SIMULATION-BASEDLEAN SIX-SIGMA AND DESIGN FOR SIX-SIGMA

BASEM EL-HAIKRAID AL-AOMAR

A JOHN WILEY & SONS, INC., PUBLICATION

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SIMULATION-BASED LEAN SIX-SIGMA AND DESIGN FOR SIX-SIGMA

SIMULATION-BASEDLEAN SIX-SIGMA AND DESIGN FOR SIX-SIGMA

BASEM EL-HAIKRAID AL-AOMAR

A JOHN WILEY & SONS, INC., PUBLICATION

Copyright © 2006 by John Wiley & Sons, Inc. All rights reserved.

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El-Haik, Basem.Simulation-based lean six-sigma and design for six-sigma / by Basem El-Haik,

Raid Al-Aomar.p. cm.

“A Wiley-Interscience publication.”Includes bibliographical references and index.ISBN-13: 978-0-471-69490-8ISBN-10: 0-471-69490-81. Six sigma (Quality control standard) 2. Total quality management. 3. Production

engineering. I. Al-Aomar, Raid. II. Title.TS156.E383 2006658.5′62–dc22 2006048112

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CONTENTS

PREFACE xi

PART I SIX-SIGMA FUNDAMENTALS 1

1 Six-Sigma Fundamentals 3

1.1 Introduction, 31.2 Quality and Six-Sigma Defined, 51.3 Introduction to Process Modeling, 71.4 Introduction to Business Process Management, 91.5 Measurement Systems Analysis, 111.6 Process Capability and Six-Sigma Process

Performance, 121.7 Overview of Six-Sigma Improvement: DMAIC, 151.8 Six-Sigma Goes Upstream: Design for Six-Sigma, 181.9 Summary, 18

2 Lean Six-Sigma Fundamentals 21

2.1 Introduction, 212.2 Lean Six-Sigma Approach, 232.3 LSS-Enhanced DMAIC, 262.4 Lean Manufacturing, 302.5 Value Stream Mapping, 352.6 Lean Techniques, 392.7 Summary, 57

vii

3 Design for Six-Sigma Fundamentals 59

3.1 Introduction, 593.2 Transaction-Based Design for Six-Sigma, 613.3 Service Design for Six-Sigma, 633.4 Service DFSS: The ICOV Process, 653.5 Service DFSS: The ICOV Process in Service Development, 683.6 Summary, 69

PART II SIMULATION FUNDAMENTALS 71

4 Basic Simulation Concepts 73

4.1 Introduction, 734.2 System Modeling, 744.3 Simulation Modeling, 844.4 The Role of Simulation, 894.5 Simulation Software, 954.6 Summary, 96

5 Discrete Event Simulation 99

5.1 Introduction, 995.2 System Modeling with DES, 1005.3 Elements of Discrete Event Simulation, 1045.4 DES Mechanisms, 1125.5 Manual Simulation Example, 1215.6 Computer DES Example, 1265.7 Summary, 131

6 The Simulation Process 133

6.1 Introduction, 1336.2 Categories of Simulation Studies, 1346.3 Systematic Simulation Approach, 1396.4 Steps in a Simulation Study, 1446.5 Example: Applying Simulation Process to a Hospital

Emergency Room, 1586.6 Summary, 166

7 Simulation Analysis 167

7.1 Introduction, 1677.2 Terminating Versus Steady-State Simulation, 1687.3 Determination of Simulation Run Controls, 1727.4 Variability in Simulation Outputs, 1817.5 Simulation-Based Optimization, 185

viii CONTENTS

PART III SIMULATION-BASED SIX-SIGMA AND DESIGN FOR SIX-SIGMA 189

8 Simulation-Based Six-Sigma Road Maps 191

8.1 Introduction, 1918.2 Lean Six-Sigma Process Overview, 1928.3 Simulation-Based Lean Six-Sigma Road Map, 1948.4 Simulation-Based Design for a Six-Sigma Road Map, 2038.5 Summary, 218

