Process Simulation Using WITNESS

Teaches basic and advanced modeling and simulation techniques to both undergraduate and postgraduate students and serves as a practical guide and manual for professionals learning how to build simulation models using WITNESS, a free-standing software package.

This book discusses the theory behind simulation and demonstrates how to build simulation models with WITNESS. The book begins with an explanation of the concepts of simulation modeling and a ?guided tour? of the WITNESS modeling environment. Next, the authors cover the basics of building simulation models using WITNESS and modeling of material-handling systems. After taking a brief tour in basic probability and statistics, simulation model input analysis is then examined in detail, including the importance and techniques of fitting closed-form distributions to observed data. Next, the authors present simulation output analysis including determining run controls and statistical analysis of simulation outputs and show how to use these techniques and others to undertake simulation model verification and validation. Effective techniques for managing a simulation project are analyzed, and case studies exemplifying the use of simulation in manufacturing and services are covered. Simulation-based optimization methods and the use of simulation to build and enhance lean systems are then discussed. Finally, the authors examine the interrelationships and synergy between simulation and Six Sigma.

• Emphasizes real-world applications of simulation modeling in both services and manufacturing sectors
• Discusses the role of simulation in Six Sigma projects and Lean Systems
• Contains examples in each chapter on the methods and concepts presented

 Process Simulation Using WITNESS is a resource for students, researchers, engineers, management consultants, and simulation trainers.

