to the vital few. As more is learned about the process
we begin to deduce which inputs can be filtered out because they appear to have little or
33
no effect on the desired outcome or output Y. The key process input variables can then
be prioritized by the results by summing of products and/or by using a percentage of the
total calculation.
Our project team struggled in two distinct areas during the construction of the
C&E Matrix. The first area of debate centered on the list of key outputs. The level of
semi-finished inventory and customer service were listed as critical outputs in the original
charter. The project team concluded the solutions this project generated to reduce
inventory could not be implemented without considering more than just semi-finished
inventory and service. Ignoring other outputs could result in a sub-optimized solution
whereby semi-finished inventory is reduced at the expense of another critical business
goal. For example, if the number of changeovers is increased for a resource and
production lot sizes are reduced as strategies to reduce inventory, we could create a
capacity constraint, decrease productivity, and increase costs. The resource may no
longer be able to support demand because of the extra changeover time and reduced run
time and may require overtime work to meet demand.
Avoiding the potential for sub-optimization required consideration of two
additional critical outputs. We labeled these outputs as “Capacity Impact” and “Raw
Material & Finished Good Inventory Level.” In addition to avoiding potential sub-
optimization, consideration of the critical outputs brought to light the conflict between
cost and cash as we focused on ways to optimize semi-finished inventory. Relative to
production resources the cost versus cash conflict can be restated as the conflict between
efficiency and flexibility. The team realized as solutions to optimize inventory were
34
developed, the relationship between efficiency (cost savings) and cash improvements
(flexibility) would be a pivot point for measuring the impact of the solution. The addition
of these two critical outputs made the development of the “Rating of Importance” scale
and the assignment of a rating number much more challenging.
The next area of debate focused on the consolidation or elimination of non-
essential inputs. It was my observation that this exercise can be hindered somewhat by
having a cross-functional team. Team members not close to the process tended to put
more stock into inputs having little or no influence on the performance of the process and
sometimes failed to understand the relationship or commonality between some inputs.
The significant difference between initial versions and the final version of the
C&E Matrix was the consolidation of the process inputs as well as the elimination of
process inputs that had no quantifiable effect on the critical outputs. This effort was
essential to reducing the project to a manageable and meaningful level.
Figure 2-5 provides the final version of the C&E Matrix for the Semi-Finished
Inventory optimization project.
35
Figure 2-5. Cause and Effects Matrix
0
50
100
150
200
250
300
350
0%
20%
40%
60%
80%
100%
120%Tier 1
Tier 2Tier 3
Tier 4
Y's 10 10 8 6
SF In
vent
ory
Leve
l
On
Tim
e &
In F
ull
Del
iver
y
Cap
acity
Im
pact
RM
& F
G
Inve
ntor
y Le
vel
Score
TierProcess Steps No. X's X's Relationship Score: 9=Strong; 3=Medium; 1=Weak; 0=None
1 4 6 9 9 9 9 306
2 2 2 9 9 9 3 270
3 2 2 9 3 9 9 246
4 5 6 3 3 9 3 150
Constrained or Unconstrained Resource; Schedule sequencing rules within resource; Planning Parameters
(Mins,Mult.,pallet qty); Part Buffer Style (ie lot-4-lot, consolidation, Time Buffer); MPS Parameters (Lead Time
Fence; Safety Stock)
Make-to-Stock Forecast Error; Make-to-Order Demand Variability
Yield (Planning for waste); Production Execution Feedback (Schedule Attainment or Supply Variability)
Operating Expense Policy; Planned Crewing & Coverage; Capacity Planning Feedback to MPS; Planning Rates; Utilization (crewing & coverage, constraint anchored planning); Resource Downtime (Planned/Unplanned)
36
From the process mapping activity, there were a total of 64 inputs identified that
carried forward to the C&E Matrix. Of the 64 inputs, 16 were determined to be critical
inputs to performance based upon their assigned rating values in the C&E Matrix
prioritization activity. The net result was the classification of the 16 inputs into four
distinct tiers that accounted for 83% of the total score. The tiers were created based on
the C&E Matrix score and the combination of process steps with shared inputs. An
example of a shared critical input was planning parameters. Planning parameters were
identified as an input to the Master Product Schedule and Material Requirements
Planning process.
Tier 1 received a total rating score of 306 and included four process steps and six
critical inputs. These critical were summarized as: planning and scheduling parameters;
unconstrained resource capacity; and production sequencing rules.
Tier 2 received a total rating score of 270 and included two process steps and two
critical inputs. Critical inputs for Tier 2 were summarized as: make-to-stock product
forecast error and make-to-order product demand variability.
Tier 3 received a total rating score of 246 and included two process steps and two
critical inputs. The critical inputs for Tier 3 included [planning for] process waste and
production execution (supply variability).
Tier 4 received a total rating score of 150 and encompassed five process steps and
six critical inputs. Critical inputs for Tier 4 included: operating expense policy; resource
crewing & coverage; capacity planning feedback to MPS; planning rates of production;
37
utilization (crewing & coverage, constraint anchored planning); and resource downtime
(planned/unplanned).
The C&E Matrix can be a very helpful tool in narrowing the focus of the
improvement effort by identifying input variables perceived to be critical to process
output performance. One shortcoming of using the C&E Matrix for this process
improvement effort is that it was invented by and for people involved in operational
process improvement. Operational processes tend to have simpler and more linear causal
structures (i.e. Process Step A→Process Step B→Process Step C→Process Step D). But
many transactional processes are not so simple and do not follow a repetitive feedback
loop (i.e. Process Step A→Process Step B→Process Step C→Process Step D). An
example of a non-repetitive feedback loop is when Process Step A causes Process Step B
and Process Step C, but Process Step C is in a different category than Process Step B.
When categorizing Process Step A, it is difficult to determine where it should be
categorized on the C&E Matrix.
2.4.2 Data Collection Plan
The process map and C&E Matrix focused the process improvement effort on
reducing from the trivial many process inputs to the vital few. In order to validate
specific process improvement observations, a data collection plan was needed to ensure
any data collected around a process change would reflect a response as a result of the
38
change. This included data that described the problem being studied, related conditions
that might provide clues about causes, and could be analyzed in ways that can answer
questions about the input measured. [16]
The data collection plan focused on three primary measurement categories and
were stated as follows:
1. Current Inventory State: The Current Inventory State represents the
baseline performance of semi-finished inventory as measured in days-of-
stock. It serves as the benchmark against which future process improvement
efforts resulting from this project will be measured. This inventory state was
measured as the actual semi-finished inventory days-of-stock over time
employing the current planning model (material constrained planning model
with current planning parameters).
2. Optimal Inventory State: The Optimal Inventory State represents the best
possible semi-finished inventory performance as measured in days-of-stock.
This inventory state was projected using a manufacturing model simulation.
The purpose of defining the Optimal Inventory State was to create a vision of
the potential improvement that is possible. This inventory state was measured
as the projected days-of-stock employing a material and capacity constrained
planning model for all production resources using the following planning
parameters:
a. Current SKU quantity and time buffers
b. Current Gateway SKU jumbo multiples
39
3. Goal Inventory State: The Goal Inventory State defines the expected
outcome from the implementation of the improvement actions. The original
project charter defined the goal as a $2 million dollar reduction in semi-
finished inventory. As specific improvement activities are identified, the
original project goal may need modification. (The degree of modification
may depend on the data used to create the original charter and the process
knowledge of the project sponsors.) This inventory state was measured as the
projected semi-finished inventory days-of-stock over time employing a
material constrained plan for all resources and a capacity constrained plan for
selected gateway resources using the following planning parameters:
a. Current SKU quantity and time buffers
b. Current Gateway SKU jumbo multiples
c. Current downstream SKU multiples
The project team hypothesized the average inventory differences resulting from
comparing the results of the three inventory measurement states would not only provide a
means for measuring the effect of process change, but also assist in further clarifying the
project Entitlement and the project Goal. Analysis of the three inventory states will be
discussed in greater detail in Section 2.4.3.
The primary questions the team strived to answer using the data collection plan
were: Do the observations developed from the FMEA exercise represent an opportunity
for inventory optimization? If so, how much is the opportunity worth?
40
2.4.3 Measurement System Analysis
Measurement System Analysis (MSA) is used to assess the statistical properties
of process measurement systems. Measurement systems can include collection
procedures, gages, and other test equipment used to collect data for analyzing process
problems.
The purpose of the MSA is to ensure or validate the quality of the process
measurement system. The analysis should include design and certification, control,
capability assessment over time, and repair and re-certification. [18] The goal is to
pinpoint the location or source of problems as precisely as possible by building a factual
understanding of existing process conditions and problems. The knowledge acquired
from the MSA will help narrow the range of potential causes needing investigation in the
Analyze phase of the Six Sigma DMAIC model.
For operational processes, measurement variance is typically defined through
assessment of the statistical properties of repeatability, reproducibility, bias, stability, and
linearity. Collectively this assessment is referred to as a Gage Repeatability and
Reproducibility (Gage R&R) study. [18] The equation to follow is often used as a
simplified representation of process variability and tolerance spread:
σ2T = σ2
P + σ2M
where: σ2T = Total Variance
σ2P = Process Variance
σ2M = Measurement Variance
41
In order to conduct a Gage R&R study the following characteristics are essential:
• The data must be in statistical control. The variation from the
measurement system is from common causes only and not special causes.
• Variability of the measurement system must be small compared with both
the manufacturing process and specification limits.
• Increments of measurement must be small relative to both process
variability and specification limits.
A substantial amount of additional information is available relating to Gage R&R
studies that will not be covered. The purpose of introducing Gage R&R in this paper is to
provide background information that establishes a basis for understanding why it was not
applied to this transactional process.
The Repeatability part of Gage R&R addresses the variation between successive
measurements of the same part, the same characteristic of the part, by the same person
using the same instrumentation. Reproducibility attempts to capture the difference in the
average of measurements made by different people or operators using the same or
different instruments measuring the same characteristic. A Gage R&R study could have
been applied to the production reporting aspect of inventory and material control.
Production reporting is the process of recording the input and output of labor and material
resulting from production activity. Inaccurate production reporting could directly affect
the accuracy of inventory balances and inventory usage. Errors in production reporting
can result in inventory performance measurement errors.
42
An indicator of production reporting accuracy is inventory cycle count
performance. If the value of inventory adjustments resulting from reporting errors is low,
the effect of production reporting errors on inventory measurement is low. The average
cycle count adjustment value for a one-year period (January 01 through December 01)
was $23,000. This value included all inventory classifications (semi-finished, finished
goods, packaging, and raw materials). The average adjustment value was .10% of the
total average inventory value for the same measurement period. The cycle count
accuracy was deemed to have virtually no effect on inventory measurement accuracy and
not investigated any further (the cycle count performance is presented in greater detail in
section 2.5.2). Since the primary and secondary measures for this business case did not
rely on operators to measure and record data, repeatability and reproducibility were
judged to be irrelevant.
Given the Gage R&R study was not applicable to the measurement system for this
business case, the project team focused on the following MSA areas:
• Definition of the type of measurement information that would best
represent the process
• Certification of the design of how measurement data is recorded and
reported
• Assessment of the statistical stability of the measurement systems
• Definition and assessment of process capability
The definition of the type of measurement information that would best represent
the process entailed the questioning of whether inventory in cost dollars was the right
43
aspect of the process outcome to be measured. The project team concluded this measure
would not indicate whether the level of inventory was optimal. Inventory cost dollars
could be lower than previous time periods but relative to usage could be much less active
(slower moving). An inventory days-of-stock (DOS) metric was added as the primary
measure of inventory optimization.
The initial MSA design and certification effort for this business case focused on
ascertaining whether the data generated for calculating the Current Inventory State
reported from a reliable source conformed to the operational definitions established by
the data collection plan, and whether the data being measured was stable.
The data collected for measuring the Current Inventory State was generated from
preexisting software programs used to record and report inventory transactions. The
inventory transaction reporting system and software code had previously been validated
and documented by the Information Technology group. A random sampling of various
stock keeping units were validated by comparing the live inventory system data with the
reported data. The current inventory data collection system was deemed valid.
Unlike operational processes, where recording data can be done without
influencing the performance of the process, the inventory states were manipulated using a
simulation model in order to generate data. The material requirements planning data from
the live planning model were copied to a simulation model, regenerated with model
parameter changes, and inventory results were recorded and reported using Microsoft
Query and Microsoft Excel respectively.
44
Process control charts were used to assess the statistical stability of the simulation
data for each inventory state. Statistical instability is defined as having an unnatural
pattern or data points outside of the control limits. Typically a pattern is defined using
out-of-control rules or conditions. For example, the I-MR Chart shown in Figure 2-6
indicates one point, labeled with a 1, is more than 3 sigmas from the average. As
expected, the simulation inventory states included data points outside the control limits as
projected inventory improvements from the baseline were realized based upon the effect
of the simulation parameters. The process I-MR charts for each inventory state are
provided in Figures 2-6, 2-7, and 2-8.
Figure 2-6. Current Inventory State Baseline I-MR Chart
2 01 0S ub g ro up 0
1 7
1 6
1 5
Ind
ivid
ua
lV
alu
e
1
Me a n =1 6 .0 6
UC L=1 7 .0 5
LC L=1 5 .0 7
1 .0
0 .5
0 .0
Mo
vin
gR
an
ge
R =0 .3 7 1 4
UC L=1 .2 1 4
LC L=0
2 01 0S ub g ro up 0
1 7
1 6
1 5
Ind
ivid
ua
lV
alu
e
1
Me a n =1 6 .0 6
UC L=1 7 .0 5
LC L=1 5 .0 7
1 .0
0 .5
0 .0
Mo
vin
gR
an
ge
R =0 .3 7 1 4
UC L=1 .2 1 4
LC L=0
45
Figure 2-7. Goal Inventory State I-MR Chart (simulation)
Figure 2-8. Optimal Inventory State I-MR Chart (simulation)
2010S ubgroup 0
16
15
14
13
Ind
ivid
ual
Val
ue 1
11
1
Me an=13.89
UCL=14.57
LCL=13.20
0.90.80.70.60.50.40.30.20.10.0
Mov
ing
Ran
ge
R=0.2579
UCL=0.8426
LCL=0
2010S ubgroup 0
16
15
14
13
Ind
ivid
ual
Val
ue 1
11
1
Me an=13.89
UCL=14.57
LCL=13.20
0.90.80.70.60.50.40.30.20.10.0
Mov
ing
Ran
ge
R=0.2579
UCL=0.8426
LCL=0
2010S ubgro up 0
16.515 .514 .513 .512 .511 .510 .5
9 .58 .5
Ind
ivid
ua
lV
alu
e
11
11 1
1
1 1 1 1 1 1 1 1 1
Me an=11 .44UCL=12.5 5
LCL=10.3 3
1 .5
1 .0
0 .5
0 .0
Mo
vin
gR
an
ge
R=0 .4158
UCL=1.35 9
LCL=0
2010S ubgro up 0
16.515 .514 .513 .512 .511 .510 .5
9 .58 .5
Ind
ivid
ua
lV
alu
e
11
11 1
1
1 1 1 1 1 1 1 1 1
Me an=11 .44UCL=12.5 5
LCL=10.3 3
1 .5
1 .0
0 .5
0 .0
Mo
vin
gR
an
ge
R=0 .4158
UCL=1.35 9
LCL=0
46
A decision the project team struggled with early on was whether capability
metrics could or should be used to represent the process. Many transactional processes
are not conducive to having customer or process specifications assigned that would be
meaningful to measuring the capability of the process. When meaningful customer or
process specifications do not exist, other process performance metrics should be
considered. The transactional process examples found in my research typically avoided
capability metrics and used measurements such as cycle time and costs to define process
performance. [18]
An assessment was completed of the current measurement systems for the other
critical outputs identified in the C&E Matrix that we did not want to negatively impact as
a result of reducing semi-finished inventory. These measures were also the outcome of
transactional processes. Control charts were used to verify and assess the data and a
review of the data sources was completed.
