Manuel Laguna is a Media One Professor of Management Science at the Leeds School of Business in the University of Colorado. He received his doctoral degree in Operations Research and Industrial Engineering from the University of Texas at Austin. He has more than one hundred publications in data analytics methods and applications, and is the editor-in-chief of the Journal of Heuristics.Johan Marklund is a Professor of Production Management at Lund University, Faculty of Engineering in Sweden. He holds a PhD in Production Management and BSc in Business Administration from Lund University, and a MSc in Industrial Engineering and Management from Linköping University. He has published in numerous scientific journals and his research interests include inventory theory, supply chain management and logistics.

• 1. INTRODUCTION TO BUSINESS PROCESS DESIGN1.1 What is a Business Process?1.1.1 Process Types and Hierarchies1.1.2 Determinants of the Process Architecture1.1.3 Workflow Management Systems1.2 The Essence of Business Process Design1.2.1 Incremental Process Improvement and Process Design1.2.2 An Illustrative Example1.3 Business Process Design, Overall Business Performance and Strategy1.3.1 Business Process Design and Overall Business Performance1.3.2 Business Process Design and Strategy1.4 Why do Inefficient and Ineffective Business Processes Exists1.5 SummaryDiscussion Questions and ExercisesReferences2. DATA ANALYTICS AND PROCESS IMPROVEMENT2.1 Process Management and a Process View2.1.1 An Illustrative Example: Managing a Document Distribution Process2.1.1.1 Assign Process Ownership2.1.1.2 Analyze Boundaries and Interfaces2.1.1.3 Define the Process2.1.1.4 Establish Control Points2.1.1.5 Develop and Implement Measures2.1.1.6 Perform Feedback and Control2.1.2 Summary and Final Remarks2.2 Data-driven Process Improvement2.2.1 Data Collection2.2.2 Data Visualization2.2.2.1 Charts and Diagrams2.2.2.2 Heatmaps2.3 Six Sigma Quality Programs2.3.1 Six Sigma Definitions2.3.2 The Six Sigma Cost and Revenue Rationale2.3.2.1 The Cost or Efficiency Rationale2.3.2.2 The Revenue or Effectiveness Rationale2.3.3 Six Sigma in Product and Process Design2.3.4 The Six Sigma Framework2.3.4.1 Top Management Commitment2.3.4.2 Stakeholder Involvement2.3.4.3 Training2.3.4.4 Measurement System2.3.4.5 The Improvement Methodology2.3.5 Control Charts2.3.5.1 Average Waiting Time in a Call Center2.3.5.2 Individual Waiting Time in a Call Center2.3.6 Key Reasons for the Success of Six Sigma2.4 Business Process Management2.4.1 Types of BPM2.4.2 BPM Lifecycle2.4.3 BPM Potential Benefits2.4.4 Typical Areas of Application2.5 Evolutionary versus Revolutionary Change2.6 SummaryDiscussion Questions and ExercisesReferences3. A FRAMEWORK FOR BUSINESS PROCESSES DESIGN PROJECTS3.1 Step 1: Case for Action and Vision Statements3.2 Step 2: Process Identification and Selection3.3 Step 3: Obtaining Management Commitment3.4 Step 4: Evaluation of Design Enablers3.4.1 Example: The Internet Enabling Change at Chase Manhattan Bank3.4.2 Example: New Technology as a Change Enabler in the Grocery Industry3.5 Step 5: Acquiring Process Understanding3.5.1 Understanding the Existing Process3.5.2 Understanding the Customer 3.6 Step 6: Creative Process Design3.6.1 Benchmarking3.6.2 Design Principles3.6.3 The Devil’s Quadrangle3.7 Step 7: Process Modeling and Simulation3.8 Step 8: Implementation of the New Process Design3.9 SummaryDiscussion Questions and ExercisesReferences4. BASIC TOOLS FOR PROCESS DESIGN4.1 Process Flow Analysis4.1.1 General Process Charts4.1.2 Process Flow Diagrams4.1.3 Process Activity Charts4.1.4 Flowcharts4.1.5 Service System Maps4.2 Workflow Design Principles and Tools4.2.1 Establish a Product Orientation in the Process4.2.2 Eliminate Buffers4.2.3 Establish One-at-a-Time Processing 4.2.4 Balance the Flow to the Bottleneck4.2.5 Minimize Sequential Processing