About the Companion Website xvii

Preface xix

Acknowledgments xxiii

1 Concepts of Simulation Modeling 1

1.1 Overview 1

1.2 System Modeling 2

1.2.1 System Concept 2

1.2.2 Modeling Concept 4

1.2.3 Types of Models 5

1.3 Simulation Modeling 11

1.3.1 Simulation Defined 11

1.3.2 Simulation Taxonomy 12

1.4 The Role of Simulation 15

1.4.1 Simulation Justified 15

1.4.2 Simulation Applications 16

1.4.3 Simulation Precautions 17

1.5 Simulation Methodology 20

1.5.1 Identify Problem/Opportunity 20

1.5.2 Develop Solution/Improvement Alternatives 21

1.5.3 Evaluate Solution Alternatives 21

1.5.4 Select the Best Alternative 22

1.5.5 Implement the Selected Alternative 22

1.6 Steps in a Simulation Study 22

1.6.1 Problem Formulation 23

1.6.2 Setting Study Objectives 23

1.6.3 Conceptual Modeling 25

1.6.4 Data Collection 26

1.6.5 Model Building 27

1.6.6 Model Verification 30

1.6.7 Model Validation 30

1.6.8 Model Analysis 31

1.6.9 Study Documentation 32

1.7 Simulation Software 34

1.7.1 WITNESS® Simulation Software 35

1.8 Summary 36

Questions and Exercises 37

Bibliography 38

2 World-Views of Simulation 41

2.1 Overview 41

2.2 System Modeling with DES 42

2.2.1 System Structure 42

2.2.2 System Layout 43

2.2.3 System Data 43

2.2.4 System Logic 44

2.2.5 System Statistics 45

2.3 Elements of Discrete Event Simulation (DES) 45

2.3.1 System Entities (EN) 45

2.3.2 System State (S) 46

2.3.3 State Variables (VR) 46

2.3.4 System Events (E) 47

2.3.5 System Activities (A) 48

2.3.6 System Resources (R) 48

2.3.7 System Delay (D) 50

2.3.8 System Logic (L) 50

2.4 DES Functionality 51

2.4.1 Discrete-Event Mechanism 52

2.4.2 Time-Advancement Mechanism 54

2.4.3 Random Sampling Mechanism 55

2.4.4 Statistical Accumulation Mechanism 58

2.4.5 Animation Mechanism 59

2.5 Example of DES Mechanisms 60

2.6 Monte Carlo Simulation (MCS) 65

2.7 Continuous Simulation 68

2.7.1 WITNESS® for Continuous Simulation 69

2.7.2 Hybrid Simulation 69

2.8 WITNESS® World-views of Simulation 70

2.8.1 Attribute 72

2.8.2 Buffer 72

2.8.3 Carrier 72

2.8.4 Conveyor 73

2.8.5 Fluid 73

2.8.6 Labor 74

2.8.7 Machine 74

2.8.8 Part 75

2.8.9 Path 75

2.8.10 Pipe 75

2.8.11 Processor 75

2.8.12 Sections 75

2.8.13 Station 76

2.8.14 Tank 76

2.8.15 Track 76

2.8.16 Vehicle 76

2.9 Summary 77

Questions and Exercises 78

Bibliography 80

3 WITNESS® Environment 83

3.1 Overview 83

3.2 The WITNESS® Environment 83

3.3 Menus 85

3.3.1 General Menu Operation 86

3.4 Tool Bars 86

3.4.1 Standard Tool Bar 86

3.4.2 Views Toolbar 87

3.4.3 Element Tool Bar 89

3.4.4 Model Tool Bar, 92

3.4.5 Assistant Toolbar, 92

3.4.6 Run Toolbar, 93

3.4.7 Reporting Toolbar, 95

3.4.8 Display Edit Toolbar, 96

3.4.9 Creating a New Toolbar, 99

3.5 Dialog Boxes and Property Sheets 100

3.5.1 Entry/Field Types 100

3.6 Windows 102

3.7 Layers 103

3.8 The WITNESS® Editor 103

3.8.1 Editor Features 103

3.8.2 Manipulating a Window 105

3.9 Window Operations 105

3.9.1 Windows Options 105

3.9.2 The Interact Box 106

3.9.3 The Clock (Time) 107

3.9.4 The Analog Clock 107

3.9.5 Copying, Cutting, and Pasting 107

3.9.6 Copy and Cut Element’s Display or Detail Features 108

3.10 The Help Facility 108

3.11 The Basic Elements 109

Questions and Exercises 109

Bibliography 110

4 Basic WITNESS® Modeling Techniques 111

4.1 Overview 111

4.2 Step-by-Step Model Building 111

4.3 Modeling a Simple Manufacturing Process 112

4.3.1 Define: Specifying Elements of the Manufacturing

Process Simulation Model 114

4.3.2 Detail: Adding Specifications for Elements to the Model 114

4.3.3 Display: Modifying the Appearance of Elements in the Layout Window 118

4.4 Modeling a Service Process 126

4.4.1 Service Model Example 126

4.5 WITNESS® Code 141

4.6 An Extended Example 141

Questions and Exercises 143

Bibliography 146

5 Modeling Material Handling Systems 149

5.1 Overview 149

5.2 Material Handling Systems 149

5.3 Material Handling Systems in WITNESS® 150

5.4 Modeling Conveyors 152

5.5 Modeling Paths for Labor and Parts Transit 156

5.6 Modeling Vehicles and Tracks 161

5.7 Modeling Power-&-Free Systems 167

Questions and Exercises 176

Bibliography 176

6 Basic Probability and Statistics for Simulation 179

6.1 Overview 179

6.2 Random Variables (RVs) 179

6.2.1 Examples of Discrete Random Variables 180

6.2.2 Examples of Continuous Random Variables 181

6.3 Point Estimation 182

6.4 Confidence Intervals for the Population Mean 182

6.5 Confidence Intervals for the Population Variance and Standard Deviation 184

6.6 Sample Size Determination when Estimating Population Mean 185

6.7 Theoretical Probability Distributions 186

6.7.1 The Uniform Distribution 187

6.7.2 The Normal Distribution 187

6.7.3 The Exponential Distribution 190