Service was defined as the percentage of order lines on time. The data is
available by product commodity by week. A customer order can be generated from the
following sources: direct customers, distribution centers, or via intra-company
manufacturing plants. Customer orders were evaluated based upon a comparison of the
customer need date versus the actual shipment date. If an ordered item was shipped on
the customer need date the line item was counted as a hit and assigned the value of 1. If
an ordered item was shipped later than the customer need date, the line item is counted as
a miss and assigned the value of 0. The total number of line item hits is divided by the
total number of line items for the week and reported as the percent service. The service
47
measure was necessary to ensure the improvements implemented to optimize inventory
would not decrease the level of service provided to our customers. An np control chart
was used to measure the number of defects (late order lines) per n samples (total order
lines) per week.
Capacity availability was measured in terms of the machine hours forecasted
versus the total hours available. The machine hours forecasted is derived by dividing the
forecasted demand quantity by the planning rate for each stock-keeping unit by resource.
The planning rate represents the time required to set-up and run the product. (The
planning rate was updated quarterly based upon the average rate using the last six months
of production history.) The capacity availability measure was necessary to ensure the
improvements implemented to optimize inventory would not increase the amount of
capacity needed to support demand.
Performance measurement systems were already in place for the complementary
critical outputs of service and capacity. Examples of the baseline control charts for
service and capacity are provided in Figures 2-9 and 2-10 respectively.
48
Figure 2-9. Service np Chart
Figure 2-10. Capacity Availability I-MR Chart
252015105Subgroup 0
6050403020100
-10-20-30
Indi
vidua
lVal
ue
Mean=16.25
UCL=51.63
LCL=-19.13
50403020100
Mov
ing
Rang
e 1
R=13.30
UCL=43.47
LCL=0
252015105Subgroup 0
6050403020100
-10-20-30
Indi
vidua
lVal
ue
Mean=16.25
UCL=51.63
LCL=-19.13
50403020100
Mov
ing
Rang
e 1
R=13.30
UCL=43.47
LCL=0
5 04 03 02 01 00
1 0 0
5 0
0
S a m p le N u m b e r
Sa
mp
leC
ou
nt
N P =1 3 .5 3U C L=2 4 .4 9
LC L=0
5 04 03 02 01 00
1 0 0
5 0
0
S a m p le N u m b e r
Sa
mp
leC
ou
nt
N P =1 3 .5 3U C L=2 4 .4 9
LC L=0
49
The special cause test criteria used to flag out-of-control data points for the
service control chart was defined as one point more than 3 sigmas from the centerline. A
review by production commodity of each special cause was conducted on a weekly basis
by the plant management team. Where possible, plans to address the special cause are
developed and implemented.
The special cause test criteria used to flag out-of-control data points for the
capacity availability I-MR chart was defined as one point more than 3 sigmas from the
centerline. Resource capacity reviews were conducted on a weekly basis by each
functional area. The review (by resource) included an analysis of capacity versus
projected demand, historical rate performance, and historical schedule attainment
performance.
The data used for reporting and measuring customer service was validated by
comparing the measurement data with the actual customer order shipment history for a
sample of stock-keeping units across the highest sales volume product commodities. The
data used for reporting and measuring capacity was validated by comparing the
measurement data with actual production reports for a sample of stock-keeping units
across each gateway resource.
Although a direct correlation does not always exist between inventory
performance and service performance and inventory performance and capacity
availability, process owners felt more comfortable including these secondary metrics in
the monitoring of the process changes resulting from this project.
50
Whether a process is operational or transactional, MSA techniques strive to
identify the contribution of measurement error to the perceived variability of the process.
That being said, there were Six Sigma tools within the MSA that were more difficult to
apply to a transactional process. For an operational process, measurement variation can
be more easily traced to a specific resource - and the tools, materials, work methods, and
environment surrounding the resource. A transactional process is subject to a variety of
internal and external influences and presents a much greater challenge in identifying and
modeling those influences. Transactional and operational processes appear to be similar
because their measurement variation can be influenced by factors pertaining to work
methods, the environment, process rules, and customers.
For those new to Six Sigma, the overwhelming urge to apply MSA tools that may
not fit must be avoided. An improperly used MSA tool, like the Gage R&R, may not
only be worthless, but may unnecessarily damage the credibility of a very effective
measurement system.
2.5 Process Analysis
The purpose of the Process Analysis phase is to begin understanding the
relationships between the process inputs and outputs and to identify potential sources of
process variability. The key steps in this phase were to:
• Complete the Failure Mode and Effects Analysis
• Complete the Multivariate Analysis
51
• Define and Complete Design of Experiments (DoE)
The desired outcomes of this phase included:
• Reduce the number of process input variables to a manageable number
• Determine high-risk input variables from the FMEA
• Determine relationships between process inputs and process outputs
• Charter revisions (as necessary)
• Improvement strategy
2.5.1 Failure Mode and Effects Analysis
The primary objective of the Failure Mode and Effects Analysis (FMEA) is to
identify and prioritize ways a process can fail and eliminate or reduce the risk of failure.
Identification of process failures is crucial for enabling the team to improve the process in
a preemptive manner – before failures occur. The inputs to the FMEA include the
Process Map, C&E Matrix, process or product history, and process technical procedures.
The outputs of the FMEA are a prioritized list of actions to prevent causes or to detect
failure modes and a record of actions taken.
The FMEA is not a new tool. It was first used in the 1960s in the Aerospace
industry during the Apollo missions. The FMEA was developed further in the 1970s by
the Navy (documented in MIL-STD-1629) and in the automotive industry to address
liability costs. [18]
52
The FMEA proved to be a very useful tool for our business case in validating the
ratings of importance determined from the C&E Matrix. Although the output is for the
most part subjective, the FMEA process structure and format facilitates the process
improvement effort by directing the team to key input variables where multivariate
studies would help define the impact of variability on the process.
The FMEA document contains five major information categories pertaining to the
most important process inputs as identified in the C&E Matrix. The first category for
consideration is labeled the “Potential Failure Mode.” This category attempts to answer
the question: What could go wrong in the process? Consideration is given to issues that
could arise only under certain process operation conditions. An operational process
failure example could include manufacturing equipment issues resulting from excessive
temperature or high humidity. A transactional process failure could include customer
service or inventory issues resulting from forecast inaccuracy.
The second category of the FMEA is labeled the “Potential Effect(s) of Failure.”
This category attempts to answer the question: What are the impacts of the failure
occurring? Potential Effects of Failure can be isolated by understanding the impact of the
input(s) on customer requirements, on downstream processes, or on related processes.
The third category is labeled “Potential Cause(s) of Failure.” This category
attempts to answer the question: What are the potential causes of this failure? The Cause
indicates a design weakness that causes the Failure Mode to occur.
The fourth category of the FMEA is the “Current Controls” section. This section
attempts to answer the question: What are the existing controls and procedures
53
(inspections and tests) that prevent either the Cause or the Failure Mode? Current
controls can be existing methods/devices in place to prevent or detect Failure Modes or
Causes.
The final major category of the FMEA is the rating system that assigns the Risk
Priority Number (RPN) for the Severity, Occurrence, and Detection. The RPN is the
output of the FMEA. The RPN is a calculated number based on information provided in
the assessment of the Potential Failure Modes, Effects, and the ability of the Current
Controls to detect the failures before reaching the customer or final output stage. The
formula for calculating the RPN is as follows: RPN = Severity * Occurrence * Detection
Severity measures the importance of the Effect on customer requirements.
Occurrence measures the frequency with which a given Cause occurs and creates Failure
Mode. Detection measures the ability of the current control scheme to detect or prevent.
Most project teams use an RPN Detection Scale numbered 1 through 5 or 1
through 10 depending on the necessity and ability to differentiate. Our project team
agreed to use a scale of 1 through 5. A scale of 1 through 10 could not provide any
additional differentiation in detection for this project. Figure 2-11 represents the FMEA
Detection Rating Scale assembled for this business case.
54
Figure 2-11. Failure Mode and Effects Analysis Detection Rating Scale
The primary input to the FMEA is the C&E Matrix. These are the steps the
project team followed to complete this project’s FMEA:
1. Determined the ways in which the input could go wrong for the top eight
inputs identified in the C&E Matrix (Failure Modes).
2. Determined the Effects of Failures on the customer and process capability for
each input Failure Mode.
3. Identified potential causes of each Failure Mode.
4. Listed the current controls for each Cause or Failure Mode.
5. Determined Severity, Occurrence, and Detection Rating Scales.
Rating Severity of Effect Likelihood of Occurrence Ability to Detect
5Significant contribution to excess
SF Inventory and poor service performance.
Very High: Failure is almost inevitable Unable to detect
4 Major contribution to excess SF inventory High: Repeated failures Remote chance of detection or
detection after the fact
3Minor contribution to excess SF
inventory and major contribution to poor service
Moderate: Occasional failures Low chance of detection
2 Minor contribution to excess SF Inventory Low: Relatively few failures High chance of detection
1 No Effect Remote: Failure is unlikely Almost certain detection
55
6. Assigned Severity, Occurrence, and Detection ratings for Effects, Causes, and
Controls respectively.
7. Calculated the RPN’s for each Failure Mode.
8. Developed list of recommended actions to reduce or minimize high RPN’s.
Figure 2-12 represents a summarized version of the FMEA for this business case:
Figure 2-12. Failure Mode and Effects Analysis Summary Diagram
0
10
20
30
40
50
60
70
0%
20%
40%
60%
80%
100%
120%1
2
3
C&E TierFMEA
RankingFMEA Corrective Action Category X Failure Modes S
EV
OC
C
DE
T
RP
N
1 1Constraint-anchored planning
Unconstrained Make Order w/qty and Need Date Material available. No capacity 5 4 3 60
1 2Planning
Parameter Review
Planning Parameters
Combination of Parameters Too High or Too Low; Time/Qty Buffer Too Large, In Wrong Location or Too many in Supply
Chain; Minimum or Multiple Order Qty Too High; Consolidation Style Too Long
4 3 4 48
1 3 Sequencing Rules
Scheduling sequencing rules within resource
Changing priorities; incorrect input inventory balance; planner error;
quality/rejects/higher waste; poor schedule attainment; unplanned downtime; difficult to plan in families; supply/supplier delivery
4 5 2 40
56
The FMEA exercise led the project team to the following conclusions:
1. The inputs associated with the materials requirements planning and
scheduling process contribute most to the outcome of semi-finished inventory.
The variability in the planning and scheduling can be described by the timing
and/or quantity of supply and/or demand. The team summarized this
relationship using Figure 2-13.
Figure 2-13. Sources of Material Requirements Planning Variability
Material received/produced for
more/less than planned
Requirements for more/less than plannedQuantity
Material received/produced
earlier/later than planned
Requirements move from one period to anotherTiming
Type
Material received/produced for
more/less than planned
Requirements for more/less than plannedQuantity
Material received/produced
earlier/later than planned
Requirements move from one period to anotherTiming
SupplyDemand Type
Sources of Variability
Material Requirements Planning
Material received/produced for
more/less than planned
Requirements for more/less than plannedQuantity
Material received/produced
earlier/later than planned
Requirements move from one period to anotherTiming
Type
Material received/produced for
more/less than planned
Requirements for more/less than plannedQuantity
Material received/produced
earlier/later than planned
Requirements move from one period to anotherTiming
SupplyDemand Type
Sources of Variability
Material Requirements Planning
57
2. The defect of the planning and scheduling process is defined as variation
in synchronization between producing and consuming resources caused by:
a. Sequencing rules within and between resources.
b. Production in excess of demand caused by a variety of factors
including lot sizing, changeover rules, buffers, demand and supply
variability, and planning errors.
c. Uncontrolled Material Requirements Planning resulting from
unconstrained and unpredictable demand and supply plans.
3. The process improvement effort will be narrowed in focus to the top 2
resources that contribute most to the flow of semi-finished inventories. These
resources were termed the gateway resources. These gateway resources
exhibit the following characteristics: the output of a single product can be
transformed into several distinct products at downstream work centers: the
number of end items is large compared to the number of input raw materials;
and the equipment is generally capital intensive and highly specialized. [21]
The product flow diagram exhibiting the characteristics of gateway work
centers is shown in Figure 2-14.
58
Figure 2-14. Gateway Product Flow Diagram [21]
A unique feature of the gateway work centers chosen is their similar technological
process capabilities. Of the 180 SKU’s manufactured across both resources, more than
half of them can be run on either resource. A second unique feature is the sharing of
many of the same raw materials. A third feature is the sharing of many common
resources – including Focused Factory Management, supply chain analyst, maintenance,
engineering support, and equipment operators.
RawMaterials
Converting Work Centers
Gateway Work Centers
Sub-assembly Work Centers
Customers
RawMaterials
Converting Work Centers
Gateway Work Centers
Sub-assembly Work Centers
Customers
59
The first improvement action identified from the FMEA work was to implement a
constraint-anchored planning process. A planning and scheduling environment whereby
demand requirements are unconstrained and constantly variable (depending on material
availability) creates an unstable inventory-planning environment. The purpose of
constraining the plan is not only to stabilize and smooth production requirements but also
to ensure material that is produced will be consumed (synchronization). Constraint-
anchored planning generated the highest FMEA RPN score of 60.
The second major improvement action was to understand the impact of planning
parameters on the level of semi-finished inventory for the gateway resources. The key
process inputs associated with this action item scored an FMEA RPN of 48. The
foundations for this corrective action are as follows:
1. Parameters are currently evaluated from a resource view. A planning
parameter model will be created to evaluate the effects of various planning
parameters from a supply chain view.
2. A primary goal for this action is to understand the dynamic and complicated
relationship between parameters (i.e. production frequency, minimums, multiples,
lead times, time buffers, quantity buffers, etc.). Optimal Order Quantity logic will
be tested and incorporated into the model to understand the cash versus cost
tradeoff.
60
The final corrective action identified from the FMEA was to develop an optimal
production sequence plan for the critical resources. The team labeled this change as the
Group Technology Planning and Scheduling process. The goal of this corrective action
was to reduce the variability in production cycle frequency as well as optimize equipment
changeover effectiveness.
The FMEA is considered to be a working document. Once improvement efforts
are identified to reduce the RPN’s for critical inputs, the FMEA document may need to
be revisited to verify the effect of the changes compared to the original RPN’s. The
improvement opportunities identified in the FMEA will be covered in greater detail as we
proceed through the DMAIC process.
2.5.2 Multivariate Analysis
Multivariate Analysis is a technique that can provide insight into the relationship
between key process input variables and key process output variables. Through the
graphical visualization of the input and output relationship a lot of information can be
evaluated about the process without modifying the process and insight can be gained into
where improvement efforts should be focused.
Many statistical practitioners and Six Sigma consultants refer to Multivariate
Analysis of Variance as MANOVA. MANOVA is a tool used to determine the
significance of several factors on the performance of key output process variables. Other
61
statistical tools like Chi-Square test, t-test, and Analysis of Variance (ANOVA) are
available to analyze the significance of a single factor on the performance of the key
output process variables. Regardless of whether a single factor or multiple factors are
analyzed, this paper will refer to the results presented as Multivariate Analysis.
Some controversy exists around whether Multivariate Analysis testing tools or
Process Control Charts are best for understanding the impact of key input variables on the
performance of a process. Some authors argue that a control chart is “a perpetual test of
significance”[20] and “process monitoring resembles a system of continuous statistical
hypothesis testing. ”[20] W. Edwards Deming wrote: “Some books teach that use of a
control chart is test of hypothesis: the process is in control, or it is not. Such errors may
derail a study…rules for detection of special causes and for action on them are not tests
of a hypothesis that a system is in a stable state.”[21] Deming also argued hypothesis
testing was inappropriate in industry where practical applications required “analytical”
studies because of the dynamic nature of the processes for which there is no well-defined
finite population or sampling frame. [21]
Regardless of the controversy of which tools to use in Multivariate Analysis, the
overall concept can apply to transactional processes. For this business case, a
combination of process control charts and box-whisker plots were used to evaluate the
effect of specific variables on the process output of inventory. These tools were chosen
primarily because of their simplicity in use and function.