and Handoffs4.2.6 Establish an Efficient Processing of Work4.2.7 Minimize Multiple Paths through Operations4.3 Additional Diagramming Tools4.4 From Theory to Practice: Designing an Order Picking Process4.5 SummaryDiscussion Questions and ExercisesReferences5. MANAGING PROCESS FLOWS5.1 Business Processes and Flows5.1.1 Throughput Rate5.1.2 Work-in-process5.1.3 Cycle Time5.1.4 Little’s Law5.2 Cycle Time and Capacity Analysis5.2.1 Cycle Time Analysis5.2.1.1 Rework5.2.1.2 Multiple Paths5.2.1.3 Parallel Activities5.2.2 Capacity Analysis5.2.2.1 Rework5.2.2.2 Multiple Paths5.2.2.3 Parallel Activities5.3 Managing Cycle Time and Capacity5.3.1 Cycle Time Reduction5.3.2 Increasing Process Capacity5.4 Theory of Constraints5.4.1 Drum-Buffer-Rope Systems5.5 SummaryDiscussion Questions and ExercisesReferences6. INTRODUCTION TO QUEUING MODELING6.1 Queuing Systems, the Basic Queuing Process and Queuing Strategies6.1.1 The Basic Queuing Process6.1.2 Strategies for Mitigating the Effects of Long Queues6.2 Analytical Queuing Models6.2.1 The Exponential Distribution and its Role in Queuing Theory6.2.2 Terminology, Notation and Little’s Law Revisited6.2.3 Birth and Death Processes6.2.4 The M/M/1 Model6.2.5 The M/M/c Model6.2.6 The M/M/c/K Model6.2.7 The M/M/c/¥/N Model6.2.8 Queuing Theory and Process Design6.3 SummaryAppendix 6A: Mathematical Derivations and Models with Generally Distributed Service Times6A.1 Mathematical Derivations of Key Results6A.1.1 The exponential distribution6A.1.2 Birth-and-death processes6A.1.3 The M/M/1 Model6A.2 Queuing Models with Generally Distributed Service Times6A.2.1 The M/G/1 queuing model6A.2.2 The M/G/¥ queuing modelDiscussion Questions and ExercisesReferences7. INTRODUCTION TO SIMULATION7.1 Simulation Models7.2 Discrete Event Simulation7.3 Getting Started in Simulation Modeling7.4 An Illustrative Example7.5 Spreadsheet Simulation of a Process7.6 Successful Simulation in Practice7.7 When not to Simulate7.8 SummaryDiscussion Questions and ExercisesReferences8. MODELING AND SIMULATING BUSINESS PROCESSES WITH ExtendSim8.1 Developing a Simulation Model – Principles and Concepts8.1.1 Model Verification8.1.2 Model Validation8.2 ExtendSim Elements8.3 ExtendSim Tutorial: A Basic Queuing Model8.4 Basic Data Collection and Statistical Analysis8.5 Adding Randomness to Processing Times and the use of Attributes8.6 Adding a Second Underwriting Team8.7 Modeling Resources and Resource Pools8.8 Customizing the Animation8.9 Calculating Activity Based Costs8.10 Cycle Time Analysis8.11 Modeling Advanced Queuing Features8.11.1 Blocking8.11.2 Balking8.11.3 Reneging8.11.4 Priorities and Priority Queues8.12 Modeling Routing in Multiple Paths and Parallel Paths8.12.1 Multiple Paths8.12.2 Parallel Paths8.13 Model Documentation and Enhancements8.14 SummaryDiscussion Questions and ExercisesReferences9. INPUT AND OUTPUT DATA ANALYSIS9.1 Dealing with Randomness9.2 Characterizing Probability Distributions of Field Data9.2.1 Goodness-of-Fit Tests9.2.2 Using Stat::Fit for Distribution Fitting9.2.3 Choosing a Distribution in the Absence of Sample Data9.3 Random Number Generators9.3.1 The Runs Test9.4 Generation of Random Variates9.5 Analysis of Simulation Output Data9.5.1 Nonterminating Processes9.5.2 Terminating Processes9.5.3 Confidence Intervals9.5.4 Sample Size Calculation9.5.5 Comparing Output Variables for Different Process Designs9.6 Modeling and Analysis of Process Design Cases9.6.1 Process Design of a Call Center for Software Support9.6.2 Design of a Hospital Admissions Process9.7 Summary9.8 Training cases9.8.1 CASE 1: IMPROVING THE X-RAY PROCESS AT COUNTY HOSPITAL9.8.2 CASE 2: PROCESS MODELING AND ANALYSIS IN AN ASSEMBLY FACTORY9.8.3 CASE 3: REDESIGN OF A CREDIT APPLICATIONS PROCESS9.8.4 CASE 4: REDISIGNING THE ADOPTION PROCESS IN A HUMANE SOCIETY9.8.5 CASE 5: PERFORMANCE ANALYSIS AND IMPROVEMENT OF AN INTERNET ORDERING