Process Simulation Using WITNESS

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.

• About the Companion Website xviiPreface xixAcknowledgments xxiii1 Concepts of Simulation Modeling 11.1 Overview 11.2 System Modeling 21.2.1 System Concept 21.2.2 Modeling Concept 41.2.3 Types of Models 51.3 Simulation Modeling 111.3.1 Simulation Defined 111.3.2 Simulation Taxonomy 121.4 The Role of Simulation 151.4.1 Simulation Justified 151.4.2 Simulation Applications 161.4.3 Simulation Precautions 171.5 Simulation Methodology 201.5.1 Identify Problem/Opportunity 201.5.2 Develop Solution/Improvement Alternatives 211.5.3 Evaluate Solution Alternatives 211.5.4 Select the Best Alternative 221.5.5 Implement the Selected Alternative 221.6 Steps in a Simulation Study 221.6.1 Problem Formulation 231.6.2 Setting Study Objectives 231.6.3 Conceptual Modeling 251.6.4 Data Collection 261.6.5 Model Building 271.6.6 Model Verification 301.6.7 Model Validation 301.6.8 Model Analysis 311.6.9 Study Documentation 321.7 Simulation Software 341.7.1 WITNESS® Simulation Software 351.8 Summary 36Questions and Exercises 37Bibliography 382 World-Views of Simulation 412.1 Overview 412.2 System Modeling with DES 422.2.1 System Structure 422.2.2 System Layout 432.2.3 System Data 432.2.4 System Logic 442.2.5 System Statistics 452.3 Elements of Discrete Event Simulation (DES) 452.3.1 System Entities (EN) 452.3.2 System State (S) 462.3.3 State Variables (VR) 462.3.4 System Events (E) 472.3.5 System Activities (A) 482.3.6 System Resources (R) 482.3.7 System Delay (D) 502.3.8 System Logic (L) 502.4 DES Functionality 512.4.1 Discrete-Event Mechanism 522.4.2 Time-Advancement Mechanism 542.4.3 Random Sampling Mechanism 552.4.4 Statistical Accumulation Mechanism 582.4.5 Animation Mechanism 592.5 Example of DES Mechanisms 602.6 Monte Carlo Simulation (MCS) 652.7 Continuous Simulation 682.7.1 WITNESS® for Continuous Simulation 692.7.2 Hybrid Simulation 692.8 WITNESS® World-views of Simulation 702.8.1 Attribute 722.8.2 Buffer 722.8.3 Carrier 722.8.4 Conveyor 732.8.5 Fluid 732.8.6 Labor 742.8.7 Machine 742.8.8 Part 752.8.9 Path 752.8.10 Pipe 752.8.11 Processor 752.8.12 Sections 752.8.13 Station 762.8.14 Tank 762.8.15 Track 762.8.16 Vehicle 762.9 Summary 77Questions and Exercises 78Bibliography 803 WITNESS® Environment 833.1 Overview 833.2 The WITNESS® Environment 833.3 Menus 853.3.1 General Menu Operation 863.4 Tool Bars 863.4.1 Standard Tool Bar 863.4.2 Views Toolbar 873.4.3 Element Tool Bar 893.4.4 Model Tool Bar, 923.4.5 Assistant Toolbar, 923.4.6 Run Toolbar, 933.4.7 Reporting Toolbar, 953.4.8 Display Edit Toolbar, 963.4.9 Creating a New Toolbar, 993.5 Dialog Boxes and Property Sheets 1003.5.1 Entry/Field Types 1003.6 Windows 1023.7 Layers 1033.8 The WITNESS® Editor 1033.8.1 Editor Features 1033.8.2 Manipulating a Window 1053.9 Window Operations 1053.9.1 Windows Options 1053.9.2 The Interact Box 1063.9.3 The Clock (Time) 1073.9.4 The Analog Clock 1073.9.5 Copying, Cutting, and Pasting 1073.9.6 Copy and Cut Element’s Display or Detail Features 1083.10 The Help Facility 1083.11 The Basic Elements 109Questions and Exercises 109Bibliography 1104 Basic WITNESS® Modeling Techniques 1114.1 Overview 1114.2 Step-by-Step Model Building 1114.3 Modeling a Simple Manufacturing