Based upon the work completed in the FMEA exercise, four specific areas of
variability were studied: inventory balance accuracy, demand variability, supply
62
variability, and schedule changes. Although inventory balance accuracy was deemed to
be a key input variable for inventory, the team decided to verify this assumption via cycle
count accuracy.
A cycle count is an inventory accuracy audit technique where inventory is
counted on a cyclic schedule rather than once a year. The key purpose of cycle counting
is to identify items in error, thus triggering research, identification, and elimination of the
cause of the errors. [2] For this business, the plant set a target of 98% average inventory
balance accuracy.
The analysis showed for the last 12 months Total Inventory Adjustment Value
(Absolute Value) averaged just about $23,000 compared with a total inventory average of
just over $34 million. For the gateway work centers, adjustments averaged just over
$9,000 against an average inventory level of $6 million. The contribution of inventory
adjustments to the effectiveness of the Material Planning Process was not significant.
Total plant cycle count accuracy averaged 99.9% and Gateway cycle count accuracy
averaged 99.9% accuracy for the most recent measurement periods. Figures 2-15 and
2-16 report the performance of cycle count accuracy using np control charts to indicate
percent defective.
63
Figure 2-15. Inventory Cycle Count np Chart
Figure 2-16. Gateway Inventory Cycle Count np Chart
3 53 02 52 01 51 050
0 .0 6
0 .0 5
0 .0 4
0 .0 3
0 .0 2
0 .0 1
0 .0 0
S a m p le N u m b e r
toT
ota
lG
ate
wa
yIn
ven
tory
%G
ate
wa
yIn
ven
tory
Ad
j.V
alu
e
N P =0 0 0 3 .4 6
U C L= 0 .0 5 6 1 5
LC L= 0
3 53 02 52 01 51 050
0 .0 6
0 .0 5
0 .0 4
0 .0 3
0 .0 2
0 .0 1
0 .0 0
S a m p le N u m b e r
toT
ota
lG
ate
wa
yIn
ven
tory
%G
ate
wa
yIn
ven
tory
Ad
j.V
alu
e
N P =0 0 0 3 .4 6
U C L= 0 .0 5 6 1 5
LC L= 0
3 53 02 52 01 51 050
0 .1 0
0 .0 5
0 .0 0
S a m p le N u m b e r
toT
ota
lIn
ven
tory
Va
lue
%In
ven
tory
Ad
j.V
alu
e
N P =0 .0 0 1 0 4 2
U C L=0 .0 9 7 8 8
LC L=0
3 53 02 52 01 51 050
0 .1 0
0 .0 5
0 .0 0
S a m p le N u m b e r
toT
ota
lIn
ven
tory
Va
lue
%In
ven
tory
Ad
j.V
alu
e
N P =0 .0 0 1 0 4 2
U C L=0 .0 9 7 8 8
LC L=0
64
Demand variability was defined as the variation in the quantity of product ordered
by a customer. A customer can be an internal (downstream resource) or an external
(purchaser of goods or services) entity. In this instance, demand variability includes
forecast error and the effects of the consuming resources’ production frequency. The
data was summarized by product commodity since the gateway resources served many
downstream resources. The amount and significance of demand variability was
dependent on the business category a commodity represented. For commodities that
included a higher ratio of make-to-order products, demand variability was more
pronounced and included outliers (more intermittent demand). For commodities that
included a higher ratio of make-to-stock products, demand variability was much less
pronounced. Figure 2-17 displays the box plot representing the demand variability data.
Figure 2-17. Demand Variability Box Plot
2 9 8 02 9 7 52 9 7 42 9 7 22 9 6 82 9 6 52 9 2 52 9 2 42 9 1 92 9 1 1
1 0 0 0 0 0 0
8 0 0 0 0 0
6 0 0 0 0 0
4 0 0 0 0 0
2 0 0 0 0 0
C o m m o d it y
SF
De
ma
nd
$
2 9 8 02 9 7 52 9 7 42 9 7 22 9 6 82 9 6 52 9 2 52 9 2 42 9 1 92 9 1 1
1 0 0 0 0 0 0
8 0 0 0 0 0
6 0 0 0 0 0
4 0 0 0 0 0
2 0 0 0 0 0
C o m m o d it y
SF
De
ma
nd
$
65
Supply variability was defined in two different dimensions. The first dimension
focused on the item schedule attainment for the gateway resources and resources directly
downstream from the gateway resources. The second dimension focused on the cycle
frequency of group technology families for the gateway resources using reported
production data.
The Item schedule attainment measure compares the quantity produced to the
quantity scheduled. If the quantity produced is within +/- 10% of the quantity scheduled
the production order is counted as a 1. If the quantity produced is not within +/- 10% of
the quantity scheduled the production order was counted as a 0. Item schedule attainment
is measured as the percent of the total number of 1’s divided by the total number of items
scheduled. Item schedule attainment performance for downstream resources looked to be
a critical input in the managing inventory. The worse the downstream item schedule
attainment, the greater the chance inventory produced or scheduled to be produced would
not be consumed when initially planned. The item schedule attainment box plot reflects a
high degree of attainment variability demonstrated by two outliers and elongated first
quartiles. Figure 2-18 illustrates the item schedule attainment performance for the five
most critical downstream resources from the gateway work centers:
66
Figure 2-18. Item Schedule Attainment Box Plot
Cycle frequency is defined as the amount of time (in days) between production
runs. (The term lead-time is sometimes used synonymously with cycle frequency.) For
this multivariate analysis the amount of time between product families (group
technologies) by gateway resource was studied.
Cycle frequency was viewed as a contributing factor to the Material Planning
Process and the average amount of inventory that is carried. The longer the cycle
frequency, the more inventory that needs to be produced to cover all of the projected
demand until the next production run. The more variable the cycle frequency the more
safety stock is needed to protect against supply variability.
54321
1.0
0.5
0.0
Resource Group
Item
Sch
ed.A
ttain
%
54321
1.0
0.5
0.0
Resource Group
Item
Sch
ed.A
ttain
%
67
Figure 2-19 represents a sample of the baseline data accumulated for one product
family on a gateway resource.
Figure 2-19. Baseline Cycle Frequency I-MR Chart
The final area of analysis focused on schedule changes for the gateway resources.
Schedule changes were believed to affect the capability of synchronizing production and
consumption of inventory. A schedule change log was developed for the gateway
resource production analyst and production supervisors for categorizing schedule changes
based upon the following criteria: Business Priority Changes, Process Failures,
Equipment Problems, Material Availability Problems, Production Causes, and Other
2010Subgroup 0
80
40
0
Indi
vidua
lVal
ue
Mean=23.75
UCL=73.86
LCL=-26.36
70605040302010
0
Mov
ing
Rang
e
R=18.84
UCL=61.56
LCL=0
2010Subgroup 0
80
40
0
Indi
vidua
lVal
ue
2010Subgroup 0
80
40
0
Indi
vidua
lVal
ue
Mean=23.75
UCL=73.86
LCL=-26.36
70605040302010
0
Mov
ing
Rang
e
Mean=23.75
UCL=73.86
LCL=-26.36
70605040302010
0
Mov
ing
Rang
e
R=18.84
UCL=61.56
LCL=0
68
Issues. Schedule changes were viewed in total using a Process Control Chart and from a
causal analysis perspective using a Pareto Chart. Figures 2-20 and 2-21 are the schedule
change control chart and schedule change Pareto chart respectively.
Figure 2-20. Schedule Change Control Chart
20100
10
5
0
S a m p le Nu m b e r
Sa
mp
leC
ou
nt
C=4.238
UCL=10.41
LCL=0
20100
10
5
0
S a m p le Nu m b e r
Sa
mp
leC
ou
nt
C=4.238
UCL=10.41
LCL=0
69
Figure 2-21. Schedule Change Pareto Chart
Whether the process is operational or transactional, if data is available that can be
used to represent process input variables, a Multivariate Analysis can be key in providing
insight into the relationship between key process input variables and key process output
variables. Multivariate analysis can prove to be an invaluable tool in beginning to
validate assumptions formed from the FMEA around the identification of critical process
inputs. The Six Sigma structure seems to tie Multivariate Analysis in nicely with the
previous steps in the process and sets the stage for continuing the process improvement
effort.
WC Desc. (All)
Cause Count CodeDate Range B O M C E P12/1/2002 - 3/30/2003 54 53 50 38 27 13
Schedule Attainment Causal Analysis
54 5350
38
27
13
0
10
20
30
40
50
60
B O M C E P
Cause Code
Freq
uenc
y
B=Business ReasonC=Coverage ShortageE=Equipment Problem
M=Material ShortageO=OtherP=Process/Quality Issue
70
2.5.3 Designed Experiments
A designed experiment is a systematic method for collecting data to understand
the cause and effect relationships in a process. Process learning can occur through
passive observation of naturally occurring events, by creating informative events, or
through experimental design by manipulating input variables. A designed experiment
focuses on the latter – manipulating input variables to observe changes to the output
responses. The goal of the designed experiment is to identify the influential inputs that
minimize the effect of input variables on the output and to facilitate centering output on
its target.
Some processes are very conducive to conducting a designed experiment while
the process is in operation. Other processes are not amiable to designed experiments
because the variable changes may drive the process towards an outcome that is difficult
to recover from. The degree to which a planned experiment can be run on a process that
is in operation is somewhat dependent on the amount of change to be introduced. This
business case is an example of a process that does not lend itself to experimentation while
the process is in operation.
Using the intelligence gathered from the FMEA and multivariate analysis, the
designed experiments methodology was used to test our improvement recommendations
in terms of a hypothesis. The research hypothesis must state an expectation or
relationship to be tested.
71
Prior to beginning the hypothesis testing, to follow is a summary of the
conclusions realized from the FMEA exercise:
Conclusion 1: Semi-finished inventory is not a process. Semi-finished inventory
is an outcome of many processes and their variability. The planning and
scheduling process were determined as contributing most to the outcome of semi-
finished inventory.
Conclusion 2: The process improvement effort will be focused on the gateway
resources. The feedback from the initial data collected, multivariate analysis, and
the FMEA all point to the gateway resources as contributing most to the semi-
finished inventory levels.
Conclusion 3: The defect of the planning and scheduling process was defined as
variation in synchronization between producing and consuming resources caused
by: sequencing rules within and between resources; production exceeding
demand due to lot sizing, changeover rules, buffers, demand and supply
variability, planning errors, etc.; and, “uncontrolled” Material Requirements
Planning resulting from unconstrained demand and supply plans, lack of firm
planning, and lack of inventory allocation management.
The first major corrective action from the FMEA was to implement a constraint-
anchored planning process. This action item fits well with the goal of Six Sigma, which
is to identify and reduce variability within the process. A planning and scheduling
environment where demand requirements are unconstrained and constantly in flux creates
a planning environment driven by variability. The purpose of constraining the plan is not
72
only to stabilize and smooth production requirements but also to ensure material that is
produced will be consumed (synchronization) as quickly as possible. Constraint-
anchored planning generated the highest FMEA RPN score of 60.
The hypotheses for testing the expected impact of constraint-anchored planning
was stated as: Ho: µ1 = µ2 versus Ha: µ1 > µ2; where µ1 = average semi-finished inventory
production value with unconstrained demand and unconstrained supply and µ2 = average
semi-finished inventory production value with unconstrained demand and constrained
supply.
To test these hypotheses a manufacturing planning and scheduling simulation
environment was created. The hardware and software infrastructure at the site studied
allowed for the running of several planning models simultaneously. The live production
model was updated and run on a daily basis using current input data. Simulation
production models could be run on command as long as certain required input data was
provided. All of the necessary input data was copied from the live model to the
simulation model prior to conducting the experiment. The hypothesis results were
reported in terms of the average projected inventory production and included fourteen
data points. Both the live and simulation model output data was written to a database and
accessed using Microsoft® Excel and Query.
The second major improvement action was to define the impact of planning
parameters on the level of semi-finished inventory for the gateway resources and create
tools that allow for analysis by supply chain. The key process inputs associated with this
73
action item scored a FMEA RPN of 48. The basis for this corrective action were as
follows:
1. Parameters are currently evaluated from an SKU view for a given production
resource. Decisions relating to lot size, buffers, cycle frequency, etc. tend to be made
in isolation without regard for the interdependency of all of the SKU’s produced on
the resource and the interaction with downstream resources.
2. The use of planning parameters in the Material Planning Process can create
dynamic and complicated planning results. Process performance issues may result
when different combinations of parameters are used together. Planning parameter
examples include: production frequency, minimums, multiples, lead times, time
buffers, quantity buffers, etc.
3. Optimal Order Quantity logic must be tested and incorporated into the model to
understand the cash versus cost tradeoff.
The hypotheses for testing the expected impact of planning parameters for this
experiment was stated as: H0: µ1 = µ2 versus Ha: µ1 > µ2; where µ1 = average semi-
finished inventory with current parameters and current demand forecast, and µ2 = average
semi-finished inventory with optimized parameters and current demand forecast.
The test data was derived from a Material Requirements Planning simulation
model created using Microsoft® Excel and Microsoft® Query to calculate average
inventory for 12 weekly data points. The simulation model was a collection of databases
that captured current demand for each product and then propagated the demand
requirements through the product bill-of-material. The model provided a mechanism for
74
the project team to understand the impact on inventory throughout a supply chain for a
given product structure for the current planning parameters as well as new or adjusted
parameters (variables). The impact on inventory was measured in both days-of-stock and
inventory dollars (fully-burdened unit cost at each level of production.) Figures 2-22 and
2-23 represent examples of the output results for a particular supply chain using the
simulation model:
Figure 2-22. Parameter Simulation Model Example (Ha) – Inputs
Supply Chain Parameter Planning Model
Business Model: Product Group 1
Model Notes: > The user enters information in cells highlighted in Yellow. > Using a Corporate Carrying cost factor of 11.5%. > Avg. Daily Demand includes Forecast only
Avg. Daily Demand 1,018
Inventory Carrying Cost
Factor1.115
Current Parameters Proposed ParametersBOM Level
Work Center Stock No.
Time Buffer (Days)
Production Cycle (Days)
Consolidation Factor Minimum Multiple Inventory
Time Buffer (Days)
Production Cycle (Days) Minimum Multiple
Consolidation Factor
MPS ST FG1 15.42 12.89 12.89 17,600 320 6,335 15 11 17,600 320 10
1 DS-1 INPUT-0 0 0 0 0 0 231,200 0 0 0 0 0
2 0 0 0 0 0 0 0 0 0 0 0
2 0 0 0 0 0 0 0 0 0 0 0
2 DS1 INPUT-1 7 7 7 1,700 1,700 53,550 7 7 1,700 1,700 7
3 0 0 0 0 0 0 0 0 0 0 0
3 0 0 0 0 0 0 0 0 0 0 0
4 0 0 0 0 0 0 0 0 0 0 0
3 0 0 0 0 0 0 0 0 0 0 0
4 0 0 0 0 0 0 0 0 0 0 0
3 GW2 INPUT-1-1 0 14 7 2,000 2,000 32,680 0 14 2,000 2,000 7
4 US-1 INPUT-1-2 0 0 0 1,000 1,000 37,400 0 7 1,000 1,000 0
5 GW1 INPUT-1-3 0 0 0 2,000 2,000 133,450 0 7 2,000 2,000 0
75
Figure 2-23. Parameter Simulation Output Example (Ha)
Testing for the expected impact of planning parameters on inventory performance
was accomplished using the Material Requirements Planning simulation. The planning
parameters tested included run frequency, production minimum order quantity, time and
quantity buffers, and production consolidation days. Parameters were studied in isolation
and in combination. Supply chains that included high dollar value and high usage
materials from the gateway resources were modeled to test this hypothesis. The delta
(change) between current inventory levels using current planning parameters and
Inventory Analysis
$0
$100,000
$200,000
$300,000
$400,000
$500,000
$600,000
$700,000
$800,000
$900,000
$1,000,000
Week 1 Week 2 Week 3 Week 4 Week 5 Week 6 Week 7 Week 8 Week 9 Week 10 Week 11 Week 1239
40
41
42
43
44
45
46
Projected Inventory Profile Current Inventory Profile Current Avg. DOS Projected Avg. DOS
Current Avg. Days-of-Stock
Projected Avg. Days-of-Stock
76
projected inventory levels with new planning parameters was recorded and compared.