6.7.4 The Erlang Distribution 190

6.7.5 The Gamma Distribution 192

6.7.6 The Weibull Distribution 193

6.7.7 Triangular Distribution 193

Questions and Exercises 197

Bibliography 198

7 Simulation Input Modeling 199

7.1 Overview 199

7.2 Determining Data Requirements 200

7.3 Methods of Data Collection 202

7.4 Representing Collected Data 211

7.5 Validating Collected Data 213

7.5.1 Filtering the Data from Outliers and Wrong Measures 215

7.5.2 Testing the Data for Independence 215

7.5.3 Testing if Data are Identically Distributed 218

7.6 Fitting Probability Distributions to Collected Data 219

7.6.1 Using Empirical Distributions 225

7.7 WITNESS® Input Modeling 226

7.7.1 WITNESS® RNG 227

7.7.2 Incorporating Collected Data in WITNESS® 229

7.7.3 Using Databases with WITNESS® 233

7.8 Practical Aspects of Input Modeling 234

7.8.1 Example of Input Modeling: Auto Service Center 236

7.8.2 Example of Input Modeling: ER Simulation 243

7.9 Summary 249

Questions and Exercises 249

Bibliography 252

8 Simulation Output Analysis 253

8.1 Overview 253

8.2 Terminating Versus Steady-State Simulation 254

8.2.1 Terminating Simulation 254

8.2.2 Steady-State Simulation 257

8.3 Determining Simulation Run Controls 259

8.3.1 Determining Warm-Up Period 260

8.3.2 Determining Simulation Run Length 263

8.3.3 Determining the Number of Simulation Runs 266

8.4 Variability in Simulation Outputs 267

8.4.1 Variance Reduction Techniques 269

8.5 Simulation Output Analysis 270

8.5.1 Statistical Analysis of Simulation Outputs 272

8.5.2 Experimental Design 285

8.6 Example: Output Analyses of a Clinic Simulation 291

8.7 WITNESS® Modules for Simulation Output Analysis 296

8.7.1 WITNESS® Outputs and Charts 296

8.7.2 WITNESS® Costing 297

8.7.3 WITNESS® Scenario Manager 299

8.7.4 WITNESS® Documentor 299

8.7.5 WITNESS® Optimizer 300

8.8 Summary 300

Questions and Exercises 301

Bibliography 303

9 Model Verification and Validation Techniques 305

9.1 Overview 305

9.2 Model Verification Techniques 306

9.2.1 Verifying Model Inputs 308

9.2.2 Verifying Model Logic 309

9.2.3 Verifying Model Outputs 314

9.3 Model Validation Techniques 314

9.3.1 Validating Model Inputs 316

9.3.2 Validating Model Behavior 318

9.3.3 Validating Model Outputs 319

9.4 Verifying WITNESS® Models 320

9.5 Summary 330

Question and Exercise 330

Bibliography 332

10 Simulation Project Management 331

10.1 Overview 331

10.2 Define the Problem 332

10.2.1 Define the Objectives of the Study 332

10.2.2 List the Specific Issues to Be Addressed 334

10.2.3 Determine the Boundary or Domain of the Study 334

10.2.4 Determine the Level of Detail or Proper Abstraction Level 334

10.2.5 Determine if a Simulation Model is Actually Needed 335

10.2.6 Estimate the Required Resources Needed to Do the Study 335

10.2.7 Perform a Cost-Benefit Analysis 335

10.2.8 Create a Planning Chart of the Proposed Project 336

10.2.9 Write a Formal Proposal 336

10.3 Design the Study 337

10.3.1 Estimate the Life Cycle of the Model 338

10.3.2 List Broad Assumptions 338

10.3.3 Estimate the Number of Models Required 338

10.3.4 Determine the Animation Requirements 338

10.3.5 Select the Tool 339

10.3.6 Determine the Level of Data Available and What Data is Needed 339

10.3.7 Determine the Human Requirements and Skill Levels 339

10.3.8 Determine the Audience (Levels of Management) 340

10.3.9 Identify the Deliverables 340

10.3.10 Determine the Priority of the Study in Relationship to Other Studies 340

10.3.11 Set Milestone Dates 341

10.3.12 Write the Project Functional Specifications 341

10.4 Design the Conceptual Model 341

10.4.1 Decide on Continuous, Discrete, or Combined Modeling 342

10.4.2 Determine the Elements that Drive the System 342

10.4.3 Determine the Entities that Should Represent the System Elements 343

10.4.4 Determine the Level of Detail Needed to Describe the System Components 343

10.4.5 Determine the Graphics Requirements of the Model 343

10.4.6 Identify the Areas That Utilize Special Control Logic 344

10.4.7 Determine How to Collect Statistics in the Model and Communicate Results to the Customer 344

10.5 Formulate Inputs, Assumptions, and Process Definition 344

10.5.1 Specify the Operating Philosophy of the System 345

10.5.2 Describe the Physical Constraints of the System 345

10.5.3 Describe the Creation and Termination of Dynamic Elements 345

10.5.4 Describe the Process in Detail 345

10.5.5 Obtain the Operation Specifications 346

10.5.6 Obtain the Material Handling Specifications 346

10.5.7 List All the Assumptions 346

10.5.8 Analyze the Input Data 346

10.5.9 Specify the Runtime Parameters 347

10.5.10 Write the Detailed Project Functional Specifications 347

10.5.11 Validate the Conceptual Model 347