Process 1124.3.1 Define: Specifying Elements of the ManufacturingProcess Simulation Model 1144.3.2 Detail: Adding Specifications for Elements to the Model 1144.3.3 Display: Modifying the Appearance of Elements in the Layout Window 1184.4 Modeling a Service Process 1264.4.1 Service Model Example 1264.5 WITNESS® Code 1414.6 An Extended Example 141Questions and Exercises 143Bibliography 1465 Modeling Material Handling Systems 1495.1 Overview 1495.2 Material Handling Systems 1495.3 Material Handling Systems in WITNESS® 1505.4 Modeling Conveyors 1525.5 Modeling Paths for Labor and Parts Transit 1565.6 Modeling Vehicles and Tracks 1615.7 Modeling Power-&-Free Systems 167Questions and Exercises 176Bibliography 1766 Basic Probability and Statistics for Simulation 1796.1 Overview 1796.2 Random Variables (RVs) 1796.2.1 Examples of Discrete Random Variables 1806.2.2 Examples of Continuous Random Variables 1816.3 Point Estimation 1826.4 Confidence Intervals for the Population Mean 1826.5 Confidence Intervals for the Population Variance and Standard Deviation 1846.6 Sample Size Determination when Estimating Population Mean 1856.7 Theoretical Probability Distributions 1866.7.1 The Uniform Distribution 1876.7.2 The Normal Distribution 1876.7.3 The Exponential Distribution 1906.7.4 The Erlang Distribution 1906.7.5 The Gamma Distribution 1926.7.6 The Weibull Distribution 1936.7.7 Triangular Distribution 193Questions and Exercises 197Bibliography 1987 Simulation Input Modeling 1997.1 Overview 1997.2 Determining Data Requirements 2007.3 Methods of Data Collection 2027.4 Representing Collected Data 2117.5 Validating Collected Data 2137.5.1 Filtering the Data from Outliers and Wrong Measures 2157.5.2 Testing the Data for Independence 2157.5.3 Testing if Data are Identically Distributed 2187.6 Fitting Probability Distributions to Collected Data 2197.6.1 Using Empirical Distributions 2257.7 WITNESS® Input Modeling 2267.7.1 WITNESS® RNG 2277.7.2 Incorporating Collected Data in WITNESS® 2297.7.3 Using Databases with WITNESS® 2337.8 Practical Aspects of Input Modeling 2347.8.1 Example of Input Modeling: Auto Service Center 2367.8.2 Example of Input Modeling: ER Simulation 2437.9 Summary 249Questions and Exercises 249Bibliography 2528 Simulation Output Analysis 2538.1 Overview 2538.2 Terminating Versus Steady-State Simulation 2548.2.1 Terminating Simulation 2548.2.2 Steady-State Simulation 2578.3 Determining Simulation Run Controls 2598.3.1 Determining Warm-Up Period 2608.3.2 Determining Simulation Run Length 2638.3.3 Determining the Number of Simulation Runs 2668.4 Variability in Simulation Outputs 2678.4.1 Variance Reduction Techniques 2698.5 Simulation Output Analysis 2708.5.1 Statistical Analysis of Simulation Outputs 2728.5.2 Experimental Design 2858.6 Example: Output Analyses of a Clinic Simulation 2918.7 WITNESS® Modules for Simulation Output Analysis 2968.7.1 WITNESS® Outputs and Charts 2968.7.2 WITNESS® Costing 2978.7.3 WITNESS® Scenario Manager 2998.7.4 WITNESS® Documentor 2998.7.5 WITNESS® Optimizer 3008.8 Summary 300Questions and Exercises 301Bibliography 3039 Model Verification and Validation Techniques 3059.1 Overview 3059.2 Model Verification Techniques 3069.2.1 Verifying Model Inputs 3089.2.2 Verifying Model Logic 3099.2.3 