9 Simulation-Based Lean Six-Sigma Application 219

9.1 Introduction, 2199.2 3S-LSS Integrated Approach, 2219.3 3S-LSS Case Study, 2269.4 Summary, 265

10 Simulation-Based Design for Six-Sigma Application 267

10.1 Introduction, 26710.2 3S-DFSS Process, 26910.3 3S-DFSS Case Study: Dental Clinic Redesign, 27010.4 Summary, 306

11 Practical Guide to Successful Development of Simulation-Based Six-Sigma Projects 307

11.1 Introduction, 30711.2 Characteristics of a 3S Application, 30811.3 Ingredients for a Successful 3S Program, 31511.4 Framework for Successful 3S Implementation, 31711.5 3S Project Charter, 32411.6 3S Software Tools, 326

APPENDIX A BASIC STATISTICS 343

APPENDIX B RANDOM NUMBERS 363

APPENDIX C AXIOMATIC DESIGN 367

APPENDIX D TAGUCHI’S QUALITY ENGINEERING 375

APPENDIX E PROCESS MAPPING 381

APPENDIX F VENDORS 387

REFERENCES AND FURTHER READING 395

INDEX 401

CONTENTS ix

PREFACE

Simulation modeling within the context of six-sigma and design for six-sigma(DFSS) methods is constantly getting more attentions from black belts, greenbelts, and other six-sigma deployment operatives, process engineers, leanexperts, and academics all over the world. This trend can easily be seen by theincreasing use of simulation tools in several successful six-sigma initiatives inmany Fortune 500 companies, coupled with the tremendous development insimulation software tools and applications.

For a six-sigma project, conducting experimental design and “what-if”analysis is a common key practice toward achieving significant results. Simu-lation models can be utilized effectively as a flexible platform for six-sigmaand DFSS experimentation and analyses, which reduces the time and cost ofphysical experimentation and provides a visual method to validate tested sce-narios. On the other hand, simulation studies often suffer from the unavail-ability of accurate input data and lack of a structured approach for conductinganalysis. The proven and widely used six-sigma and DFSS approaches providethe simulation study with reliable simulation data as input, an accurate processmap, and integrates the simulation process with a state-of-the-art set of processanalyses. Hence, coupling simulation modeling with a well-structured six-sigma process compensates for such limitations and bridges the gap betweenmodeling and process engineering. Such integration provides the synergy and infrastructure essential for successful problem solving and continuousimprovement in a wide spectrum of manufacturing and service applications.To develop an appreciation for the simulation-based six-sigma methodology,the subject of this book, we first review both six-sigma and simulationapproaches and then lay the background for their integration.

xi

SIX-SIGMA DEFINED

The success of six-sigma deployment in many industries has generated enor-mous interest in the business world. In demonstrating such successes, six-sigmacombines the power of teams and process. The power of teams implies orga-nizational support and trained teams tackling objectives.The power of processmeans effective six-sigma methodology deployment, risk mitigation, projectmanagement, and an array of statistical and system thinking methods. Six-sigma focuses on the whole quality of a business, which includes product orservice quality to external customers and operational quality of all internalprocesses, such as accounting and billing. A whole quality business with wholequality perspectives will not only provide high-quality products or services, butwill also operate at lower cost and higher efficiency, because all the businessprocesses are optimized.

In basic terms, six-sigma is a disciplined program or methodology forimproving quality in all aspects of a company’s products and services. In thisbook we adopt the DMAIC process over its five phases: define, measure, analy-sis, improve, and control. DMAIC represents that latest step in the evolutionof the total quality management movement begun by W. Edwards Deming inthe 1950s. The six-sigma initiative is credited to Mikel Harry, a statistician whois cofounder and a principal member of the Six Sigma Academy in Scottsdale,Arizona. Early corporate adopters of the program include Motorola in the1980s, and other technology-based firms, such as General Electric, TexasInstruments, and Allied Signal.

The central theme of six-sigma is that product and process quality can beimproved dramatically by understanding the relationships between the inputsto a product or process and the metrics that define the quality level of theproduct or process. Critical to these relationships is the concept of the voiceof the customer: that quality can be defined only by the customer who will ulti-mately receive the outputs or benefits of a product or process. In mathemati-cal terms, six-sigma seeks to define an array of transfer functions of thecritical-to-quality characteristics (CTQs) or Y’s, where Y = f(X1, X2, . . . , Xn)between the quality metrics of a product or process (e.g., Y = the % on-timedelivery for a fulfillment process) and the inputs that define and control theproduct or process (e.g., X1 = the number of resources available to service cus-tomers).The focus of six-sigma, then, is twofold: (1) to understand which inputs(X’s) have the greatest effect on the output metrics (Y’s), and (2) to controlthose inputs so that the outputs remain within a specified upper and/or lowerspecification limit.