These simulation model results demonstrated how different planning parameters interact
in the manufacturing requirements planning model.
The results of this experiment provided some interesting insight. The impact of
cycle frequency on the average inventory level proved to be the most critical parameter
regardless of the supply chain chosen and the combination of other parameters modeled.
Longer average lead times resulted in higher average inventory levels (primarily in
working inventory). Higher cycle frequency variability equated to higher safety stock
inventory.
A two-sample t-test for unpaired data was used to verify the constraint-anchored
planning alternative hypothesis. The general formulas for computing a test statistic for
making an inference about a difference between two populations is:
Test Statistic: T = /N22
2s/N121s
2X1X
+
−
where N1 and N2 are the sample sizes, X1 and X2 are the sample means, and 21s and
22s are the sample variances
If equal variances are assumed, this test statistic reduces to:
Test Statistic: T = 21p
21
1/N1/NsXX+
−
where 2N2N1
221)s(N22
11)s(N12ps
−+
−+−=
77
The null hypothesis where the two means are equal (u1 = u2) will be rejected if
T < - ν)t(α(α/
or
T > + ν)t(α(α/
where ),2/( ναt is the critical value of the t distribution with ν degrees of freedom
where 1)/(N22/N2)2
2(s1)/(N12/N1)21(s
2/N2)22s/N12
1(sν
−+−
+=
If the equal variances are assumed, then 2N2N1ν −+=
The equation H0: s1 = s2 versus Ha: s1 > s2 (where s1 equals the variance of the
unconstrained supply and s2 equals the variance of the constrained supply) was used to
represent the hypothesis that the variances are unequal for an unconstrained versus a
constrained supply plan. These test results lead to the rejection of the null of hypothesis
that the variances are equal. The tests for equal variance results using MINITAB™ are
represented in Figure 2-24.
78
Figure 2-24. Test for Equal Variances
Based upon the results of the test for equal variance, the test statistics in Figure 2-
25 were calculated using MINITAB™ for the constraint-anchored planning alternative
hypothesis of Ha: µ1 > µ2 assuming unequal variances.
15000010000050000
95% Confidence Intervals for Sigmas
U1 STDEV
U2 STDEV
700000600000500000400000300000200000
Boxplots of Raw Data
P-Value : 0.318Test Statis tic: 1.033
Levene's Tes t
P-Value : 0.294Test Statistic: 1.777
F-Test
Factor Levels
U2 STDEV
U1 STDEV
15000010000050000
95% Confidence Intervals for Sigmas
U1 STDEV
U2 STDEV
700000600000500000400000300000200000
Boxplots of Raw Data
P-Value : 0.318Test Statis tic: 1.033
Levene's Tes t
P-Value : 0.294Test Statistic: 1.777
F-Test
Factor Levels
U2 STDEV
U1 STDEV
79
Two-Sample T-Test and CI: U1 Actual Values versus U2 Actual Values
N Mean StDev SE Mean
U1 Actual 15 1834379 238053 61465
U2 Actual 15 1347515 163267 42155
Difference = mu U1 Actual Values - mu U2 Actual Values
Estimate for difference: 486864
95% CI for difference: (333037, 640690)
T-Test of difference = 0 (vs not =): T-Value = 6.53; P-Value = 0.000; DF = 24
Figure 2-25. Constraint-Anchored Planning (Ha) Test Results
The Two-sample t-test resulted in a T-Value of 6.53. When compared with a
critical t-distribution value of 1.711 (95% confidence interval and with 24 degrees of
freedom), the null hypothesis was rejected. The effect of constraint-anchored planning on
the level of planned production is statistically significant.
The designed experiments approach was very helpful in focusing simulation
efforts on the effects from manipulating input variables to observe responses to the output
variables. As was demonstrated by this business case, use of every tool available to
execute a designed experiment is not necessary.
80
2.6 Process Improvement
The purpose of the Improve phase is to develop, implement, and evaluate
solutions targeted at the verified cause. The goal is to demonstrate, with data, the
solutions solve the problem and lead to improvement.
Prior to implementing changes to the process, the project team created a
Stakeholder Analysis matrix to identify and understand potential resistance to the project
solutions. Stakeholders for this project included the plant manufacturing management
team, gateway supervisors and supply chain analysts, upstream and downstream work
center managers and supply chain analysts, and gateway equipment operators. Regular
and frequent communication with those affected by the process change can create more
buy-in, identify better solutions, and avoid pitfalls.
Figure 2-26 is an excerpt from the Stakeholder Analysis completed for this
project.
81
Figure 2-26. Stakeholder Analysis Excerpt
The first strategy for reducing material requirements planning variability was to
develop a constraint-anchored planning process. The constraint-anchored planning
strategy centered on creating supply plans based upon the availability of capacity and
input materials for gateway resource manufactured products. Demand requirements that
could not be supplied by the requested due date because of capacity and/or material
constraints would be rescheduled to supply dates based upon when material or capacity
was made available. Supply plans would not be created for demand requirements that
could not be done.
Influence, Strategy, Tactics
Stakeholders Positive or Supportive Items Issues or ConcernsTo achieve or maintain needed
level of support
Gateway Supply Chain Analyst
C,N
more control over planning; more stable environment; more
effective & efficient communications; better management of RM's
less flexibility on what to run; requires more discipline; must have business rules (priorities)
pre-established
Communicate benefits & discuss likely concerns & issues; include
analyst in development of business rules governing process
Downstream and Upstream Supply Chain Analysts
C N
should stabilize downstream planning; more accurate mat'l availability dates; will improve
inventory levels; better communications, should reduce
supply variabilty
reduced flexibility in the event of a crisis (high sales, quality
problems, etc.) could experience long term b/o on some items;
requires more discipline
Communicate benefits & discuss likely concerns & issues; include
analyst in development of business rules governing gateway & MPS scheduling process; provide help
with managing FF manager concerns
Gateway Product Manager
C,Npotential reduction in operating expense & waste; more stable
& predictable environment
loss of flexibility due to scheduling business rules; heat
from other FF managers and Marketers
Communicate details of process and implications of new discipline;
Review and gain input on control plan: how will business rules be used & enforced, etc.; contingency plan;
impact on operators; new requirements on operations;
N = Needed LevelC = Current Level
Level of Support Comments re: Level of Support Influence, Strategy, Tactics
Stakeholders Positive or Supportive Items Issues or ConcernsTo achieve or maintain needed
level of support
Gateway Supply Chain Analyst
C,N
more control over planning; more stable environment; more
effective & efficient communications; better management of RM's
less flexibility on what to run; requires more discipline; must have business rules (priorities)
pre-established
Communicate benefits & discuss likely concerns & issues; include
analyst in development of business rules governing process
Downstream and Upstream Supply Chain Analysts
C N
should stabilize downstream planning; more accurate mat'l availability dates; will improve
inventory levels; better communications, should reduce
supply variabilty
reduced flexibility in the event of a crisis (high sales, quality
problems, etc.) could experience long term b/o on some items;
requires more discipline
Communicate benefits & discuss likely concerns & issues; include
analyst in development of business rules governing gateway & MPS scheduling process; provide help
with managing FF manager concerns
Gateway Product Manager
C,Npotential reduction in operating expense & waste; more stable
& predictable environment
loss of flexibility due to scheduling business rules; heat
from other FF managers and Marketers
Communicate details of process and implications of new discipline;
Review and gain input on control plan: how will business rules be used & enforced, etc.; contingency plan;
impact on operators; new requirements on operations;
N = Needed LevelC = Current Level
Level of Support Comments re: Level of Support Influence, Strategy, Tactics
Stakeholders Positive or Supportive Items Issues or ConcernsTo achieve or maintain needed
level of support
Gateway Supply Chain Analyst
C,N
more control over planning; more stable environment; more
effective & efficient communications; better management of RM's
less flexibility on what to run; requires more discipline; must have business rules (priorities)
pre-established
Communicate benefits & discuss likely concerns & issues; include
analyst in development of business rules governing process
Downstream and Upstream Supply Chain Analysts
C N
should stabilize downstream planning; more accurate mat'l availability dates; will improve
inventory levels; better communications, should reduce
supply variabilty
reduced flexibility in the event of a crisis (high sales, quality
problems, etc.) could experience long term b/o on some items;
requires more discipline
Communicate benefits & discuss likely concerns & issues; include
analyst in development of business rules governing gateway & MPS scheduling process; provide help
with managing FF manager concerns
Gateway Product Manager
C,Npotential reduction in operating expense & waste; more stable
& predictable environment
loss of flexibility due to scheduling business rules; heat
from other FF managers and Marketers
Communicate details of process and implications of new discipline;
Review and gain input on control plan: how will business rules be used & enforced, etc.; contingency plan;
impact on operators; new requirements on operations;
N = Needed LevelC = Current Level
Level of Support Comments re: Level of Support Influence, Strategy, Tactics
Stakeholders Positive or Supportive Items Issues or ConcernsTo achieve or maintain needed
level of support
Gateway Supply Chain Analyst
C,N
more control over planning; more stable environment; more
effective & efficient communications; better management of RM's
less flexibility on what to run; requires more discipline; must have business rules (priorities)
pre-established
Communicate benefits & discuss likely concerns & issues; include
analyst in development of business rules governing process
Downstream and Upstream Supply Chain Analysts
C N
should stabilize downstream planning; more accurate mat'l availability dates; will improve
inventory levels; better communications, should reduce
supply variabilty
reduced flexibility in the event of a crisis (high sales, quality
problems, etc.) could experience long term b/o on some items;
requires more discipline
Communicate benefits & discuss likely concerns & issues; include
analyst in development of business rules governing gateway & MPS scheduling process; provide help
with managing FF manager concerns
Gateway Product Manager
C,Npotential reduction in operating expense & waste; more stable
& predictable environment
loss of flexibility due to scheduling business rules; heat
from other FF managers and Marketers
Communicate details of process and implications of new discipline;
Review and gain input on control plan: how will business rules be used & enforced, etc.; contingency plan;
impact on operators; new requirements on operations;
N = Needed LevelC = Current Level
Level of Support Comments re: Level of Support
82
As an outcome to creating realistic supply plans at the gateway resources,
subordination of downstream resources to the capability of the gateway resources would
be realized. Constrained supply plans from the gateway resources dictate the capability
of downstream work centers to supply as well. Supply plans would not be created for
demand requirements that could not be done for downstream work centers.
The team recognized the implementation of constraint-anchored planning could
result in a reduction in schedule change flexibility in favor of resource utilization,
reduced supply variability, and reduced inventory. The reduction in schedule change
flexibility was identified as a potential concern in the Stakeholder Analysis. Schedule
change guidelines were created to improve the decision making process around schedule
interruptions. These guidelines were deemed necessary to increase the understanding of
the importance of maintaining the group technology schedule integrity. Each group
technology family produced on a gateway resource has an interdependent relationship
with each other. A schedule change can cause delay and disruption for subsequent group
technology production runs.
The schedule change guidelines defined the response plan for the escalation of
schedule change events based upon the severity of the changeover (in hours) to the
resource and the anticipated affect of the unplanned changeover on other products.
The schedule change guidelines were presented to the stakeholders for their input.
Consensus approval from stakeholders of the schedule change guidelines required several
hours of discussion. As predicted in the stakeholder analysis, the most debated schedule
change matrix concern was the potential for reduced flexibility. Prior to the schedule
83
change matrix, schedule change decisions were based solely on urgency without regard
for the affect on other products, resource optimization, waste, or inventory ramifications.
Schedule changes resulting from servicing one product often resulted in service issues for
other products due to the delay created by the unplanned changeover. The stakeholders
were eventually convinced the changeover guidelines would be beneficial in quantifying
the positives and negatives of significant unplanned changeovers and engender more
communication. The schedule change guidelines are presented in Figure 2-27.
Figure 2-27. Schedule Change Guidelines
Brookings Coater Schedule Change Guidelines
Analyst/Coating Supervisor Coating FF Manager Plant Manager Sourcing Director or
Supply Chain Manager
No breaking into families with requirements requiring HARD changeovers Consult
change will push next scheduled item out by
12hours or less
change will push next scheduled item out by
between 12 to 24 hours
change will push next scheduled item out by more than 24hours
Unplanned MX/PPE’s must have approval of FF Manager; must be run in family group Consult
All unplanned PPE/MX require Coating FF Manager consent
escalate as necessary escalate as necessary
MX/PPE must be completed in time allotted minimal impact on completion of schedule
extended run will push schedule out by 4hours
or less
extended run will push schedule out by more than 4
but less than 8 hours
extended run will push schedule out by > 8
hours
Product runtimes that exceed 10% of standard time allotted will be aborted until process
problem rectified.Consult
extended run will push schedule out by 4hours
or less
extended run will push schedule out by more than 4
but less than 8 hours
extended run will push schedule out by > 8
hours
Cannot insert runs out of family sequence minimal impact on completion of schedule
change will push next scheduled item out by
12hours or less
change will push next scheduled item out by 12 to
24hours
change will push next scheduled item out by more than 24hours
All items must be run in specified sequence as set forth in production schedule (where
material is available).
minimal impact on completion of schedule
All other changes require Coating FF Manager consent
escalate as necessary escalate as necessary
Quantities must be completed to within +/- 10 % unless prior approval given
run out materials; minimal impact on
completion of schedule
All other changes require Coating FF Manager consent
escalate as necessary escalate as necessary
Preventive maintenance must be completed when scheduled, within time allotted
minimal impact on completion of schedule
PM extension will push schedule out by 4 hours
or less
PM extension will push schedule out by more than 4
less than 8hours escalate as necessary
All schedule delays will be approved. minimal impact on completion of schedule
All other changes require Coating FF Manager consent
escalate as necessary escalate as necessary
Special Cause Circumstances As required As required As required As required
Approval Level to Bypass Guideline
Schedule Change GuidelinesBrookings Coater Schedule Change Guidelines
Analyst/Coating Supervisor Coating FF Manager Plant Manager Sourcing Director or
Supply Chain Manager
No breaking into families with requirements requiring HARD changeovers Consult
change will push next scheduled item out by
12hours or less
change will push next scheduled item out by
between 12 to 24 hours
change will push next scheduled item out by more than 24hours
Unplanned MX/PPE’s must have approval of FF Manager; must be run in family group Consult
All unplanned PPE/MX require Coating FF Manager consent
escalate as necessary escalate as necessary
MX/PPE must be completed in time allotted minimal impact on completion of schedule
extended run will push schedule out by 4hours
or less
extended run will push schedule out by more than 4
but less than 8 hours
extended run will push schedule out by > 8
hours
Product runtimes that exceed 10% of standard time allotted will be aborted until process
problem rectified.Consult
extended run will push schedule out by 4hours
or less
extended run will push schedule out by more than 4
but less than 8 hours
extended run will push schedule out by > 8
hours
Cannot insert runs out of family sequence minimal impact on completion of schedule
change will push next scheduled item out by
12hours or less
change will push next scheduled item out by 12 to
24hours
change will push next scheduled item out by more than 24hours
All items must be run in specified sequence as set forth in production schedule (where
material is available).
minimal impact on completion of schedule
All other changes require Coating FF Manager consent
escalate as necessary escalate as necessary
Quantities must be completed to within +/- 10 % unless prior approval given
run out materials; minimal impact on
completion of schedule
All other changes require Coating FF Manager consent
escalate as necessary escalate as necessary
Preventive maintenance must be completed when scheduled, within time allotted
minimal impact on completion of schedule
PM extension will push schedule out by 4 hours
or less
PM extension will push schedule out by more than 4
less than 8hours escalate as necessary
All schedule delays will be approved. minimal impact on completion of schedule
All other changes require Coating FF Manager consent
escalate as necessary escalate as necessary
Special Cause Circumstances As required As required As required As required
Approval Level to Bypass Guideline
Schedule Change Guidelines
84
Implementation of the constraint-anchored firm planning strategy entailed both a
planning system and process change. The process change was dependent on the success
of implementing system changes in support of constraint-anchored planning. The effort
spent defining the process change led to the identification of the necessary planning
system changes.