Verifying Model Outputs 3149.3 Model Validation Techniques 3149.3.1 Validating Model Inputs 3169.3.2 Validating Model Behavior 3189.3.3 Validating Model Outputs 3199.4 Verifying WITNESS® Models 3209.5 Summary 330Question and Exercise 330Bibliography 33210 Simulation Project Management 33110.1 Overview 33110.2 Define the Problem 33210.2.1 Define the Objectives of the Study 33210.2.2 List the Specific Issues to Be Addressed 33410.2.3 Determine the Boundary or Domain of the Study 33410.2.4 Determine the Level of Detail or Proper Abstraction Level 33410.2.5 Determine if a Simulation Model is Actually Needed 33510.2.6 Estimate the Required Resources Needed to Do the Study 33510.2.7 Perform a Cost-Benefit Analysis 33510.2.8 Create a Planning Chart of the Proposed Project 33610.2.9 Write a Formal Proposal 33610.3 Design the Study 33710.3.1 Estimate the Life Cycle of the Model 33810.3.2 List Broad Assumptions 33810.3.3 Estimate the Number of Models Required 33810.3.4 Determine the Animation Requirements 33810.3.5 Select the Tool 33910.3.6 Determine the Level of Data Available and What Data is Needed 33910.3.7 Determine the Human Requirements and Skill Levels 33910.3.8 Determine the Audience (Levels of Management) 34010.3.9 Identify the Deliverables 34010.3.10 Determine the Priority of the Study in Relationship to Other Studies 34010.3.11 Set Milestone Dates 34110.3.12 Write the Project Functional Specifications 34110.4 Design the Conceptual Model 34110.4.1 Decide on Continuous, Discrete, or Combined Modeling 34210.4.2 Determine the Elements that Drive the System 34210.4.3 Determine the Entities that Should Represent the System Elements 34310.4.4 Determine the Level of Detail Needed to Describe the System Components 34310.4.5 Determine the Graphics Requirements of the Model 34310.4.6 Identify the Areas That Utilize Special Control Logic 34410.4.7 Determine How to Collect Statistics in the Model and Communicate Results to the Customer 34410.5 Formulate Inputs, Assumptions, and Process Definition 34410.5.1 Specify the Operating Philosophy of the System 34510.5.2 Describe the Physical Constraints of the System 34510.5.3 Describe the Creation and Termination of Dynamic Elements 34510.5.4 Describe the Process in Detail 34510.5.5 Obtain the Operation Specifications 34610.5.6 Obtain the Material Handling Specifications 34610.5.7 List All the Assumptions 34610.5.8 Analyze the Input Data 34610.5.9 Specify the Runtime Parameters 34710.5.10 Write the Detailed Project Functional Specifications 34710.5.11 Validate the Conceptual Model 34710.6 Build, Verify, and Validate the Model 34810.7 Experiment with the Model 34810.8 Documentation and Presentation 34910.8.1 Project Book 35010.8.2 Documentation of Model Input, Code, and Output 35010.8.3 Project Functional Specifications 35010.8.4 User Manual 35010.8.5 Maintenance Manual 35110.8.6 Discussion and Explanation of Model Results 35110.8.7 Recommendations for Further Areas of Study 35110.8.8 Final Project Report and Presentation 35110.9 Define the Model Life Cycle 35210.9.1 Construct User-Friendly Model Input and Output Interfaces 35310.9.2 Determine Model and Training Responsibility 35310.9.3 Establish Data Integrity and Collection Procedures 35410.9.4 Perform Field Data Validation Tests 