In statistical terms, six-sigma quality means that for any given product orprocess quality measurement, there will be no more than 3.4 defects producedper 1 million opportunities (assuming a normal distribution). An opportunityis defined as any chance for nonconformance, not meeting the required spec-ifications. The goal of six-sigma is both to center the process and to reduce the

xii PREFACE

variation such that all observations of a CTQ measure are within the upperand lower limits of the specifications.

Defect-correction six-sigma methodologies are highlighted in the DMAICprocess; DFSS, a proactive methodology, is characterized by its four phases:identify, characterize, optimize, and verify. The DMAIC six-sigma objective isto improve a process without redesigning the current process. DFSS puts thefocus on design by doing things right the first time. The ultimate goal of DFSSis whole quality: do the right things, and do things right all the time, to achieveabsolute excellence in design whether it is a service process facing a customeror an internal business process facing an employee. Superior design will deliversuperior functions to generate great customer satisfaction. A DFSS entity willgenerate a process that delivers a service or product in the most efficient, eco-nomical, and flexible manner. Superior process design will generate a serviceprocess that exceeds customer wants and delivers these with quality and atlow cost. Superior business process design will generate the most efficient,effective, economical, and flexible business process. This is what we mean bywhole quality.

To ensure success, a six-sigma initiative must receive complete buy-in andcontinuous support from the highest level of a company’s leadership team. Inaddition, a rigorous training program and dedicated staff positions will requirethe best and brightest minds that can be allocated to the initiative.

SIMULATION DEFINED

Simulation, in general, is a disciplined process of building a model of an exist-ing or proposed real system and performing experiments with this model toanalyze and understand the behavior of selected characteristics of a realsystem so as to evaluate various operational strategies to manage the realsystem. In abstract terms, simulation is used to describe the behavior of phys-ical and business systems, to construct hypotheses or theories to explain behav-iors, to predict the behavior of future systems, and to perform what-if scenariosfor existing and proposed design alternatives.

In this book we are concerned only with stimulating processes of transac-tional nature where events can be discretely isolated and described over time.Such simulation is called discrete event simulation (DES).There are many ben-efits for simulation in a six-sigma context. Specifically, DES simulation can beused to:

• Study DMAIC/DFSS solutions before piloting or modification• Manage risk by preventing defects and reducing costs• Prevent or eliminate unforeseen barriers, bottlenecks, and design flaws• Allocate necessary resources to assure stakeholder satisfaction, includ-

ing customers

PREFACE xiii

• Build predictable, controllable, and optimal designs and solutions• Facilitate communication among multifunctional team members

Discrete event simulation has to be designed and performed in a system-atic fashion (Part II of the book). A model is an approximation of part of areal system. Model building is an important step, necessary but not sufficient.That is, a model is not simulation in itself. Simulation is a process. It is impos-sible to replicate all of reality in a simulation process. Only a handful ofselected characteristics can be adequately modeled and analyzed to providemeaningful results. This is usually carried over several revisions involvingexperimentation, modification, analysis, and interpretation, as shown in Figure P.1.

Simulation can be used throughout the DFSS process, but especially in thecharacterize and optimize phases. In these phases, simulation is used to:

• Determine if proposed solutions meet CTQs and functional requirements• Evaluate how high-level design (structure) and inputs will affect overall

process output and performance• Compare the performance of high-level design alternatives• Assemble the behavior of subelements to predict overall performance• Perform sensitivity analysis to study the importance of subelements• Redesign the product and service as needed• Assess the risk of failure of subelements to provide system integrity• Compare the performance of detailed design alternatives