The network of systems and applications for this business has become very
complex over time. The planning system infrastructure is made up of many specialized
applications – some located and supported on-site and some located and supported at
corporate headquarters. Although each application serves a unique purpose, they are all
interconnected and provide pieces of information critical to the planning process.
At the very center of this information interchange is the manufacturing planning
and scheduling system. Although this system receives critical input information from
several supporting applications, it serves as the material planning and scheduling
calculation brain. The capability and performance of this system will have a significant
influence on the success of changes proposed in this business case.
A feasibility study was completed to assess whether the solution is achievable
given the organization’s resources and constraints. With the assistance of an Information
Technology representative, our team evaluated three major areas of feasibility:
1) Technical Feasibility: whether the proposed solution can be implemented
with the available hardware, software, and technical resources.
2) Economic Feasibility: whether the benefits of the proposed solution
outweigh the costs.
85
3) Operational Feasibility: whether the proposed solution is desirable within
the existing managerial and organizational framework.
The feasibility evaluation merges the project solution and its system support
requirements with the available hardware, software, and technical resources. Table 2-1
was developed as a summary of the estimated cost associated for each programming
change.
Table 2-1. Information Technology Feasibility Matrix
86
The results of the analysis indicated the proposed solution could be implemented
with current hardware, software, and technical resources. Changes to current scripts,
routines, and database file structures would need to be made to implement the solution.
The programming and process difficulty both average 3.75 on a scale of 1 to 5 (with 1
being the easiest and 5 being the most difficult) for those features not currently used but
needed. The cost to implement is an estimated $7,800. The total cost to implement
includes only the cost of software programming changes. Additional hardware and/or
hardware changes were determined to be unnecessary. Since the cost of implementation
can be viewed as fixed costs, the only potential cost is “lost opportunity cost” resulting
from resources being committed to this project versus another project or projects. The
results of the feasibility study indicated the benefits of the proposed solution (an
estimated $500,000 inventory reduction) far outweigh the costs and the project is deemed
economically feasible.
Operational feasibility is much easier to justify. Since the recommendations for
system changes were the result of a Six Sigma project, corporate and plant management
signed off and approved the project and project solution.
Complementing the constraint-anchored planning process was the development of
gateway changeover sequence plans, expansion of the schedule attainment measure, and
the development of scheduling change guidelines. The implementation of a changeover
sequence strategy had the potential to decrease costs, increase inventory turns, and
improve machine and labor productivity through improvements in efficiency and
predictability for supply replenishment.
87
To arrive at a sequence plan, we involved the machine operators, process and
equipment engineers, and the primary supply chain analyst. In preparation for this
meeting, several pieces of information were obtained for each gateway resource using
Microsoft® Query and Excel. Critical SKU information for each gateway resource
included: annual production quantities, annual production hours, annual usage, and bills-
of-material.
As discussions progressed, it became clear the critical scheduling influence for
differentiating sequencing families related to a common raw material input. Grouping by
this common input would reduce changeover time through a reduction in time for cleanup
between runs as well as reducing the number of material moves. Sequencing rules
between raw material family’s could also be improved - as changing from one raw
material to another could reduce equipment tear down and set-up time.
Sequencing rules within each group technology family are dictated more by run
frequency than any other criteria. For example, some products within a group technology
family may have sufficient demand volume to warrant a weekly or bi-weekly production
cycle while others may be produced monthly or as needed to order.
Following the identification of families and sequencing, the next effort focused on
computing an optimal production quantity (OPQ) range for each SKU on each gateway
resource. The approach to arriving at this range utilized a combination of the Delphi
Technique (using the group of experts to help “predict” the future of changeover
improvements) and the traditional OPQ formula. The premise for the OPQ approach is to
contain the inventory cost versus ordering cost balance within a known operating range –
88
allowing for some degree of quantity freedom and to reduce supply quantity variability
and family cycle frequency variability.
Figure 2-28 illustrates a simplified example of the theory behind the OPQ range.
Figure 2-28. Optimal Order Quantity Model
The OPQ model was constructed using Microsoft® Query to extract the OPQ
input data and Microsoft® Excel to calculate and the display the OPQ results for any
given SKU. The model was robust enough to allow the user to calculate the cost versus
cash implications for a non-OPQ simulation as well. An example of this model is
provided in Figure 2-29.
Order QuantityOrder Quantity
Annual CostAnnual Cost
Holding Cost Curve
Holding Cost CurveTotal Cost Curve
Total Cost Curve
Order (Setup) Cost CurveOrder (Setup) Cost Curve
Optimal Optimal
Order QuantityOrder Quantity
MinimumOrder Qty
MaximumOrder Qty
Order QuantityOrder Quantity
Annual CostAnnual Cost
Holding Cost Curve
Holding Cost CurveTotal Cost Curve
Total Cost Curve
Order (Setup) Cost CurveOrder (Setup) Cost Curve
Optimal Optimal
Order QuantityOrder Quantity
MinimumOrder Qty
MaximumOrder Qty
89
Figure 2-29. Optimal Production Quantity Simulation Model
The final activity of the exercise consisted of developing the changeover sequence
plan. This plan was constructed as follows:
1. Divide minimum order quantity (from the OPQ model) by the historical
average production rate for each SKU to arrive at the production hours.
2. Total the production hours for each family.
3. Define SKU production frequency within each adhesive family.
90
4. Define family sequencing schedule.
5. Transfer hours by family to calendar grid. (Ease of understanding and
communication.)
An example of the sequencing calendar grid is provided in Figure 2-30.
Figure 2-30. Group Technology Scheduling Plan
The schedule attainment measure was expanded from attainment in hours to
include attainment by item. Item schedule attainment was viewed as a critical feedback
measure for both schedule execution and supply plan attainment. The schedule
attainment in hours measure provides a view of coverage at a machine and operator level.
Week 1
Week 2
Week 3
Week 4
Wednesday Thursday FridaySaturday Sunday Monday Tuesday
Group 10: 48 Hrs
= Capacity Bank
Group 7: 28 Hrs
Group 10: 48 hrs Group 4: 38 Hrs
Group 3 - 36 hrs Group 7: 36 Hrs 9
Group 8: 27 Hrs
Group 12: 80 hrs
Group 9: 11 Hrs
Group 6: 76 Hrs 8
Group 14: 45 Hrs Group 13: 15 Hrs
Group 13: 25 Hrs Group 5: 48 Hrs
Group 11: 41 Hrs Group 1: 28 Hrs Group 15: 32 Hrs
9
Week 1
Week 2
Week 3
Week 4
Wednesday Thursday FridaySaturday Sunday Monday Tuesday
Group 10: 48 Hrs
= Capacity Bank
Group 7: 28 Hrs
Group 10: 48 hrs Group 4: 38 Hrs
Group 3 - 36 hrs Group 7: 36 Hrs 9
Group 8: 27 Hrs
Group 12: 80 hrs
Group 9: 11 Hrs
Group 6: 76 Hrs 8
Group 14: 45 Hrs Group 13: 15 Hrs
Group 13: 25 Hrs Group 5: 48 Hrs
Group 11: 41 Hrs Group 1: 28 Hrs Group 15: 32 Hrs
9
91
Item schedule attainment provides an organizational performance measure because many
functional areas contribute to the results.
The item schedule attainment application was designed to provide some
automation to the schedule change log previously maintained manually in a spreadsheet.
Like the schedule change log, the item schedule attainment application would also
provide the capability for causal analysis on schedule change reasons.
The final improvement recommendation comes as a result of the parameter
review and should prove to be a complement to both firm planning and group technology
scheduling. A buffer management program was developed to help manage the variability
in demand and supply at the gateway resources. During the analysis of the effects of
parameter changes on a supply chain, the number of time and quantity buffers found to be
in place was alarming. Supply Chain Analysts were responsible for implementing and
managing buffers. An informal survey found buffers were more often put in place “just-
in-case” versus for a strategic purpose and were not based on demand or supply
variability and did not correlate directly to a desired level of service protection.
To improve the area of buffer management an application was created to calculate
and manage a buffer through exception-based reporting. For this application, the
corporation had developed a Microsoft® Excel-based safety stock calculator. This
application required the following information in order to calculate a safety stock
quantity: service protection level, average cycle frequency (lead time), average demand
over lead time, standard deviation of demand or forecast error, and standard deviation of
cycle frequency. Additional databases were linked to the safety stock model that assisted
92
with analysis of demand and supply variability, historical inventory balance monitoring,
and exception-based buffer performance feedback. Implementation of the Buffer
Management application entailed validation of the data, providing users access to the data
(database access), and user training.
The data made available in the buffer management application allowed analysts to
quantify the differences between current buffers and buffers calculated based upon
demand variability, supply variability, and the desired level of service. The application
was programmed to automatically create individuals charts for demand and supply
information for the user-defined stock number and date range. The average demand
quantity, the standard deviation of demand, the average supply lead-time, and the
standard deviation of supply lead-time were used as inputs for calculating buffers and the
target inventory level. The target inventory level was defined as being equal to safety
stock plus half of the average demand during average supply lead-time. Figures 2-31, 2-
32, and 2-33 represent simulations of the screen views offered by the buffer management
application:
93
Figure 2-31. Buffer Management Inventory Monitor
Figure 2-32. Buffer Management Cycle Frequency Individuals Chart
Avg. Lead Time Service Level
SKU A 13 0%
Start Date End Date Work Center
Rept Unit
UCL-Max Inv. Avg. Inv. Avg. Inv. $ Target Inv. Target Inv. $
Inventory Reduction Entitlement
Inv. LCL (Sfty Stk)
SS Inv. $
No. Days below Safety Stock
5/1/02 12/31/2002 Gateway 2 LNYD 310,332 163,887 228,278$ 185,007 $257,696 -$29,418 0 $0 14
Inventory Balance Monitor
0
50,000
100,000
150,000
200,000
250,000
300,000
350,000
400,000
450,000
R QTY Avg. Inv. UCL-Max Inv. Target Inv. Inv. LCL (Sfty Stk)
SKU A
Start Date End Date Work Center Data Points L/T - Avg. Days L/T Std.Dev. L/T Coeff.
of Var.
5/1/2002 12/31/2002 Gateway 2 17 13 4 0.34
Cycle Frequency Individuals Chart
0
5
10
15
20
25
30
Act. Lead Times Avg. Lead Time UCL LCL
Note: Avg. Lead Time may not be centered. Statistically calculated LCL is not allowed to go below zero.
94
Figure 2-33: Buffer Management Demand Individuals Chart
The data presented in the buffer management application proved useful in more
ways than as a tool to calculate safety stock quantity. The demand individuals chart was
useful in analyzing the historical profile of demand patterns for a stock-keeping unit.
This demand information was valuable for analyzing customer demand patterns and in
seeing the impact of planning and scheduling methods on upstream work centers.
The supply cycle time individuals chart was useful to analysts in understanding
the frequency in which a product was manufactured and the relationship of this frequency
to the demand profile. Situations were discovered where the cycle frequency did not
SKU A
Start Date End Date Work Center Data Points Avg. Wkly
Usage QtySTDEV
Usage Qty Coeff.of Var.
5/1/2002 12/31/2002 Gateway 2 1873 167,102 11,746 0.39
Demand Individuals Chart
0
10,000
20,000
30,000
40,000
50,000
60,000
70,000
80,000
90,000
Usage Qty Avg. Usage Qty UCL LCL
Note: Avg. Demand may not be centered. Statistically calculated LCL is not allowed to go below zero.
95
match a demand profile with very little variation – even though the resource responsible
for supply did not have cost constraints preventing the reduction in lot size or lead-time.
The inventory monitor was useful in assessing the performance of the planning
and scheduling process relative to managing inventory to target, identifying when and
how often safety stock was penetrated, and indicating the frequency with which
maximum inventory levels were exceeded.
The final step in the process improvement phase was to complete pilot testing to
validate the system infrastructure changes, understand the effects of the changeover
sequence plan on downstream work centers and inventory consumption, and to validate
the planning and scheduling process.
This exercise was accomplished by using a test model that included the live
system supply chain tables, scripts, and manufacturing-planning model. The test model
was capable of being updated with the same daily demand information, bill-of-material
data, work center routing and rate data, and production schedule data as the live system.
Once the test model has been updated with the test plan, the manufacturing model data
can be saved and accessed for comparative analysis. This pilot test system proved
invaluable in comparing the model results using several combinations of planning system
features and planning and scheduling techniques.
Once the new system configuration had been confirmed and validated, the test
system was made available (through Windows NT Client) to the gateway supply chain
analyst. The test system provided an environment for the analyst to learn and understand
the new planning process and system requirements. The analyst was also able to provide
96
feedback on the use and functionality of the process, the complexity, and whether the
time required for utilizing the new process was reasonable and manageable.
At this phase of using the Six Sigma DMAIC roadmap, there is very little insight
that can be given to discern the applicability of Six Sigma tools to the process
improvement effort. All of the work completed prior to this phase would either lead to
the improvement or they would not. There is not an infallible method to validate the
solutions will be correct except to “run and record.” Like any other process improvement
effort, if the recommended solutions do not fit together or are not bought into by process
owners, they will not be successful. Like the Deming PDCA model, Six Sigma
methodology suggests returning to the FMEA if improvements do not deliver the results
expected.
Typically, after the measurements supporting the improvement phase have been
developed and are in place, a project leader will present the project results to the process
owners, process champion, and black belt sponsor in what is termed a pre-close. The
final project presentation is termed the close.
The time between the pre-close and the close is typically spent observing the
process using the measurement control systems. This “run and record” time was
beneficial for this business case because it allowed for additional time for proving the
concept, for ironing out any programming issues for obtaining data, and for providing
additional training time for process owners prior to the transfer of project control.
97
2.7 Process Control
The purpose of the Control phase is to develop, implement, and evaluate solutions
targeted at the verified cause or causes. The goal is to demonstrate that the solutions
solve the problem, lead to improvement, and reduce or eliminate special causes.
Key activities to be taken in managing the process improvement solutions
include: implementing ongoing measures and actions to sustain improvement; defining
responsibility for process ownership and management; and, executing closed-loop
monitoring. The ongoing measures that are put in place to manage the process should be
meaningful and measurable. The measurement should help track process performance
and assist leading the process owners towards making better decisions.
Documentation should accompany the measurement systems. This
documentation is necessary for several important reasons. The documentation spells out
where the data comes from, how the measurement systems are updated, how and when to
respond to emergencies, and serves as a means to update and track measurement system
revisions.
The Six Sigma Control Phase is really not a unique concept. The concepts are
very similar to the “Act” step in the Deming’s PDCA continuous improvement model.
The recommended tools and the processes for evaluating results are very similar.
Whichever process improvement technique is used to describe this phase of process
improvement, many of the concepts are transferable to both operational and transactional
processes.
98
2.7.1 Project Controls
The project controls for this business case can be best demonstrated using the
classic inventory replenishment diagram. Advantages to using this classic diagram to
illustrate the project control plan included: familiarity to process owners (in this case the
Materials Manager and Supply Chain Analysts); it is simple and easy to comprehend; it
condenses a complex process down to understandable pieces; and it demonstrates the
interdependence of key input variables on process performance. As Figure 2-34 depicts,
the control plan for this business case encompasses three critical measurement areas:
Supply Plan & Schedule Attainment, Lead Time/Cycle Time Management, and Buffer
Management.