PROCESSAppendix 9A: Hypothesis Testing, Confidence Intervals, and Statistical Tables9A.1 Goodness-of-Fit Tests9A.1.1 The Chi-Square Test9A.1.2 The Kolmogorov-Smirnov Test9A.2 Confidence Interval for a Population Proportion9A.3 Hypothesis Testing9A.4 Statistical TablesExercisesReferences10. PRESCRIPTIVE ANALYTICS FOR PROCESS PERFORMANCE OPTIMIZATION10.1 Identifying the Main Drivers of Process Performance10.1.1 Factorial Design for Simulation Models10.1.2 Illustrative Example of Design of Experiments10.2 Business Process Optimization10.3 The Role of Simulation Optimization in Business Process Management104 Simulation-Optimization with ExtendSim10.4.1 Tutorial: Process Optimization with ExtendSim10.4.2 Alternative Optimization Models10.5 Optimization of Process Simulation Models10.5.1 Configuring a Hospital Emergency Room Process10.5.2 Staffing Levels for a Personal Insurance Claims Process10.6 SummaryAppendix 10A: Evolutionary ComputationExercisesSimulation-Optimization ProjectsProject 1: Emergency Room StaffingProject 2: Call Center ConfigurationProject 3: Loan Application ProcessProject 4: Process with Multiple Job Types and DeadlinesReferences11. BUSINESS PROCESS ANALYTICS11.1 Competing on Analytics11.2 Business Process Management Systems11.2.1 Business Rules11.2.2 Monitor and Control11.2.3 Process Mining11.3 Machine Learning11.3.1 Support Vector Machines11.3.2 k-Nearest Neighbor Classifier11.3.3 Neural Networks11.3.4 Classification Problems in Business ProcessesDiscussion Questions and ExercisesReferences

Manuel Laguna is a Media One Professor of Management Science at the Leeds School of Business in the University of Colorado. He received his doctoral degree in Operations Research and Industrial Engineering from the University of Texas at Austin. He has more than one hundred publications in data analytics methods and applications, and is the editor-in-chief of the Journal of Heuristics.Johan Marklund is a Professor of Production Management at Lund University, Faculty of Engineering in Sweden. He holds a PhD in Production Management and BSc in Business Administration from Lund University, and a MSc in Industrial Engineering and Management from Linköping University. He has published in numerous scientific journals and his research interests include inventory theory, supply chain management and logistics.

• 1. INTRODUCTION TO BUSINESS PROCESS DESIGN1.1 What is a Business Process?1.1.1 Process Types and Hierarchies1.1.2 Determinants of the Process Architecture1.1.3 Workflow Management Systems1.2 The Essence of Business Process Design1.2.1 Incremental Process Improvement and Process Design1.2.2 An Illustrative Example1.3 Business Process Design, Overall Business Performance and Strategy1.3.1 Business Process Design and Overall Business Performance1.3.2 Business Process Design and Strategy1.4 Why do Inefficient and Ineffective Business Processes Exists1.5 SummaryDiscussion Questions and ExercisesReferences2. DATA ANALYTICS AND PROCESS IMPROVEMENT2.1 Process Management and a Process View2.1.1 An Illustrative Example: Managing a Document Distribution Process2.1.1.1 Assign Process Ownership2.1.1.2 Analyze Boundaries and Interfaces2.1.1.3 Define the Process2.1.1.4 Establish Control Points2.1.1.5 Develop and Implement Measures2.1.1.6 Perform Feedback and Control2.1.2 Summary and Final Remarks2.2 Data-driven Process Improvement2.2.1 Data Collection2.2.2 Data Visualization2.2.2.1 Charts and Diagrams2.2.2.2 Heatmaps2.3 Six Sigma Quality Programs2.3.1 Six Sigma Definitions2.3.2 The Six Sigma Cost and Revenue Rationale2.3.2.1 The Cost or Efficiency Rationale2.3.2.2 The Revenue or Effectiveness Rationale2.3.3 Six Sigma in Product and Process Design2.3.4 The Six Sigma Framework2.3.4.1 Top Management Commitment2.3.4.2 Stakeholder Involvement2.3.4.3 Training2.3.4.4 Measurement System2.3.4.5 The Improvement Methodology2.3.5 