SIMULATION-BASED SIX-SIGMA

Over the last two decades, there has been a significant penetration of problemsolving and continuous improvement using simulation modeling and six-sigma

xiv PREFACE

Real SystemExisting or ProposedProduct or Process

Real SystemManaged or Modified

Understand Understand Understand

Model Rev 1 Model Rev 2 Model Rev n

Experiment Analyze Experiment Analyze Experiment Analyze

Figure P.1 Simulation high-level process.

initiatives. Those approaches include primarily the design of experimentmethod (DOE), Taguchi methods, and simulation-based optimizationmethods. DFSS is a very important element of a full six-sigma deploymentprogram, since design is the one of the important activities in which quality,cost, and cycle time can be greatly improved if the right strategy and method-ologies are used. Major corporations are training many design engineers andproject managers to become six-sigma green belts, black belts, and masterblack belts, enabling them to play a leadership role in corporate operation andprocess excellence. DES has become common among engineers, designers, andbusiness planners. With the aid of DES, companies have been able to designefficient production and business systems, validate and trade off proposeddesign solution alternatives, troubleshoot potential problems, and improvesystem performance metrics. That aid usually enables companies to cut costand meet targets while boosting sales and profit.

OBJECTIVES OF THE BOOK

The objectives of the book are:

1. To provide a good reference for six-sigma and simulation processes inthe same book.

2. To provide in-depth and clear coverage of simulation-based six-sigmaand our terminology for six-sigma and simulation integration (Part III).

3. To illustrate clearly complete road maps for both simulation-based leansix-sigma and simulation-based DFSS projects.

4. To present the know-how for all of the principal methods used in simulation-based six-sigma approaches and to discuss the fundamentalsand background of each process clearly (Parts I and II). Case studies areprovided with detailed step-by-step implementation process of eachmethod described in Part III.

5. To help develop readers’ practical skills in applying simulation-based six-sigma in transactional environments (i.e., project execution and opera-tional and technical aspects).

BACKGROUND NEEDED

The background required to study this book is some familiarity with simplestatistical methods, such as normal distribution, mean, and variance, and with simple data analysis techniques. A background in DES theory is alsohelpful.

PREFACE xv

SUMMARY OF CHAPTER CONTENTS

The book is organized into three parts:

• Part I, Chapters 1, 2, and 3, has six-sigma fundamentals as its theme.• Part II, Chapters 4, 5, 6, and 7, covers simulation fundamentals.• Part III, begins in Chapters 8, 9, and 10 by focusing on simulation-based

lean six-sigma (3S-LSS) and design for six-sigma (3S-DFSS) and providesseveral case studies. In Chapter 11 we present elements of successfuldeployment of 3S methods.

Following is a summary of the chapters.

Part I

In Chapter 1 we introduce the concepts of process and customer satisfactionand highlight how customers experience a product or a service as well as itsdelivery process. This chapter concentrates on the service side and definesvarious elements of a generic process as well as tools such as process mapping.We also demystify the concept of transaction and event discreteness. In thischapter we explain what six-sigma is and how it has evolved over time. Weexplain that it is a process-based methodology and introduce the reader toprocess modeling with a high-level overview of process mapping, value streammapping and value analysis as well as business process management systems.The criticality and application of measurement systems analysis is introduced.The DMAIC methodology and how it incorporates these concepts into a roadmap method is also explained.

Chapter 2 covers the lean six sigma (LSS) concept and discusses topicsrelated to the integration of six-sigma and lean manufacturing. The focus inthis chapter is on the details of the LSS approach, the enhancements made tosix-sigma DMAIC tollgates, lean manufacturing concepts and aims, valuestream mapping, and lean manufacturing techniques. The chapter also high-lights the synergy and benefits of implementing an LSS system as a founda-tion for the proposed 3S-LSS approach.

Chapter 3 is an introduction to a high-level DFSS process. The DFSSapproach introduced helps design teams frame their project with financial, cul-tural, and strategic implications to the business. In this chapter we form andintegrate several strategic, tactical, and synergistic methodologies to enhanceservice DFSS capabilities and to deliver a broad set of optimized solutions.We highlights and present the service DFSS phases: identify, characterize,optimize, and verify.