Figure 2-34. Semi-Finished Inventory Control Plan
Inve
ntor
y Le
vel
Time
Safety Stock
Supp
ly
Dem
and
Supply Plan & Schedule Attainment
Buffer Management
Cycle Frequency
Lead Time/Cycle Time Management
Schedule Change Guidelines
Inve
ntor
y Le
vel
Time
Safety Stock
Supp
ly
Dem
and
Supply Plan & Schedule Attainment
Buffer Management
Cycle Frequency
Lead Time/Cycle Time Management
Schedule Change Guidelines
99
A Control Plan Matrix was developed to assist the project team as well as process
owners with understanding the relationship of each control measure, enabler, and
countermeasure. The matrix was a convenient communication tool as it provided one
information location that summarized the controls necessary to manage the process and
provided a connection back to the process map. Figures 2-35, 2-36, and 2-37 illustrate
the format used for this business case.
Figure 2-35. Primary Control Plan Measures
1 2 3Measurement Supply Plan Attainment Inventory Performance Item Schedule Attainment
Process Material Requirements Planning Production Schedule Execution Production Schedule Execution
Input Net Unconstrained Demand Production Schedule Production ScheduleOutput Firm Constrained Supply Plan Inventory Inventory
Measurement Description
Measures actual supply qty versus demand qty using the
demand due date.Actual inventory levels over time
Measures actual supply qty versus scheduled quantity. Date range is the scheduling week (beginning Monday and ending Sunday).
Measurement Frequency Weekly Weekly Weekly
Data Granularity Weekly; Square Yards Weekly; Square Yards Weekly; Square Yards
LSLLesser of the Demand Plan (-10%) and Constraint-anchored
Plan (-10%)Safety Stock 90%
Target Greater of the Demand Plan and Constraint-anchored Plan
Avg. Demand over Lead Time divided by two plus safety stock 95%
USL Target +10%Sum of Maximum quantity for products manufactured during
measurement period.100%
Reaction Plan
Assign a cause code whenever a data point violates the LSL or
USL. Review causal information weekly. Develop corrective actions for largest
problem areas.
Respond when 1data point violates the LSL or USL.
Assign a cause code whenever a data point violates the LSL or USL. Review causal information weekly.
Develop corrective actions for largest problem areas.
Proc
ess
Ma p
100
Figure 2-36. Control Plan Measurement Enablers
Figure 2-37. Counterbalance Control Plan Measures
1 2Enabler Demand Variability Supply Variability
Process Material Requirements Planning Material Requirements Planning
Input Actual Demand Actual ProductionOutput Demand variability Supply variability
Measurement Description
Measures the general performance of demand
variabilityControl Chart
Measurement Frequency
As needed (mandatory review for safety
stock analysis quarterly)
As needed (mandatory review for safety stock
analysis quarterly)
Data Granularity Weekly; Square Yards Per Changeover; Days between changeovers
LCLThe greater of zero or the statistically calculated LCL
using the I-MR chart
The greater of zero or the statistically calculated LCL using
the I-MR chartTarget Statistically calculated Mean Statistically calculated Mean
UCL Calculated using the I-MR chart Calculated using the I-MR chart
Reaction Plan Investigate when cause flag appears.
Investigate when cause flag appears.
Proc
ess
Map
1 2 3Measurement Customer Service Overall Equipment Effectiveness Capacity
Process Demand Planning Capacity Planning Capacity PlanningInput Customer Orders & Inventory Production Reporting Net Unconstrained Demand
Output Order lines on time Operating Cost / Productivity Rough Cut Machine Loading
Measurement Description
Measures the sales order lines on time as a percent of total
lines
Measures the effectiveness of equipment based upon machine availability, performance, and
quality.
Compares the current machine loading to the maximum machine
loading (CSIP).
Measurement Frequency Weekly Weekly Weekly
Data Granularity Weekly; Percentage Sales Order Lines on Time Weekly; Percentage CEE Weekly; Machine Load (Hours
Required / 520)
LCL or LSL LSL - Zero LCL - Calculated using I-MR chart LSL - Customer Service Interruption Point
Target 95% Average 85% Average Average Machine Loading
UCL or USL UCL - Calculated using np chart UCL - Calculated using I-MR chart USL = 1.40
Reaction Plan Investigate when cause flag appears.
Investigate when cause flag appears.
Respond when projected machine loading exceeds CSIP
Proc
ess
Map
101
The project team, with input from the process owner, developed a Responsible,
Accountable, Consultant, and Informed (RACI) Matrix in support of the metrics outlined
in the Control Plan Matrix. The purpose of the RACI Matrix is to assign names and/or
job titles to the control plan to ensure a smooth transfer of project control from the project
team to those who implement, maintain, and respond to the performance of the control
metrics. The RACI Matrix for this business case is provided in Figure 2-38.
Figure 2-38. Responsible, Accountable, Consulted, Informed (RACI) Matrix
R=Respons ibleA=Accountable
Control Plant Manager
Gateway Manager
Supply Chain Manager
Gateway Supply Chain Analys t
Downstream Resource Managers
Downstream Supply Chain Analys ts
Gateway Operators Engineering
Inventory Planning and Control
Gateway Semi-Finished Inventory Tracking I I A R C C I I
Gateway SKU Inventory Performance to Target I I A R I I I
Gateway Safety Stock Evaluation & Adjustment C C A R C C I
Scheduling Planning and Control
Gateway Firm Planning Process A A C R I I I I
Gateway Lead Time Management A A C R I I I C
Gateway Schedule Attainment Tracking A A I R I I I I
Gateway Supply Plan Attainment Tracking A A I R I I I I
Gateway Group Technology Family Maintenance I I A R C
SF Inventory DOS I I A R I I I I
Secondary Measures
Service A R R R R R R R
Constrained Equipment Effectiveness (CEE) A R I I I I I I
Capacity Planning A R I I I I I R
C=ConsultantI=Informed
102
The Supply Plan & Schedule Attainment measure gauges how well the gateway
resources are performing to their production plans and schedules. For all practical
purposes, the schedule attainment measure is part of the overall supply plan attainment
measure. The reason for the separation of the plan and the schedule is that the schedule
attainment portion includes feedback to the production operators on the performance
issues they have more control or influence over. The firm plan portion of supply plan
attainment does not always relate to production performance and was viewed as more an
organizational issue.
This Supply Plan measure was developed to gauge the synchronization between
the gateway (producing) resources and downstream (consuming) resources and provide
control feedback on the management of performance between the interdependent
objectives surrounding inventory control, constrained equipment efficiency, and customer
service (i.e. achieve and maintain service goals with the least amount of inventory and
cost). The Supply Plan compares actual production against two different dimensions of
the “plan.” The first dimension is the measure of actual production versus the constraint-
anchored plan and the second dimension is the measure of actual production to what was
needed by the customer. In an environment where variability is minimal and there are no
cost or capacity pressures, these two dimensions could be the same. Historically, the
gateway resources have been loaded (required hours of production) very heavily and
were not always able to produce what the customer needed by the date they needed it.
This is why the gateway resources were selected as the constraint – to drive the
103
availability of material to downstream resources based upon what the resources could
produce.
The schedule attainment portion of the measure is focused on how well the
gateway resources execute to the production plan requirements that are converted to the
schedule from the firm plan. This measure differs from the Supply Plan measure because
of whom it is applicable to and where it can be applied. The Supply Plan Attainment
measure is more an organizational performance measure. Schedule Attainment is an
operational measure having direct applicability to production and/or operator
performance. The Schedule Attainment measure is also where execution miscues can be
more easily be tallied using causal analysis data.
The data used for both measures is stored in a database and can be accessed via
Microsoft® Query and/or Microsoft® Access. The databases were created using Oracle
SQL Forms. The data can be accessed at various levels of information detail. The long-
range plan for both measures is to create on-line control chart applications accessible via
the planning and scheduling applications. (This feature was not available prior to the
writing of this paper.) Figures 2-39 and 2-40 represent examples of the how the
measurement data is organized.
104
Figure 2-39. Supply Plan Attainment Detail Screen
Figure 2-40. Schedule Attainment Detail Screen
105
Although emphasized as separate control variables in the inventory replenishment
diagram, buffer management and cycle frequency were found to be related to no one’s
surprise. Because of this relationship, the monitoring of both variables was combined
into one measurement application. The measurement application was created in such a
manner that every aspect of the creation of buffers and its impact on inventory can be
modeled and monitored. The application was created using Microsoft® Query and
Microsoft® Excel. The primary data components consisted of:
1. Query Input Sheet: Interface for the user to Query by stock numbers and start
and end dates.
2. Demand Individuals Control Chart: Provides a picture of actual demand,
average demand, demand standard deviation, and coefficient of variation for the
dates selected. (Coefficient of variation was provided to users as an indicator of
variability relative to the mean. A higher coefficient of variation usually indicates
the data is more spread-out and widely dispersed. The safety stock calculator
model used tends to underestimate the true safety stock requirement for
coefficient of variation values greater than 1.25 when higher service levels
(>=97%) are required.)
3. Cycle Frequency Individuals Control Chart: Provides a picture of actual lead-
time, average lead-time, lead-time standard deviation, and coefficient of variation
for the dates selected.
106
4. Safety Stock Calculator: Model that calculates buffer quantity and target
inventory based upon the desired service level protection, variability of demand,
and variability of supply.
5. Inventory Balance Monitor: Tracks inventory balance performance to the
calculated target.
In addition to the Buffer Management model, a Group Technology Cycle
Frequency monitor was developed to track the cycle variability for each product family
on a gateway resource. The process owner is able to select a group technology family by
resource for a given date range and analyze the variability around the cycle frequency for
that family. The idea behind this control is that as changeover times decrease, the cycle
time between changeovers should also decrease. If changeover times increase, the
process owner should reevaluate the cycle frequency and optimal order quantity to
determine the effect on cost. An example of the Group Technology Lead Time Monitor
is shown in Figure 2-41. (The Buffer Management model application examples can be
found in Figures 2-31, 2-32, and 2-33).
107
Figure 2-41. Group Technology Cycle Frequency Individuals Chart
The final control plan strategy focused on minimizing the frequency and impact
of schedule changes. During the Measurement Phase, schedule changes were found to be
a significant contributor in sub-optimized synchronization between product demand
requirements and actual product supply.
Schedule change guidelines were developed to define the levels of schedule
change disruption that required escalation to the appropriate levels of management.
Besides minimizing the impact of schedule changes, business justification was now
GW3 Enter Wrk Ctr No.
1 Enter Family No.
1/1/2002Enter Start Date
(mm/dd/yy)
12/31/2002Enter End Date
(mm/dd/yy)
Pre-5/1/02
Data PointsPre-5/1/02 Avg.
Cycle TimePre-5/1/02 Std Dev
Pre-5/1/02 Coeff. Of Var.
Post-5/1/02 Data Points
Post-5/1/02 Avg. Cycle Time
Post-5/1/02 Std Dev
Post-5/1/02 Coeff. Of Var.
Family Desc. 2 4 28 17 0.60 15 16 8 0.52
Cycle Time Analysis by Production Family
0
10
20
30
40
50
60
70
80
90
1/13/2
002
1/27/2
002
2/10/2
002
2/24/2
002
3/10/2
002
3/24/2
002
4/7/20
02
4/21/2
002
5/5/20
02
5/19/2
002
6/2/20
02
6/16/2
002
6/30/2
002
7/14/2
002
7/28/2
002
8/11/2
002
8/25/2
002
9/8/20
02
9/22/2
002
10/6/
2002
10/20
/2002
11/3/
2002
11/17
/2002
12/1/
2002
L/T
Days
Act. Cycle Time Avg. Cycle Time UCL LCL Lead Time Trend
Note: Avg. Lead Time may not be centered. Statistically calculated LCL is not allowed to go below zero.
108
required with each change. An understanding of the business need, the affect on other
products, and the affect on customers were examples of some of the information required
as justification. (The Schedule Change Guidelines can be found in Figure 2-26.)
Complementing the schedule change guidelines were the assignment of cause
codes found in the schedule attainment application. The cause codes will be used to track
the number of schedule changes and allow managers to identify the cause reasons that
occur most often to help direct schedule execution improvement efforts. (Examples of
the Schedule Change control chart and Schedule Change Pareto Chart can be found in
Figures 2-20 and 2-21 respectively.)
Control plan implementation also entailed documentation and training for those
identified as “Responsible” in the RACI Matrix. Documentation and training was both
process and systems-related. The process documentation and training was focused on
group technology, firm planning strategies, managing inventory to target, and safety
stock review frequency. The systems documentation and training centered on
understanding the process metrics including: updating the metrics, process out-of-control
definitions, and response plans.
2.7.2 Process Capability
In very broad terms, process capability assesses a process performance relative to
specification criteria. A process is deemed capable if virtually all of the possible variable
values fall within specification limits. [16]
109
Capability studies are viewed as a key component of the Six Sigma process. The
project team uses capability analysis to assess current process performance and to analyze
the impact of improvement efforts. Since research was unavailable that demonstrated the
use of capability analysis in transactional processes, the capability measures were kept
separate from the process performance measures that were created for the process owner
and supply chain analysts (see section 2.7.1).
Process capability is typically reported as the 6σ range of a process’s common
cause variation where σ is usually 2/ dR . [18] The Cp and Cpk indices can be used to
represent process capability. The Cp index shows how well the variation of the process
fits within the specifications and Cpk indicates how well the process can meet
specification limits while accounting for the location of the average (centering). [18]
Process performance studies also assess a process relative to specification criteria.
Process performance is typically reported as the 6σ range of a process’s total variation
(common and special cause), where σ is usually estimated by either average range or by
s, the sample standard deviation. [18] The Pp and Ppk indices are typically used to represent
process performance. The Pp index shows how well the total variation fits within the
specifications and Ppk indicates how well the process can meet specification limits while
accounting for the location of the average (centering). [18]
My research discovered various opinions on which measures assess short-term
capability and which measures assess long-term capability. [18] For example, one opinion
holds that Cp and Cpk typically assess short-term capability by using a “short-term”
standard deviation estimate, while Pp and Ppk typically assess overall long-term capability
110
by using a “long-term” standard deviation estimate. Other opinions are based on the
differing calculation methods for standard deviations -ranging from lumping all of the
process data together to determining standard deviation from a variance components
model. [18]
Defining and reporting process capability can provide misleading process
information if the right approach is not used. Two areas were recognized as potential
issues with developing capability measures: the use of computer software for conducting
capability analysis and the application of capability analysis to processes where
meaningful specifications do not exist.
Statistical computer software like MINITABTM can be very convenient and
helpful in simplifying the calculation of process capability. Where good communication
and agreement has occurred in determining the techniques and use of capability metrics,
statistical computer software should support Six Sigma improvement efforts. However,
in situations where the use of capability has not been agreed upon, there is a danger that
process capability metrics will be employed incorrectly. This issue is particularly
prevalent in situations where training advocates the use of a statistical software package
without giving enough guidance on its use. For those project leaders that have minimal
experience with capability analysis and/or statistics in general, there is a tendency to
believe that entering process data into the computer package will provide a valid and
reliable capability metric. This, of course, is total nonsense.
A second issue concerns the attempt to apply capability analysis to processes
where meaningful specifications do not exist. Many project leaders may feel pressure to
111
use capability analysis where it does not fit or use the wrong capability indices. AIAG
(1995) states that “the key to effective use of any process measure continues to be the
level of understanding of what the measure truly represents. Those in the statistical
community who generally oppose how Cpk numbers, for instance, are being used are
quick to point out that few “real world” processes completely satisfy all of the conditions,
assumptions, and parameters within which Cpk has been developed. Further, it is the
position of [the AIAG] manual that, even when all conditions are met, it is difficult to
assess or truly understand a process on the basis of a single index or ratio number.”[22]
Another hurdle to consider when using capability indices is the comfortability of a
process owner in using these indices to measure the performance of a process. For
owners of transactional processes, capability indices may not feel as intuitive as a control
chart in measuring the performance of a process.
The potential negative effects associated with inappropriate capability analysis
application can be minimized if an organization defines the necessary elements of process
control that must be in place before a capability assessment can be performed and then
communicates how and when capability will be measured.