Control Charts2.3.5.1 Average Waiting Time in a Call Center2.3.5.2 Individual Waiting Time in a Call Center2.3.6 Key Reasons for the Success of Six Sigma2.4 Business Process Management2.4.1 Types of BPM2.4.2 BPM Lifecycle2.4.3 BPM Potential Benefits2.4.4 Typical Areas of Application2.5 Evolutionary versus Revolutionary Change2.6 SummaryDiscussion Questions and ExercisesReferences3. A FRAMEWORK FOR BUSINESS PROCESSES DESIGN PROJECTS3.1 Step 1: Case for Action and Vision Statements3.2 Step 2: Process Identification and Selection3.3 Step 3: Obtaining Management Commitment3.4 Step 4: Evaluation of Design Enablers3.4.1 Example: The Internet Enabling Change at Chase Manhattan Bank3.4.2 Example: New Technology as a Change Enabler in the Grocery Industry3.5 Step 5: Acquiring Process Understanding3.5.1 Understanding the Existing Process3.5.2 Understanding the Customer 3.6 Step 6: Creative Process Design3.6.1 Benchmarking3.6.2 Design Principles3.6.3 The Devil’s Quadrangle3.7 Step 7: Process Modeling and Simulation3.8 Step 8: Implementation of the New Process Design3.9 SummaryDiscussion Questions and ExercisesReferences4. BASIC TOOLS FOR PROCESS DESIGN4.1 Process Flow Analysis4.1.1 General Process Charts4.1.2 Process Flow Diagrams4.1.3 Process Activity Charts4.1.4 Flowcharts4.1.5 Service System Maps4.2 Workflow Design Principles and Tools4.2.1 Establish a Product Orientation in the Process4.2.2 Eliminate Buffers4.2.3 Establish One-at-a-Time Processing 4.2.4 Balance the Flow to the Bottleneck4.2.5 Minimize Sequential Processing and Handoffs4.2.6 Establish an Efficient Processing of Work4.2.7 Minimize Multiple Paths through Operations4.3 Additional Diagramming Tools4.4 From Theory to Practice: Designing an Order Picking Process4.5 SummaryDiscussion Questions and ExercisesReferences5. MANAGING PROCESS FLOWS5.1 Business Processes and Flows5.1.1 Throughput Rate5.1.2 Work-in-process5.1.3 Cycle Time5.1.4 Little’s Law5.2 Cycle Time and Capacity Analysis5.2.1 Cycle Time Analysis5.2.1.1 Rework5.2.1.2 Multiple Paths5.2.1.3 Parallel Activities5.2.2 Capacity Analysis5.2.2.1 Rework5.2.2.2 Multiple Paths5.2.2.3 Parallel Activities5.3 Managing Cycle Time and Capacity5.3.1 Cycle Time Reduction5.3.2 Increasing Process Capacity5.4 Theory of Constraints5.4.1 Drum-Buffer-Rope Systems5.5 SummaryDiscussion Questions and ExercisesReferences6. INTRODUCTION TO QUEUING MODELING6.1 Queuing Systems, the Basic Queuing Process and Queuing Strategies6.1.1 The Basic Queuing Process6.1.2 Strategies for Mitigating the Effects of Long Queues6.2 Analytical Queuing Models6.2.1 The Exponential Distribution and its Role in Queuing Theory6.2.2 Terminology, Notation and Little’s Law Revisited6.2.3 Birth and Death Processes6.2.4 The M/M/1 Model6.2.5 The M/M/c Model6.2.6 The M/M/c/K Model6.2.7 The M/M/c/¥/N Model6.2.8 Queuing Theory and Process Design6.3 SummaryAppendix 6A: Mathematical Derivations and Models with Generally Distributed Service Times6A.1 Mathematical Derivations of Key Results6A.1.1 The exponential distribution6A.1.2 Birth-and-death processes6A.1.3 The M/M/1 Model6A.2 Queuing Models with Generally Distributed Service Times6A.2.1 The M/G/1 queuing model6A.2.2 The M/G/¥ queuing modelDiscussion Questions and ExercisesReferences7. INTRODUCTION TO SIMULATION7.1 Simulation Models7.2 Discrete Event Simulation7.3 Getting Started in Simulation Modeling7.4 An Illustrative Example7.5 Spreadsheet Simulation of a Process7.6 Successful Simulation in Practice7.7 When not to Simulate7.8 SummaryDiscussion Questions and ExercisesReferences8. MODELING AND SIMULATING BUSINESS PROCESSES WITH ExtendSim8.1 Developing a Simulation Model – Principles and Concepts8.1.1 Model Verification8.1.2 Model Validation8.2 ExtendSim Elements8.3 ExtendSim Tutorial: A Basic Queuing Model8.4 Basic Data Collection and Statistical Analysis8.5 Adding Randomness to Processing Times and the use of