Part II

In Chapter 4 we introduce the basic concepts of simulation modeling with afocus on process modeling and time-based performance measurement.We also

xvi PREFACE

clarify the role of simulation studies in serving the increasing needs of com-panies that seek continuous improvement and optimality in production andbusiness processes. To this end, we provide an introduction to the concept, ter-minology, and types of models, along with a description of simulation taxon-omy and a justification for utilizing simulation tools in a variety of real-worldapplications. Such a background is essential to establishing a basic under-standing of what simulation is all about and to understanding the key simula-tion role in simulation-based six-sigma studies and applications.

In Chapter 5 we present the details and mechanics of DES that are essen-tial to providing a flexible platform for use of a simulation-based six-sigmaapproach: elements of system modeling, events activation, random numbergeneration, time advancement, animation, and accumulating statistics. In addi-tion to powerful DES mechanics, we address how fast computations on today’shigh-speed processors, along with the growing graphics capability, contributeto the effectiveness and visualization capability of DES models. We thusprovide a deeper understanding of the DES process, components, and mech-anisms. Examples of manual and computer simulations are used to clarify theDES functionality.

Chapter 6 is focused on analyzing the various aspects of the simulationprocess and the set of techniques and steps followed when conducting a sim-ulation study.The elements of the simulation process discussed include projectscoping, conceptual modeling, data collection, model building, model analyses,and documentation. The chapter also highlights the linkage of these simula-tion practices to the overall scope of a six-sigma project, to the simulation soft-ware used, and to the process followed by a six-sigma team in carrying out acomplete simulation study. Finally, we present the implications of simulationon six-sigma applications to system design, problem solving, and continuousimprovement.

In Chapter 7 we discuss the main issues involved in analyzing simulationoutputs: distinguishing between terminating and steady-state simulation,understanding the stochastic nature of simulation outcomes, determining simulation run controls (i.e., warm-up period, run length, and number of repli-cations), and selecting the appropriate model output analysis method. We alsodiscuss the main methods of output analysis, including statistical analysis,experimental design, and optimization.

Part III

Chapter 8 contains all the project road maps for the 3S and 3S-LSS method-ologies. The DMAIC process and lean principles and concepts are integratedinto a synergetic road map in a 3S-LSS environment. In Chapter 8 we alsopresent a 3S-DFSS project road map, which highlights at a high level the iden-tify, charcaterize, optimize, and validate phases over the seven developmentstages: idea creation, voice of the customer and business, concept develop-ment, preliminary design, design optimization, verification, and launch

PREFACE xvii

readiness. In this chapter the concept of tollgate is introduced. We also high-light the most appropriate DFSS tools and methods by the DFSS phase, indi-cating where it is most appropriate to start tool use. The road maps alsorecognize the concept of tollgates, design milestones where DFSS teamsupdate stakeholders on development and ask that a decision be made as towhether to approve going to the next stage, recycling back to an earlier stage,or canceling the project altogether.

In Chapter 9 we discuss the details of 3S-LSS application to real-worldsystems using the road map presented in Chapter 8. The focus is on the prac-tical aspects of 3S-LSS use in reengineering transactional processes in bothservices and manufacturing. These aspects are discussed through an appli-cation of the 3S-LSS approach to a manufacturing case study. Emphasis is placed on utilizing simulation-based application of lean techniques and six-sigma analysis to improve a set of process CTQs, defined in terms of time-based performance to take advantage of simulation modeling for asystem-level application of six-sigma statistical tools used in DMAIC and leantechniques.

In Chapter 10 we develop a 3S-DFSS clinic case study using the road mapin Chapter 8. We show the application of several tools as they span over theproject road map. The case study also highlights most appropriate DFSS tools.It indicates where it is most appropriate to start tool use such as transfer func-tions and quality function deployment. Following the DFSS road map helpsaccelerate the process introduction and aligns benefits for customers andstakeholders.

Chapter 11 is a practical guide to successful 3S-DFSS and 3S-LSS devel-opment, deployment, and project execution. A generic framework for suc-cessful project management and development is proposed based on theauthors’ experience. This framework demystifies the ambiguity involved inproject selection and specifies a method for project development. We providea practical guide for successful development of 3S projects based on the roadmaps discussed in Chapter 8. The guide begins by discussing the unique char-acteristics of 3S projects.