Both areas of controversy were prevalent in determining how capability would be
measured in this business case. The organization sponsoring the project did define how
Cp, Cpk, Pp, or Ppk can be applied to operational processes but did not cite examples of the
application of capability indices to transactional processes. The organization failed to
provide information and training relating to other accepted capability indices and did not
cite examples for alternatives to capability indices where specifications were not
112
available. Project leaders were led to believe Cp, Cpk, Pp, or Ppk metrics were the only
acceptable capability indices available and that every process has valid specification
limits.
Our project team debated whether meaningful specification limits could be
defined and, if so, if capability indices would be of any use in measuring the material
planning process as firm planning, group technology scheduling, and planning parameter
management changes were implemented. Inventory targets, minimum inventory levels,
and maximum inventory levels for each SKU could be calculated based upon the process
input information for supply, demand, and the desired service level. The minimum and
maximum inventory levels could be viewed as process specification limits. These
specifications are not set by a customer and are not statistical control limits. The
specifications are established based upon some key process information. This process
information includes: Average Lead Time, Standard Deviation of Lead Time; Average
Demand during Average Lead Time; Standard Deviation of Demand; Desired Customer
Service Level; and any miscellaneous process requirements. Miscellaneous process
information affecting specification limits may include: product shelf-life; optimal
processing conditions (i.e. the longer the material is in queue waiting processing the more
waste that is incurred when it is used as input); storage limitations; etc. Table 2-2 briefly
outlines the definitions of the process information.
113
Process Information
Description
Average Lead Time
Average number of days between
production runs.
Lead Time Variability
Standard Deviation of the number of days
between production runs.
Average Demand during Average
Lead Time
Average total demand (independent and
dependent) during the average lead-time.
Demand Variability
Standard deviation of total demand.
Miscellaneous Process Requirements
Example: Product with a specific shelf-life;
Jumbo freshness for slitting productivity.
Table 2-2. Key Process Information Definitions
The maximum and minimum inventory levels could be subject to change if any
part of the process information changed. For example, if the average lead time,
114
variability of lead time, average demand during average lead time, and the desired service
level remained constant but demand variability increased - the minimum, maximum, and
target inventory levels would be projected to increase to protect against the change in
demand variability. Conversely, the minimum, maximum and target inventory levels
would be projected to decrease if variability were reduced.
As the project team worked with developing the specification definitions, we
learned that each SKU needed to be evaluated independently. Like an operational
process where each manufactured product has design, quality, process, or customer
specifications that will optimize the performance of the product, each SKU has similar
characteristics that differentiate it from other SKU’s relative to the level of inventory that
is carried. This approach is a departure from this organization’s inventory goal-setting
techniques of the past. The organization typically communicated inventory reduction
goals by market segment. Within each market segment, business teams were formed
around product groupings. Each business team within the market segment is held
accountable for achieving the same inventory reduction goal as its market segment
regardless of the complexity of their manufacturing processes, products, or customer
requirements. This approach led to the sub-optimization of other metrics like customer
service or cost control in an attempt to achieve required inventory targets.
The top five SKU’s in volume for the gateway work centers were selected for
capability analysis. The project team obtained approval from management to use the
methodology we developed to establish minimum and maximum inventory levels as
specification limits for each SKU. The inventory data (as measured in average days-of-
115
stock) was then evaluated for stability using Individuals and Moving Range (I-MR)
control charts. Figure 2-42 represents the actual data used for Gateway SKU 1 to
evaluate stability.
Figure 2-42: Gateway Stock Keeping Unit 1 I-MR Chart
Gateway SKU 1 accounts for over 80% of the total average inventory for one of
the Gateway work centers and just over 30% of the total average inventory for all
Gateway work centers. The days of stock data for Gateway SKU 1 was found to be
stable and fit for capability analysis.
2010S ubgroup 0
20
10
0
Ind
ivid
ua
lV
alu
e
Me an=11.84
UCL=21.32
LCL=2.364
10
5
0
Mo
vin
gR
an
ge
R=3.563
UCL=11.64
LCL=0
Observation 2010S ubgroup 0
20
10
0
Ind
ivid
ua
lV
alu
e
Me an=11.84
UCL=21.32
LCL=2.364
10
5
0
Mo
vin
gR
an
ge
R=3.563
UCL=11.64
LCL=0
Observation
116
The next step in preparing to run a capability analysis was to determine the
specification limits for this SKU. The first decision was to use days of stock as the unit of
measure rather than inventory quantity or inventory value. The days of stock unit of
measure was selected over inventory value in cost dollars or inventory quantity to drive
the focus of the capability analysis towards inventory optimization versus inventory
investment (which does not always correlate to the level of inventory optimization).
The Average Days-of-stock for Gateway SKU 1 was reported on a monthly basis
and used the following calculation: Average Daily Inventory / Previous 3 months
average daily usage. This calculation was approved by management and coincided with
corporate and division guidelines for calculating days-of-stock.
Specification limits were evaluated using a combination of the buffer
management model referenced earlier in this chapter (see Figures 2-31, 2-32, and 2-33)
and process information from supervisors, operators, and analysts of the Gateway and
downstream work centers. The specification limits for the Gateway SKU 1 were heavily
influenced by process-related information. Through discussions with Gateway and
downstream work center supervisors and equipment operators we discovered the sooner
SKU 1 material was processed at downstream work centers following its release from the
gateway work center, the faster the processing rate and the less the material waste. This
window of optimal performance could include material that was up to 5 days old - after
which average productivity and waste performance for downstream work centers dropped
appreciably. Data verifying the productivity loss from using material greater than 5 days
old was analyzed using production reporting. The effect of delayed consumption on
117
material waste was recorded by operators at downstream work centers using waste-by-
cause tally sheets. (NOTE: These production reporting and tallying activities were part
of the production process before this project team was formed. The link between
productivity and waste issues had not been incorporated into the planning and scheduling
process until this project.) On average, downstream work centers experienced a 2%
increase in waste and a 3% loss in productivity (as measured in yards per hour) when
converting material greater than 5 days old.
Gateway SKU 1 was used as an example for this paper because establishing the
specification limits for this process demonstrated how a process variable like average
lead time may indicate how the planning and scheduling process was managed but may
be inadequate in describing how the process should be managed based on other variables.
After specification limits had been determined the team turned its efforts toward
selecting the capability metric(s) that would be used for reporting and analysis. The
primary questions the project team sought to answer concerning which capability metric
would provide the best picture of how the process is performing included: 1) What is the
difference between short-term and long-term capability and how does it apply to this
process?; 2) Is the amount of variation and its relationship to the tolerance most
important?; 3) Is the measurement of process centering most important?; 4) Is the
comparison of actual inventory values to target appropriate for this process?
The project team initially started to focus on measuring inventory to target using
such measures as the Z score and Cpm. The objective of the Z score is to indicate how
many standard deviations a value (x) is from the mean. In order to improve the capability
118
of the process, the Z score would need to be reduced. A reduction in Z score correlates to
a reduction in variability in managing inventory to target, and conversely, an increase in
Z score correlates to an increase in variability in managing inventory to target.
The Cpm index incorporates the target when calculating the standard deviation.
Instead of comparing the data to the mean (like Cp or Cpk), the data is compared to the
target. These differences are squared. Any observation that is different from the target
observation will increase the Cpm standard deviation. As this difference increases, so does
the sigma. As this sigma becomes larger, the Cpm index gets smaller. If the difference
between the data and the target is small, so too is the sigma. And as this sigma gets
smaller, the Cpm index becomes larger. The higher the Cpm index, the better the process.
The project team encountered problems in applying the Z score and Cpm index
across each SKU. For Gateway SKU 1, the target value was less important than the
upper and lower specification limits. As long as the inventory replenishment and
consumption process operates within the 1-day LSL and 5-day USL, inventory will be
optimized to fit the needs of both the customer and the manufacturing facility (cost and
waste).
The project team also explored the use of Cp, Cpk, Pp, or Ppk capability metrics.
The MINITABTM software application provides very convenient tools for calculating the
Cp, Cpk, Pp, or Ppk capability metrics for both normal and non-normal distributions. Once
the specifications were defined and the method for gathering actual data was developed,
the reporting of these measures was very simple using MINITABTM software. Data for
past 20 months indicated the lead-time had averaged just over 10 days for this SKU and
119
the days of stock for the same time period averaged 11.84. Using capability metrics such
as Cp, Cpk, Pp, or Ppk indicated exactly what we already knew: Our process was not
capable of performing at those specification limits because we did not plan to. Capability
analysis for the performance of the process using an Upper Specification Limit (USL) of
5 days and a Lower Specification Limit (LSL) of 1 day yielded very poor capability
results for the past 20 months. Nevertheless, capability metrics were created as a means
of comparing the process performance as it was to the process performance based upon
the changes made to meet the specifications. The capability results for the past 20
months are provided in Figure 2-43 using the MINITABTM “Capability Sixpack
(Normal)” reporting tool.
Figure 2-43. Capability Results – Baseline Data for Gateway SKU 1
20100
24
16
8
0
Individua l a nd MR Cha rt
Obser.
Indivi
dual
Value
Mean=11.84
UCL=21.32
LCL=2.364
12
8
4
0
Mov.R
ange
R=3.563
UCL=11.64
LCL=0
20100
Las t 20 Obse rva tions20
15
10
5
ObservationNumber
Value
s
51
Ca pa bility P lotProcess Tolerance
IIIIII
IISpecifications
WithinOverall
21135
Norma l P rob Plot21135
Ca pa bility His togram
WithinStDev:Cp:Cpk:
3.158830.21
-0.72Overall
StDev:Pp:Ppk:
3.976150.17
-0.57
20100
24
16
8
0
Individua l a nd MR Cha rt
Obser.
Indivi
dual
Value
Mean=11.84
UCL=21.32
LCL=2.364
12
8
4
0
Mov.R
ange
R=3.563
UCL=11.64
LCL=0
20100
Las t 20 Obse rva tions20
15
10
5
ObservationNumber
Value
s
51
Ca pa bility P lotProcess Tolerance
IIIIII
IISpecifications
WithinOverall
21135
Norma l P rob Plot21135
Ca pa bility His togram
WithinStDev:Cp:Cpk:
3.158830.21
-0.72Overall
StDev:Pp:Ppk:
3.976150.17
-0.57
120
The initial capability measures provided for interesting observations. First, the
capability of the process as measured by Cp and Pp was very poor. Since specification
limits were previously not used to measure this process, this outcome was not surprising
to the project team. Specification limits were selected that represented implementation of
process changes. A second observation was the negative values of Cpk and Ppk. This
result was also related to the selection of specification limits. Since Cpk is equal to
��
���
� −−3sLSLx,
3sxUSLmin , if x is greater than the upper specification limit and the value
of 3s
xUSL − is less than 3sLSLx − , a negative value is possible. This also holds true for Ppk
as well since the only difference between Cpk and Ppk is the calculation of standard
deviation. Obviously, theses indices were not useful in ascertaining a measurement of
baseline capability.
As was stated earlier in this section, determining whether capability indices
provided any meaning to a transactional process was a key issue for this project team.
Making changes that affect the planning and scheduling process related to the top 5
Gateway SKU’s was seen as an opportunity to gauge how well capability metrics
represented process improvement. Continuing with our Gateway SKU 1 example, the
project team (which now included the temporary membership of supervisors, operators
and analysts related to the production and consumption of Gateway SKU 1) implemented
changes that were intended to improve the capability of the process using a USL of 5 and
LSL of 1. These changes included: moving from bi-weekly production runs to weekly
runs; synchronizing the critical downstream work centers for consumption of material
121
based upon the production schedule for Gateway SKU 1; removal of the stock buffer for
Gateway SKU 1; removal of stock buffers for all downstream SKU’s; and subordination
of the production schedule sequence of all products run on the same resource as Gateway
SKU 1 to the production schedule of Gateway SKU 1. The capability results for the nine
months following implementation are presented in Figure 2-44.
Figure 2-44. Capability Results – Post-Improvement Data for Gateway SKU 1
Additional data points will need to be reported in order to assess the true impact
of the changes made to the planning process for Gateway SKU 1. However, the indexes
9876543210
4
3
2
1
Individua l a nd MR Cha rt
Obser.
Indivi
dual
Value
Mean=2.711
UCL=4.307
LCL=1.115
1.8
1.2
0.6
0.0
Mov
.Ran
ge
R=0.6
UCL=1.960
LCL=0
9876543210
La s t 9 Obse rva tions3.2
2.8
2.4
2.0
Observation Number
Value
s
51
Ca pa bility P lotProcess Tolerance
IIIIII
IISpecifications
WithinOverall
432
Norma l Prob Plot3.02.52.0
Ca pa bility His togra m
WithinStDev:Cp:Cpk:
0.5319151.251.07
OverallStDev:Pp:Ppk:
0.4712001.411.21
9876543210
4
3
2
1
Individua l a nd MR Cha rt
Obser.
Indivi
dual
Value
Mean=2.711
UCL=4.307
LCL=1.115
1.8
1.2
0.6
0.0
Mov
.Ran
ge
R=0.6
UCL=1.960
LCL=0
9876543210
La s t 9 Obse rva tions3.2
2.8
2.4
2.0
Observation Number
Value
s
51
Ca pa bility P lotProcess Tolerance
IIIIII
IISpecifications
WithinOverall
432
Norma l Prob Plot3.02.52.0
Ca pa bility His togra m
WithinStDev:Cp:Cpk:
0.5319151.251.07
OverallStDev:P
WithinStDev:Cp:Cpk:
0.5319151.251.07
OverallStDev:Pp:Ppk:
0.4712001.411.21
122
do indicate an improvement has been made in capability and in the level of inventory as
measured in days-of-stock from levels reported during the 20 months prior.
Although the project team proved that capability analysis could be done for this
transactional process, the team determined that using the measures of Cp, Cpk, Pp, and Ppk
was not practical based upon the level of understanding of capability analysis of the
process owner. More automated, timely, and simple indicators were explored to provide
the owner a general sense of process capability and performance. To accomplish these
objectives, simple control charts were created. The I-MR charts presented in Figures
2-45 and 2-46 are provided as examples of the format used to reflect the change in
process performance following the implementation of the improvement strategies.
Figure 2-45. Days-of-Stock I-MR Chart – Gateway SKU 1
252015105Subgroup 0
20
10
0Indi
vidua
lVal
ue
1
Mean=2.713UCL=4.384
LCL=1.041
15
10
5
0
Mov
ing
Ran
ge
R=0.6286UCL=2.054
LCL=0
252015105Subgroup 0
20
10
0Indi
vidua
lVal
ue
252015105Subgroup 0
20
10
0Indi
vidua
lVal
ue
1
Mean=2.713UCL=4.384
LCL=1.041
15
10
5
0
Mov
ing
Ran
ge
R=0.6286UCL=2.054
LCL=0
123
Figure 2-46. Days-of-Stock I-MR Chart-Gateway Total
The process improvement results for this business case can best be described as
guardedly optimistic. Short-term performance of the material requirements planning
process has proven to be successful. However, long-term process capability is yet to be
proven.
Summary measures were incorporated to gauge the change in semi-finished
inventory performance resulting from the improvements in the Material Planning
Process. These summary measures were valuable as means of documenting process
changes and their effect on the performance of the process. The measures were also
helpful to the process owner for communicating general inventory performance to
managers and gateway resource team members. The first summary measure tracks the
605040302010Subgroup 0
222120191817161514
Indi
vidua
l Val
ue
1Mean=16.90
UCL=18.87
LCL=14.94
3
2
1
0
Mov
ing
Ran
ge
R=0.74
UCL=2.418
LCL=0
124
change (from the baseline) in inventory dollars and days-of-stock in total for products
manufactured at the gateway resources. The second summary measure reports the
change in inventory dollars and days-of-stock for semi-finished SKU’s directly
downstream from the gateway resources. These inventory measurements were intended
to represent the impact of firm planning and group technology scheduling as compared
with the baseline inventory levels.