Attributes8.6 Adding a Second Underwriting Team8.7 Modeling Resources and Resource Pools8.8 Customizing the Animation8.9 Calculating Activity Based Costs8.10 Cycle Time Analysis8.11 Modeling Advanced Queuing Features8.11.1 Blocking8.11.2 Balking8.11.3 Reneging8.11.4 Priorities and Priority Queues8.12 Modeling Routing in Multiple Paths and Parallel Paths8.12.1 Multiple Paths8.12.2 Parallel Paths8.13 Model Documentation and Enhancements8.14 SummaryDiscussion Questions and ExercisesReferences9. INPUT AND OUTPUT DATA ANALYSIS9.1 Dealing with Randomness9.2 Characterizing Probability Distributions of Field Data9.2.1 Goodness-of-Fit Tests9.2.2 Using Stat::Fit for Distribution Fitting9.2.3 Choosing a Distribution in the Absence of Sample Data9.3 Random Number Generators9.3.1 The Runs Test9.4 Generation of Random Variates9.5 Analysis of Simulation Output Data9.5.1 Nonterminating Processes9.5.2 Terminating Processes9.5.3 Confidence Intervals9.5.4 Sample Size Calculation9.5.5 Comparing Output Variables for Different Process Designs9.6 Modeling and Analysis of Process Design Cases9.6.1 Process Design of a Call Center for Software Support9.6.2 Design of a Hospital Admissions Process9.7 Summary9.8 Training cases9.8.1 CASE 1: IMPROVING THE X-RAY PROCESS AT COUNTY HOSPITAL9.8.2 CASE 2: PROCESS MODELING AND ANALYSIS IN AN ASSEMBLY FACTORY9.8.3 CASE 3: REDESIGN OF A CREDIT APPLICATIONS PROCESS9.8.4 CASE 4: REDISIGNING THE ADOPTION PROCESS IN A HUMANE SOCIETY9.8.5 CASE 5: PERFORMANCE ANALYSIS AND IMPROVEMENT OF AN INTERNET ORDERING PROCESSAppendix 9A: Hypothesis Testing, Confidence Intervals, and Statistical Tables9A.1 Goodness-of-Fit Tests9A.1.1 The Chi-Square Test9A.1.2 The Kolmogorov-Smirnov Test9A.2 Confidence Interval for a Population Proportion9A.3 Hypothesis Testing9A.4 Statistical TablesExercisesReferences10. PRESCRIPTIVE ANALYTICS FOR PROCESS PERFORMANCE OPTIMIZATION10.1 Identifying the Main Drivers of Process Performance10.1.1 Factorial Design for Simulation Models10.1.2 Illustrative Example of Design of Experiments10.2 Business Process Optimization10.3 The Role of Simulation Optimization in Business Process Management104 Simulation-Optimization with ExtendSim10.4.1 Tutorial: Process Optimization with ExtendSim10.4.2 Alternative Optimization Models10.5 Optimization of Process Simulation Models10.5.1 Configuring a Hospital Emergency Room Process10.5.2 Staffing Levels for a Personal Insurance Claims Process10.6 SummaryAppendix 10A: Evolutionary ComputationExercisesSimulation-Optimization ProjectsProject 1: Emergency Room StaffingProject 2: Call Center ConfigurationProject 3: Loan Application ProcessProject 4: Process with Multiple Job Types and DeadlinesReferences11. BUSINESS PROCESS ANALYTICS11.1 Competing on Analytics11.2 Business Process Management Systems11.2.1 Business Rules11.2.2 Monitor and Control11.2.3 Process Mining11.3 Machine Learning11.3.1 Support Vector Machines11.3.2 k-Nearest Neighbor Classifier11.3.3 Neural Networks11.3.4 Classification Problems in Business ProcessesDiscussion Questions and ExercisesReferences

Manuel Laguna is a Media One Professor of Management Science at the Leeds School of Business in the University of Colorado. He received his doctoral degree in Operations Research and Industrial Engineering from the University of Texas at Austin. He has more than one hundred publications in data analytics methods and applications, and is the editor-in-chief of the Journal of Heuristics.Johan Marklund is a Professor of Production Management at Lund University, Faculty of Engineering in Sweden. He holds a PhD in Production Management and BSc in Business Administration from Lund University, and a MSc in Industrial Engineering and Management from Linköping University. He has published in numerous scientific journals and his research interests include inventory theory, supply chain management and logistics.