WHAT DISTINGUISHES THIS BOOK FROM OTHERS

Several six-sigma books are available on the market for both students andpractitioners, as are several simulation books. We believe that none of thesebooks integrates both methodologies to achieve the benefits outlined herein.This book constitutes an integrated problem-solving and continuous improve-ment approach based on six-sigma thinking, tools, and philosophy, togetherwith simulation modeling flexibility and visualization. The book also includesa review of six-sigma process fundamentals (Part I), a detailed description ofsimulation process fundamentals (Part II), and a presentation of simulation-based six-sigma methodology (Part III).

xviii PREFACE

The uniqueness of the book lies in bringing six-sigma and simulation modeling processes under the umbrella of problem solving and continuousimprovement in product and service development, management, and planning.The book will not only be helpful to simulation professionals, but will also helpsix-sigma operatives, design engineers, project engineers, and middle-levelmanagers to gain fundamental knowledge of six-sigma and simulation. Afterreading this book, readers will have a round grasp of the body of knowledgein both areas.

The book is the first book to cover completely all of “the body of knowl-edge of six-sigma and design for six sigma with simulation methods” outlinedby the American Society for Quality including process mapping, the design ofexperiment method, quality function deployment, and failure mode effectanalysis, among many other methods.

In the book, both simulation and contemporary six-sigma and DFSSmethods are explained in detail together with practical case studies that helpdescribe the key features of simulation-based methods. The systems approachto designing products and services, as well as problem solving, is integratedinto the methods discussed. Readers are given the project life-cycle know-howfor a project so that the method is understandable not only from a usabilityperspective (how it is used) but also from an implementation perspective(when it is used).

This book is the best way to learn about all the useful tools in six-sigma andDFSS, because it will be the only book that covers the body of knowledge usedin a simulation environment. Six-sigma now dominates business decisionmaking at all major U.S. and many international companies for both businesssurvival and total quality improvement. The book supplements traditionaltechniques with emerging methods.

ACKNOWLEDGMENTS

In preparing this book we received advice and encouragement from severalpeople. For this we thank Pearse Johnston, Mike O’Ship, Joe Smith, OnurUlgen, and Steve Beeler. We also appreciate the help of many others, espe-cially the faculty and students of the industrial engineering department atJordan University of Science & Technology. We are also very grateful for theassistance of George Telecki and Rachel Witmer of John Wiley & Sons, Inc.

CONTACTING THE AUTHORS

Your comments and suggestions related to this book will be greatly ap-preciated and we will give them serious consideration for inclusion in futureeditions. We also conduct public and in-house six-sigma, DFSS, and simulation-based six-sigma workshops and provide consulting services.

PREFACE xix

Dr. Basem El-Haik can be reached by e-mail at:

basemhaik@Six-SigmaPro.com or basemhaik@gmail.com(734) 765-5229

Dr. Raid Al-Aomar can be reached by e-mail at Jordan University ofScience & Technology:

ralaomar@just.edu.jo

xx PREFACE

PART I

SIX-SIGMA FUNDAMENTALS

1

1SIX-SIGMA FUNDAMENTALS

1.1 INTRODUCTION

Throughout the evolution of quality control, there has always been a prepon-derance of manufacturing (parts) focus. In recent years more attention hasbeen placed on process in general; however, the application of a full suite oftools to transaction-based industry is rare and still considered risky or chal-lenging. Only companies that have mature six-sigma1 deployment programssee the application of design for six sigma (DFSS) to processes as an invest-ment rather than a needless expense. Even those companies that embark onDFSS for processes seem to struggle with confusion over DFSS “process” andthe process being designed.

Many business processes can benefit from DFSS, some of which are listedin Table 1.1. If measured properly, we would find that few if any of theseprocesses perform at six-sigma performance levels. The cost per transaction,timeliness, or quality (accuracy, completeness) are never where they should beand hardly world class.

3

Simulation-Based Lean Six-Sigma and Design for Six-Sigma, by Basem El-Haik and Raid Al-AomarCopyright © 2006 John Wiley & Sons, Inc.

1 The word sigma refers to the lowercase Greek letter σ, a symbol used by statisticians to measurevariability. As the numerical values of σ increase, the number of defects in a process falls expo-nentially. Six-sigma design is the ultimate goal since it means that if the same task is performed1 million times, there will be only 3.4 defects, assuming normality.