Figures 2-47 and 2-48 represent the gateway and downstream semi-finished
inventory performance improvement metrics:
Figure 2-47. Gateway Inventory Improvement Measure
125
Figure 2-48. Downstream Inventory Improvement Measure
The results achieved for the project at the time this paper was written provides
support the process improvements and their supporting control plans are having the
desired effect on the material planning process for the gateway work centers. If not for
the inventory build (for the gateway work center equipment upgrade), semi-finished
inventory reduction would be between $800,000 and $900,000 and days-of-stock would
be averaging between 15 and 16 days. However, these special cause situations must be
included as part of managing the process.
126
Chapter 3 Results and Conclusions
This section will be presented in two parts. The first part will summarize the
conclusions reached surrounding the question: “Can Six Sigma Methodology be
Successfully Applied to Transactional Processes?” The second part of this section will
address Six Sigma-related topics that are recommended for future study.
It cannot be implied that the success or failure of a project is the result of using
the Six Sigma Methodology. There is some degree of subjectivity that is employed in the
identification of the critical inputs and solutions for any of the process improvement
techniques available. As long as there is some subjectivity there is a risk of selecting the
incorrect input variables and/or improvement solutions.
Project results are also influenced by how project teams apply the improvement
tools available to them. The incorrect application of tools can mislead the team on the
importance of data collected, the relevance of solution criteria, the capability of the
process, and the effectiveness of the measurements in identifying process problems.
Other dynamics also play a critical role in whether a project succeeds. The
availability and quality of project team members, the quality of the process improvement
training, the availability and quality of support resources, the amount of funding available
(if capital equipment is required), and organizational culture and structure are examples
of other variables that may affect the outcome of a project.
127
3.1 Research Problem Results
The response to the question “Can Six Sigma Methodology be Successfully
Applied to Transactional Processes?” is not a binary “yes” or “no.” This study
demonstrated that some tools provided in the Six Sigma Methodology did not fit the
transactional process improvement requirements while other tools complemented
improvement efforts. The results of this business case provide only a brief glimpse into
the applicability of Six Sigma methodology to transactional processes. The fit of Six
Sigma Methodology to other types of transactional processes should also be given some
consideration. This section will briefly report on the relevance to other types of
transactional processes and summarize the results of the applicability of Six Sigma tools
to this business case.
During the course of my research, I found very few examples that detailed the
application of Six Sigma in improving transactional processes. The lack of published,
detailed transactional process examples came as somewhat of a surprise. Quality Digest
recently conducted a Six Sigma survey to find out who’s using Six Sigma and what kind
of programs are being implemented. Approximately 87,500 Quality Digest readers were
asked to participate in the survey. A total of 2,870 responses were received. (The survey
results may include more than 1 response from the same company.) The survey results
were interesting from the perspective of the types of programs Six Sigma was being
applied to. The application of Six Sigma to transactional processes appears to be very
128
strong – although not as widespread as operational processes. Figure 3-1 represents the
results of the Quality Digest Six Sigma survey.
Figure 3-1. Quality Digest Survey Results [23]
The survey results would lead you to believe there should be an abundance of
published work demonstrating the application of Six Sigma to transactional processes.
Through my research, I was able to find examples of transactional processes where Six
Sigma had been applied. I was able to gain access to a handful of detailed examples that
demonstrated what tools were used to arrive at a process improvement strategy and the
results of the process improvements. None of the examples I found elaborated on which
Distribution of Six Sigma Programs by Functional Areas
520
357 349
311 301 300
256240
207193 190
100
0
100
200
300
400
500
600
Manufa
cturin
g
Plant O
perat
ions
Engine
ering
Custom
er Serv
ice
Test/In
spec
tion
Admini
strati
on
Purcha
sing
Shippin
g/Rec
eiving
Sales
Resea
rch/D
evelo
pmen
t
Docum
ent C
ontro
l
Polluti
on Prev
entio
n
Res
pond
ents
Source: Quality Digest Six Sigma SurveyNote: This was asked only of respondents whose companies have a Six Sigma program in place.
129
Six Sigma tools did not work well for their process. The detailed information I was
hoping to find was either protected by a company as proprietary or protected by
consultants (maybe due to the costs of implementation as discussed in this Quality Digest
article).
Another area of confusion surrounding these survey results is the question of:
What constitutes a Six Sigma project? When a company introduces Six Sigma, there is
significant pressure to justify the cost of training and to validate that the methodology
works by classifying process improvement gains as fitting under the Six Sigma umbrella.
There is also an attraction to the convenience of having one database or location for
capturing all project savings. It was evident at GE, Allied Signal, and the company
studied in this paper, that many improvement projects were put into the "Dollars saved by
Six Sigma" category even though they may not have used Six Sigma tools to achieve the
results. So - how many Six Sigma projects are really Six Sigma projects?
Another potential explanation for the lack of Six Sigma process improvement
examples is the desire for organizations and consulting firms to protect their Six Sigma
application knowledge and/or proprietary process information.
Although growing in popularity, Six Sigma has not been embraced or
implemented by a majority of organizational sectors. A Quality Digest (Nov. 2001)
survey of about 4,300 of its 75,000 readers, asked respondents to provide their
perceptions of Six Sigma, and if they had experience with it, the results of their
experience. Among the respondents, only a small number of companies have
130
implemented a formal Six Sigma program and the vast majority of those were units of
large corporations.[24]
Some Six Sigma consultants point to a couple of reasons why Six Sigma has been
primarily embraced by big organizations. The first potential reason is the larger the
company the more areas for improvement. Greg Brue, president and CEO of Six Sigma
Consultants, explained that because Six Sigma methodology is dependent upon
identifying concrete areas for improvement that directly affect the bottom line. The more
numerous or glaring the problem areas, the easier it is to launch a successful Six Sigma
program.[24]
A second potential factor is that small companies tend to have a more difficult
time assigning the resources necessary to effectively implement Six Sigma. Thomas
Pyzdek, a published Six Sigma author and consultant, and John Kullmann, director of
marketing at Six Sigma Qualatec, both suggest that companies with fewer than 500
employees struggle with implementing Six Sigma due to the inability to assign dedicated
resources.[24]
As the research for this paper progressed through the DMAIC process, many Six
Sigma tools were evaluated for their applicability and use in improving the transactional
business case. In general the tools that were less data-driven and more subjective in their
use were more easily applicable. Subjective tool examples include process mapping,
Cause and Effects Matrix, Failure Mode and Effects Analysis, and Stakeholder Matrix.
The Six Sigma tools that were more difficult to apply were more data-related
and/or statistical in nature. Examples of these tools include Gage Reproducibility and
131
Repeatability, live Design of Experiments, Correlation and Regression, Analysis of
Variance tests, Chi-Square Tests, and classical capability metrics.
Perhaps by their nature, transactional process improvement efforts may not lend
themselves to the applicability of data-related tools. To follow are some observations
relating specifically to this business case that may better describe the difficulty with
applying some of the Six Sigma tools:
1. The material planning work process that creates the outcome called semi-
finished inventory is somewhat invisible. Material planning revolves around
information handled and stored in a manufacturing and planning computer
application. The observable work results are not very tangible and make
understanding how the work gets done more difficult.
2. There were a lack of facts and data specific to the material planning process.
The process understanding that existed was narrowly focused and somewhat
anecdotal. These circumstances made it difficult to identify specific variables that
correlated to sub-optimal inventory results.
3. There were insufficient examples and training materials relating to
transactional processes to draw experience from. This shortcoming manifested
itself in the overemphasizing of statistical applications – including the use of
computer software (i.e. MINITABTM).
4. Meaningful customer specification limits were not initially available for this
process. Classical capability metrics like Cp, Cpk, Pp, or Ppk are more difficult to
132
apply to transactional processes. There was pressure from management to
describe capability using only these classical metrics.
5. Because the material planning process was more virtual, experiments were
conducted in a laboratory setting versus live modeling. Although this type of
simulation protected the process from disruption, it was not able to predict the
response of all process variables as accurately and robustly as a live experiment.
6. The initial scope of the project was too vague for any associated data to be
very meaningful. This issue resulted in “paralysis-by-analysis” as the team
attempted to discern how the data that was available fit the project goals.
3.2 Recommendations for Future Study
As long as a research void exists around using Six Sigma for transactional process
improvement, areas of future study will be numerous. The purpose of this section is to
briefly describe a few opportunities for future study that were discovered during this
business case application study.
An area that I found to be in need of additional research and clarification is the
application of capability analysis to transactional processes. This type of study could
include an analysis of how capability can be defined for processes that lack meaningful
specification limits, what tools can be used to define process capability, and the success
or failure in achieving long-term capability based upon the tool chosen. Additional study
133
would also be welcome around the effect of assuming data to be normally distributed and
the importance of normality in different situations.
Another area of interest is a study relating to the correlation between the
success/failure and span of time until project close of a Six Sigma project relative to how
well a project is initially defined. This may include: the amount of data available to
support the defined project opportunity; the magnitude of the process selected for
improvement; the operating boundaries (capital, resources, etc.); and the support from
management. This issue should be investigated for both operational and transactional
process improvement projects.
Studies comparing the success rate of Six Sigma and various other process
improvement strategies across different organizational sectors and process types could
prove helpful in understanding which improvement strategies worked best in certain
organizational models and processes.
Research is needed regarding the effectiveness of using simulation in
transactional process environments. Topics of study could include: recommended
simulation tools; DoE application and strategies; statistical techniques for measuring
simulation results; and simulation validation techniques.
Additional case studies would be helpful in understanding the potential
application scope of Six Sigma. For example, whether the methodology can be applied to
growth strategies, or whether Six Sigma tools can be applied to academic processes. In
what other process settings (operational or transactional) are Six Sigma tools inefficient
or a total waste of time?
134
A study centered on the level or breadth of statistical training that should be
provided as part of implementing the Six Sigma program would be interesting. The study
could prove useful in demonstrating whether de-emphasizing statistics for transactional
Six Sigma projects delivers better and faster results than training that focuses on or
emphasizes statistics.
135
Bibliography
1. NIST/SEMATECH (2003), Engineering Statistics Handbook, National Institute of
Standards and Technology, International SEMATECH.
2. American Production and Inventory Control Society, Inc., APICS Dictionary, Falls
Church, VA.,1995.
3. Laux, Daniel T., Six Sigma Evolution Clarified – Letter to the Editor, 2 pp. Online.
IsixSigma, 2002.
4. Swinney, Zach, 1.5 Sigma Process Shift Explanation. 2pp. Online. isixsigma, 2001.
5. Ramberg, John S., Six Sigma: Fad or Fundamental? Quality Digest, May 2000.
6. Stamatis, D.H., Who Needs Six Sigma Anyway? Quality Digest, May 2000.
7. Basu, Ron (2001). Six Sigma to Fit Six Sigma, IIE Solutions, v33, i7, p28.
8. Martin, Sheree, Six Sigma: Quality’s New King? Fabricating Equipment News, 2001.
9. Costanzo, Chris, Celebrated Six Sigma Has Its Critics, Too, American Banker,
Aug. 28, 2002, v167, i165, p165.
10. Clifford, Lee, Why You Can Safely Ignore Six Sigma. Fortune, 2001, v143, i2, p140.
11. Gyorki, John R., Machine Safety Deserves Better than Six Sigma. Machine Design,
1999,v71, i1, p112.
12. Hahn, Gerald I., Hill, William J., Hoerrl, Roger W., Zinkgraf, Stephen A., The Impact
of Six Sigma Improvement - - A Glimpse into the Future of Statistics, The American
Statistician, 1999, v53, i3, p208.
136
13. Wyper, Bill, Harrison, Alan, Deployment of Six Sigma Methodology in Human
Resource Function: A Case Study, Total Quality Management, July 2000, pS720.
14. Van Kooy, Mark, Edell, Lori, Melchiorre Scheckner, Heather, Use of Six Sigma to
Improve the Safety and Efficacy of Acute Anticoagulation with Heparin, Journal of
Clinical Outcomes Management, Aug 2002, v9, i8, p445.
15. San, Dee C., Six Sigma Method Application in Reducing ED Wait Time, The Journal
of the American Medical Association, July 17, 2002, v288, i3, p290.
16. Pande, Peter S., Neuman, Robert P., Cavanagh, Roland R., The Six Sigma Way. New
York: McGraw Hill, 2000.
17. Chemical Week Associates, Is Six Sigma Fast Enough?, Chemical Week, March 1,
2000, p26.
18. Breyfogle, Forest W. III, Measurement of Process Capability: Cp, Cpk, Pp, Ppk,
Probability Plotting and Six Sigma. Smarter Solutions. Austin, TX, 1999.
19. Umble, Michael, Srikanth, Mokshagundam L., Synchronous Management-Volume 2,
Guiliford: The Spectrum Publishing Company, 1997.
20. Woodall, William H., Controversies and Contradictions in Statistical Process Control.
Journal of Quality Technology, v32, no.4, October 2000.
21. Deming, W.E. (1986). Out of the Crisis. Massachusetts Institute of Technology,
Center for Advanced Engineering Study, Cambridge, Mass.
22. AIAG (1995), Statistical Process Control (SPC) Reference Manual, Chrysler
Corporation, Ford Motor Company, General Motors Corporation.
137
23. Dusharme, Dirk, Six Sigma Survey: Big Success…but what about the other 98
percent? Quality Digest. Feb. 2003.
24. Dusharme, Dirk, Six Sigma Survey: Breaking Through the Six Sigma Hype. Quality
Digest. Nov. 2001.
138
Supplemental Research References
1. Box, G.E.P. and Kramer, T. (1992). “Statistical Process Monitoring and Feedback
Adjustment. A Discussion”, Technometrics 34, pp 251-285.
2. Chowdhury, Subir, Working Toward Six Sigma Success. Manufacturing Engineering,
2001,v127, i1, p14.
3. Cusin, Jean-Daniel (2001). A Lot Size of One? Get Serious, APICS: The
Performance Advantage, November/December 2001.
4. Echempati, Raghu, White, Christy, Case Study of Hinge Alignment Problems: A Six
Sigma Quality Analysis, Production and Inventory Management Journal, 2000, v41,
no. 2.
5. Frazier, Gregory V, Reyes, Pedro M., Applying Synchronous Manufacturing
Concepts to Improve Production Performance in High-Tech Manufacturing,
Production and Inventory Management Journal, 2000, v41, no. 3.
6. Grossman, Thomas A., Teacher’s Forum: Spreadsheet Modeling and Simulation
Improves Understanding of Queues. Institute for Operations Research and the
Management Sciences, 1999.
7. Hoerl, Roger W, Six Sigma Black Belts: What Do They Need to Know?, Applied
Statistics Group, 2001, v33, no. 4.
8. Juran, J.M. (1997). “Early SQC: A historical Supplement”. Quality Progress pp. 129-
137.
139
9. Lucier, Gregory T., Seshardi, Sridhar, GE Takes Six Sigma Beyond the Bottom Line.
Strategic Finance, 2001, v82, i11, p41.
10. Miller, Sandra, Lean Sigma. APICS 2001 International Conference Proceedings,
2001.
11. Pressly, Thomas R., Six Sigma: The Breakthrough Management Strategy
Revolutionizing the World’s Top Corporations. The Ohio CPA Journal, 2001,v60, i2,
p69.
12. Schmitt, Bill, Expanding Six Sigma. Chemical Week, 2001, v163, i8, p21.
13. Shand, Dawne, Six Sigma. Computerworld, 2001, p38.
14. Sheridan, John H., Lean Sigma Synergy. Industry Week, 2000,v249, i17, p81.
15. Srikanth, Mokshagundam L., Umble, Michael, Synchronous Management-Volume 1,
Guiliford: The Spectrum Publishing Company, 1997.
16. Walden, Joseph, Applying Six Sigma Methodology to Supply Chain Operations.
APICS 2001 International Conference Proceedings, 2001.
17. Walker, William T., Synchronized for Growth. APICS: The Performance Advantage,
April 2001.
18. Wood, Andrew, Making Six Sigma Benefits Stick. Chemical Week, 2001, v163, i19,
p40.