10.6 Build, Verify, and Validate the Model 348

10.7 Experiment with the Model 348

10.8 Documentation and Presentation 349

10.8.1 Project Book 350

10.8.2 Documentation of Model Input, Code, and Output 350

10.8.3 Project Functional Specifications 350

10.8.4 User Manual 350

10.8.5 Maintenance Manual 351

10.8.6 Discussion and Explanation of Model Results 351

10.8.7 Recommendations for Further Areas of Study 351

10.8.8 Final Project Report and Presentation 351

10.9 Define the Model Life Cycle 352

10.9.1 Construct User-Friendly Model Input and Output Interfaces 353

10.9.2 Determine Model and Training Responsibility 353

10.9.3 Establish Data Integrity and Collection Procedures 354

10.9.4 Perform Field Data Validation Tests 354

10.10 Summary 354

Bibliography 354

11 Manufacturing Simulation Case Studies 357

11.1 Overview 357

11.2 Hybrid Simulation of Titanium Manufacturing Process 358

11.2.1 Model Description 358

11.2.2 Model Assumptions 360

11.2.3 Process Logic 360

11.2.4 Start-up Conditions and Model Run Length 361

11.2.5 Model Input Data 361

11.2.6 Model Outputs 363

11.2.7 The WITNESS® Model 363

11.2.8 Model Verification and Validation 366

11.2.9 Model Experiments 367

11.2.10 Project Results and Conclusions 371

11.3 Paint Capacity Study of an Aviation Company 373

11.3.1 Paint Shop Layout 373

11.3.2 Study Assumptions 373

11.3.3 Data Collection 375

11.3.4 The WITNESS® Model 375

11.3.5 Study Results 375

11.3.6 Throughput Improvement Opportunities 375

11.4 Simulation of a Seamless Pipe Facility 376

11.4.1 Study Objectives Include 377

11.4.2 System Description 379

11.4.3 Input Parameters 379

11.4.4 Schedule Data 381

11.4.5 The WITNESS® Model 381

11.4.6 Base Model–Worst-Case Schedule 381

11.4.7 Results Summary 387

11.4.8 Observations Summary 389

11.4.9 Conclusions 393

11.5 Summary 393

Bibliography 393

12 Service Simulation Case Studies 395

12.1 Overview 395

12.2 Elements of Service Systems 396

12.2.1 System Entities 396

12.2.2 Service Providers 396

12.2.3 Customer Service 397

12.2.4 Staff and Human Resources 397

12.2.5 Facility Layout and Physical Structure 397

12.2.6 Operating Policies 398

12.3 Characteristics of Service Systems 398

12.4 Modeling Service Systems 399

12.4.1 Modeling Considerations 399

12.4.2 Model Elements 401

12.4.3 Model Control Factors 401

12.4.4 Model Performance Measures 402

12.5 Applications of Service System Simulation 402

12.5.1 Examples of Service Systems Simulation 403

12.6 Case Studies on Service Systems Simulation 404

12.6.1 Car Wash 404

12.6.2 Harbor Traffic Simulation 406

12.6.3 Bank Simulation Example 409

12.6.4 Clinic Simulation Example 411

12.6.5 Public Service Office Simulation 417

12.7 Summary 423

Bibliography 423

13 Simulation-Based Optimization Methods 425

13.1 Overview 425

13.2 Optimization Approaches in Simulation Studies 426

13.3 Simulation-Based Optimization 427

13.4 WITNESS® Experimenter 429

13.4.1 Comparison of Multiple Alternatives with WITNESS® Experimenter 429

13.4.2 More Advanced Use of the Experimenter 435

13.5 Optimization within the WITNESS® Experimenter 440

13.5.1 Productivity-Cost Tradeoffs Explored with the Experimenter 444

13.6 Summary 447

Questions and Exercises 447

Bibliography 448

14 Simulation for Lean Systems 449

14.1 Overview 449

14.2 Basics of Lean Systems 450

14.2.1 Lean Principles 450

14.2.2 Lean Techniques 453

14.2.3 Value Stream Mapping 454

14.3 Simulation-Based Lean Systems 457

14.3.1 Lean Simulation Example 459

14.4 Lean Using WITNESS® 477

14.5 Summary 485

Question and Exercises 485

Bibliography 487

15 Simulation for Six Sigma 489

15.1 Overview 489

15.2 Six Sigma Quality 490

15.2.1 Six Sigma Capability 493

15.2.2 Determining Process Sigma Rating 494

15.3 Six Sigma Methods 496

15.3.1 DMAIC Process 497

15.3.2 Design for Six Sigma (DFSS) 499

15.4 WITNESS® for Six Sigma 501

15.4.1 Sigma Ratings in WITNESS® 504

15.5 Simulation-Based Six Sigma 520

15.5.1 Simulation-Based DMAIC 520

15.5.2 Simulation-Based DFSS 526

15.5.3 Lean Six Sigma (LSS) 537

15.6 Summary 545

Questions and Exercises 546

Bibliography 547

Appendix 549

Index 553

Raid Al-Aomar is a Simulation Expert and a Professor of Industrial Engineering at in College of Engineering at Abu Dhabi University in the UAE.

Edward J. Williams works at the Production Modeling Corporation in Dearborn, Michigan, and teaches courses in Business Analytics at the University of Michigan - Dearborn.

Onur M. Ülgen is a Professor in the Industrial and Manufacturing Systems Engineering Department at the University of Michigan in Dearborn, Michigan. He is also the President of Production Modeling Corporation, a process simulation company with offices in USA (HQ), Sweden, and India.