• 1. INTRODUCTION TO BUSINESS PROCESS DESIGN1.1 What is a Business Process?1.1.1 Process Types and Hierarchies1.1.2 Determinants of the Process Architecture1.1.3 Workflow Management Systems1.2 The Essence of Business Process Design1.2.1 Incremental Process Improvement and Process Design1.2.2 An Illustrative Example1.3 Business Process Design, Overall Business Performance and Strategy1.3.1 Business Process Design and Overall Business Performance1.3.2 Business Process Design and Strategy1.4 Why do Inefficient and Ineffective Business Processes Exists1.5 SummaryDiscussion Questions and ExercisesReferences2. DATA ANALYTICS AND PROCESS IMPROVEMENT2.1 Process Management and a Process View2.1.1 An Illustrative Example: Managing a Document Distribution Process2.1.1.1 Assign Process Ownership2.1.1.2 Analyze Boundaries and Interfaces2.1.1.3 Define the Process2.1.1.4 Establish Control Points2.1.1.5 Develop and Implement Measures2.1.1.6 Perform Feedback and Control2.1.2 Summary and Final Remarks2.2 Data-driven Process Improvement2.2.1 Data Collection2.2.2 Data Visualization2.2.2.1 Charts and Diagrams2.2.2.2 Heatmaps2.3 Six Sigma Quality Programs2.3.1 Six Sigma Definitions2.3.2 The Six Sigma Cost and Revenue Rationale2.3.2.1 The Cost or Efficiency Rationale2.3.2.2 The Revenue or Effectiveness Rationale2.3.3 Six Sigma in Product and Process Design2.3.4 The Six Sigma Framework2.3.4.1 Top Management Commitment2.3.4.2 Stakeholder Involvement2.3.4.3 Training2.3.4.4 Measurement System2.3.4.5 The Improvement Methodology2.3.5 Control Charts2.3.5.1 Average Waiting Time in a Call Center2.3.5.2 Individual Waiting Time in a Call Center2.3.6 Key Reasons for the Success of Six Sigma2.4 Business Process Management2.4.1 Types of BPM2.4.2 BPM Lifecycle2.4.3 BPM Potential Benefits2.4.4 Typical Areas of Application2.5 Evolutionary versus Revolutionary Change2.6 SummaryDiscussion Questions and ExercisesReferences3. A FRAMEWORK FOR BUSINESS PROCESSES DESIGN PROJECTS3.1 Step 1: Case for Action and Vision Statements3.2 Step 2: Process Identification and Selection3.3 Step 3: Obtaining Management Commitment3.4 Step 4: Evaluation of Design Enablers3.4.1 Example: The Internet Enabling Change at Chase Manhattan Bank3.4.2 Example: New Technology as a Change Enabler in the Grocery Industry3.5 Step 5: Acquiring Process Understanding3.5.1 Understanding the Existing Process3.5.2 Understanding the Customer 3.6 Step 6: Creative Process Design3.6.1 Benchmarking3.6.2 Design Principles3.6.3 The Devil’s Quadrangle3.7 Step 7: Process Modeling and Simulation3.8 Step 8: Implementation of the New Process Design3.9 SummaryDiscussion Questions and ExercisesReferences4. BASIC TOOLS FOR PROCESS DESIGN4.1 Process Flow Analysis4.1.1 General Process Charts4.1.2 Process Flow Diagrams4.1.3 Process Activity Charts4.1.4 Flowcharts4.1.5 Service System Maps4.2 Workflow Design Principles and Tools4.2.1 Establish a Product Orientation in the Process4.2.2 Eliminate Buffers4.2.3 Establish One-at-a-Time Processing 4.2.4 Balance the Flow to the Bottleneck4.2.5 Minimize Sequential Processing and Handoffs4.2.6 