A transaction is typically something that we create to serve a paying cus-tomer. Transactions are event-based and discrete in nature. (See Chapter 5 formore details.) Customers may be internal or external; if external, the term consumer (or end user) will be used for clarification purposes. Some services(e.g., dry cleaning) consist of a single process, whereas many services consistof several processes linked together. At each process, transactions occur. Atransaction is the simplest process step and typically consists of an input,procedures, resources, and a resulting output. The resources can be people ormachines, and the procedures can be written, learned, or even digitized in soft-ware code. It is important to understand that some services are enablers ofother services, while others provide their output to the end customer. Forexample, transactions centered around the principal activities of an order-entry environment include transactions such as entering and delivering orders,recording payments, checking the status of orders, and monitoring the stocklevels at a warehouse. Processes may involve a mix of concurrent transactionsof different types and complexity either executed online or queued fordeferred execution.

We experience services spanning the range from ad hoc to designed. Ourexperience indicates that the vast majority of services are ad hoc and have noassociated metrics; many consist solely of a person with a goal and objectives.These services have a large variation in their perceived quality and are verydifficult to improve. It is akin to building a house on a poor foundation.

Services affect almost every aspect of our lives. There are services such asrestaurants, health care, financial, transportation, entertainment, and hospital-ity, and they all have the same elements in common. Services and transaction-based processes can be modeled, analyzed, and improved using discrete eventsimulation (DES) – hence this book.

In this chapter we cover an overview of six-sigma and its development aswell as the traditional deployment for process or product improvement andits components [DMAIC (define–measure–analyze–improve–control)]. TheDMAIC platform is also discussed in Chapter 2, which complements thischapter and covers topics related to the integration of six-sigma and lean man-ufacturing. The focus in this chapter is on the fundamental concepts of six-sigma DMAIC methodology, value stream mapping and lean manufacturing

4 SIX-SIGMA FUNDAMENTALS

TABLE 1.1 Examples of Organizational Functions

Marketing Sales Human DesignBrand Discovery Resources Change control

management Account Staffing New productProspect management Training

Production Control Sourcing Information FinanceInventory control Commodity Technology Accounts payableScheduling Purchasing Help desk Accounts receivable

Training

techniques, and the synergy and benefits of implementing a lean six sigma(LSS) system. LSS represents the foundation for the proposed approach of3S-LSS (simulation-based LSS). Chapter 9 provides a detailed case study ofthe 3S-LSS approach.We introduce design for six sigma in Chapter 3, togetherwith a detailed simulation-based case study in Chapter 10.

1.2 QUALITY AND SIX-SIGMA DEFINED

We all use services and interact with processes every day. When was the lasttime you remember feeling really good about a transaction or service? Whatabout the last poor service you received? It is usually easier to rememberpainful and dissatisfying experiences than it is to remember the good ones.One of the authors recalls sending a first-class registered letter that he couldnot be sure if it had been received yet eight business days later, so he calledthe postal service provider’s toll-free number and had a very professional andcaring experience. It is a shame the USPS couldn’t perform the same level ofservice in delivering the letter. Actually, the letter had been delivered but thesystem failed to track it. So the question is: How do we measure quality for atransaction or service?

In a traditional manufacturing environment, conformance to specificationsand delivery are the common items related to quality that are measured andtracked. Often, lots are rejected because they don’t have the correct support-ing documentation. Quality in manufacturing, then, is conforming product,delivered on time, having all of the supporting documentation. In services,quality is measured as conformance to expectations, availability, experience ofthe process, and people interacting with the service delivery.

If we look at Figure 1.1, we can see that customers experience three aspectsof service: (1) the specific service or product has attributes such as availabil-ity, being what was wanted, and working; (2) the process through which theservice is delivered can be ease of use or value added; (3) the people (orsystem) should be knowledgeable and friendly. To fulfill these needs there isa service life cycle to which we apply a quality operating system.

Six-sigma is a philosophy, a measure, and a methodology that provides busi-nesses with a perspective and tools to achieve new levels of performance inboth services and products. In six-sigma, the focus is on process improvementto increase capability and reduce variation. The few vital inputs are chosenfrom the entire system of controllable and noise variables,2 and the focus ofimprovement is on controlling these inputs.