35410.10 Summary 354Bibliography 35411 Manufacturing Simulation Case Studies 35711.1 Overview 35711.2 Hybrid Simulation of Titanium Manufacturing Process 35811.2.1 Model Description 35811.2.2 Model Assumptions 36011.2.3 Process Logic 36011.2.4 Start-up Conditions and Model Run Length 36111.2.5 Model Input Data 36111.2.6 Model Outputs 36311.2.7 The WITNESS® Model 36311.2.8 Model Verification and Validation 36611.2.9 Model Experiments 36711.2.10 Project Results and Conclusions 37111.3 Paint Capacity Study of an Aviation Company 37311.3.1 Paint Shop Layout 37311.3.2 Study Assumptions 37311.3.3 Data Collection 37511.3.4 The WITNESS® Model 37511.3.5 Study Results 37511.3.6 Throughput Improvement Opportunities 37511.4 Simulation of a Seamless Pipe Facility 37611.4.1 Study Objectives Include 37711.4.2 System Description 37911.4.3 Input Parameters 37911.4.4 Schedule Data 38111.4.5 The WITNESS® Model 38111.4.6 Base Model–Worst-Case Schedule 38111.4.7 Results Summary 38711.4.8 Observations Summary 38911.4.9 Conclusions 39311.5 Summary 393Bibliography 39312 Service Simulation Case Studies 39512.1 Overview 39512.2 Elements of Service Systems 39612.2.1 System Entities 39612.2.2 Service Providers 39612.2.3 Customer Service 39712.2.4 Staff and Human Resources 39712.2.5 Facility Layout and Physical Structure 39712.2.6 Operating Policies 39812.3 Characteristics of Service Systems 39812.4 Modeling Service Systems 39912.4.1 Modeling Considerations 39912.4.2 Model Elements 40112.4.3 Model Control Factors 40112.4.4 Model Performance Measures 40212.5 Applications of Service System Simulation 40212.5.1 Examples of Service Systems Simulation 40312.6 Case Studies on Service Systems Simulation 40412.6.1 Car Wash 40412.6.2 Harbor Traffic Simulation 40612.6.3 Bank Simulation Example 40912.6.4 Clinic Simulation Example 41112.6.5 Public Service Office Simulation 41712.7 Summary 423Bibliography 42313 Simulation-Based Optimization Methods 42513.1 Overview 42513.2 Optimization Approaches in Simulation Studies 42613.3 Simulation-Based Optimization 42713.4 WITNESS® Experimenter 42913.4.1 Comparison of Multiple Alternatives with WITNESS® Experimenter 42913.4.2 More Advanced Use of the Experimenter 43513.5 Optimization within the WITNESS® Experimenter 44013.5.1 Productivity-Cost Tradeoffs Explored with the Experimenter 44413.6 Summary 447Questions and Exercises 447Bibliography 44814 Simulation for Lean Systems 44914.1 Overview 44914.2 Basics of Lean Systems 45014.2.1 Lean Principles 45014.2.2 Lean Techniques 45314.2.3 Value Stream Mapping 45414.3 Simulation-Based Lean Systems 45714.3.1 Lean Simulation Example 45914.4 Lean Using WITNESS® 47714.5 Summary 485Question and Exercises 485Bibliography 48715 Simulation for Six Sigma 48915.1 Overview 48915.2 Six Sigma Quality 49015.2.1 Six Sigma Capability 49315.2.2 Determining Process Sigma Rating 49415.3 Six Sigma Methods 49615.3.1 DMAIC Process 49715.3.2 Design for Six Sigma (DFSS) 49915.4 WITNESS® for Six Sigma 50115.4.1 Sigma Ratings in WITNESS® 50415.5 Simulation-Based Six Sigma 52015.5.1 Simulation-Based DMAIC 52015.5.2 Simulation-Based DFSS 52615.5.3 Lean Six Sigma (LSS) 53715.6 Summary 545Questions and Exercises 546Bibliography 547Appendix 549Index 553

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