Six-sigma as a philosophy helps companies achieve very low defects permillion opportunities over long-term exposure. Six-sigma as a measure gives us a statistical scale to measure progress and benchmark other

QUALITY AND SIX-SIGMA DEFINED 5

2 Noise factors are factors that cannot be controlled or can be controlled at an unaffordable cost,such as randomness and variability factors.

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companies, processes, or products. The defect-per-million opportunities(DPMO) measurement scale ranges from zero to 1 million, while the realisticsigma scale ranges from zero to six. The methodologies used in six-sigma,which we discuss in more detail in subsequent chapters, build on all of the toolsthat have evolved to date but put them into a data-driven framework. Thisframework of tools allows companies to achieve the lowest defects per millionopportunities possible.

Six-sigma evolved from total quality management early efforts as discussedin El-Haik and Roy (2005). Motorola initiated the movement and it spread toAsea Brown Boveri, Texas Instruments Missile Division, and Allied Signal. Itwas at this juncture that Jack Welch became aware of the power of six-sigmafrom Larry Bossidy, and in the nature of a fast follower, committed GeneralElectric to embracing the movement. It was GE that bridged the gap betweena simple manufacturing process and product focus and what were first calledtransactional processes, and later, commercial processes. One of the reasonsthat Welch was so interested in this program was that an employee survey hadjust been completed and it revealed that the top-level managers of thecompany believed that GE had invented quality (after all, Armand Feigen-baum worked at GE); however, the vast majority of employees didn’t thinkGE could spell “quality.” Six-sigma turned out to be the methodology toaccomplish Crosby’s goal of zero defects. By understanding what the keyprocess input variables are and that variation and shift can occur, we can createcontrols that maintain six-sigma (6σ) performance on any product or serviceand in any process. The Greek letter σ is used by statisticians to indicate thestandard deviation (a statistical parameter) of the population of interest.Before we can clearly understand the process inputs and outputs, we need tounderstand process modeling.

1.3 INTRODUCTION TO PROCESS MODELING

Six-sigma is a process-focused approach to achieving new levels of perform-ance throughout any business or organization. We need to focus on a processas a system of inputs, activities, and output(s) in order to provide a holisticapproach to all the factors and the way they interact to create value or waste.When used in a productive manner, many products and services are alsoprocesses. An automated teller machine takes your account information, per-sonal identification number, energy, and money and processes a transactionthat dispenses funds or an account rebalance. A computer can take keystrokeinputs, energy, and software to process bits into a word document. At the sim-plest level the process model can be represented by a process diagram, oftencalled an IPO (input–process–output) diagram (Figure 1.2).

If we take the IPO concept and extend the ends to include the suppliers ofthe inputs and the customers of the outputs, we have the SIPOC(supplier–input–process–output–customer) (Figure 1.3). This is a very

INTRODUCTION TO PROCESS MODELING 7

effective tool in gathering information and modeling any process. A SIPOCtool can take the form of a table with a column for each category in the name.

1.3.1 Process Mapping

Whereas the SIPOC is a linear flow of steps, process mapping is a tool of dis-playing the relationship between process steps and allows for the display ofvarious aspects of the process, including delays, decisions, measurements, andrework and decision loops. Process mapping builds on the SIPOC informationby using standard symbols to depict varying aspects of the processes flowlinked together with lines that include arrows demonstrating the direction offlow.

8 SIX-SIGMA FUNDAMENTALS

Process

Inputs Outputs

Materials

Procedures

Methods

Information

Energy

People

Skills

Knowledge

Training

Facilities/Equipment

Service

Figure 1.2 IPO diagram.

Inputs InputCharacteristic

Process Outputs OutputCharacteristic

Suppliers Customers

1. What is the process?

2a. What is the start of theprocess?

2b. What is the end of theprocess?

3. What are the outputsof the process?

4. Who are the customersof the outputs?

5. What are the characteris-tics of theoutputs?

7. Who are the suppliersof the inputs?

8. What are the characteris-tics of theinputs?

6. What are the inputs of theprocess?

Figure 1.3 SIPOC table.