Establish an Efficient Processing of Work4.2.7 Minimize Multiple Paths through Operations4.3 Additional Diagramming Tools4.4 From Theory to Practice: Designing an Order Picking Process4.5 SummaryDiscussion Questions and ExercisesReferences5. MANAGING PROCESS FLOWS5.1 Business Processes and Flows5.1.1 Throughput Rate5.1.2 Work-in-process5.1.3 Cycle Time5.1.4 Little’s Law5.2 Cycle Time and Capacity Analysis5.2.1 Cycle Time Analysis5.2.1.1 Rework5.2.1.2 Multiple Paths5.2.1.3 Parallel Activities5.2.2 Capacity Analysis5.2.2.1 Rework5.2.2.2 Multiple Paths5.2.2.3 Parallel Activities5.3 Managing Cycle Time and Capacity5.3.1 Cycle Time Reduction5.3.2 Increasing Process Capacity5.4 Theory of Constraints5.4.1 Drum-Buffer-Rope Systems5.5 SummaryDiscussion Questions and ExercisesReferences6. INTRODUCTION TO QUEUING MODELING6.1 Queuing Systems, the Basic Queuing Process and Queuing Strategies6.1.1 The Basic Queuing Process6.1.2 Strategies for Mitigating the Effects of Long Queues6.2 Analytical Queuing Models6.2.1 The Exponential Distribution and its Role in Queuing Theory6.2.2 Terminology, Notation and Little’s Law Revisited6.2.3 Birth and Death Processes6.2.4 The M/M/1 Model6.2.5 The M/M/c Model6.2.6 The M/M/c/K Model6.2.7 The M/M/c/¥/N Model6.2.8 Queuing Theory and Process Design6.3 SummaryAppendix 6A: Mathematical Derivations and Models with Generally Distributed Service Times6A.1 Mathematical Derivations of Key Results6A.1.1 The exponential distribution6A.1.2 Birth-and-death processes6A.1.3 The M/M/1 Model6A.2 Queuing Models with Generally Distributed Service Times6A.2.1 The M/G/1 queuing model6A.2.2 The M/G/¥ queuing modelDiscussion Questions and ExercisesReferences7. INTRODUCTION TO SIMULATION7.1 Simulation Models7.2 Discrete Event Simulation7.3 Getting Started in Simulation Modeling7.4 An Illustrative Example7.5 Spreadsheet Simulation of a Process7.6 Successful Simulation in Practice7.7 When not to Simulate7.8 SummaryDiscussion Questions and ExercisesReferences8. MODELING AND SIMULATING BUSINESS PROCESSES WITH ExtendSim8.1 Developing a Simulation Model – Principles and Concepts8.1.1 Model Verification8.1.2 Model Validation8.2 ExtendSim Elements8.3 ExtendSim Tutorial: A Basic Queuing Model8.4 Basic Data Collection and Statistical Analysis8.5 Adding Randomness to Processing Times and the use of Attributes8.6 Adding a Second Underwriting Team8.7 Modeling Resources and Resource Pools8.8 Customizing the Animation8.9 Calculating Activity Based Costs8.10 Cycle Time Analysis8.11 Modeling Advanced Queuing Features8.11.1 Blocking8.11.2 Balking8.11.3 Reneging8.11.4 Priorities and Priority Queues8.12 Modeling Routing in Multiple Paths and Parallel Paths8.12.1 Multiple Paths8.12.2 Parallel Paths8.13 Model Documentation and Enhancements8.14 SummaryDiscussion Questions and ExercisesReferences9. INPUT AND OUTPUT DATA ANALYSIS9.1 Dealing with Randomness9.2 Characterizing Probability Distributions of Field Data9.2.1 Goodness-of-Fit Tests9.2.2 Using Stat::Fit for Distribution Fitting9.2.3 Choosing a Distribution in the Absence of Sample Data9.3 Random Number Generators9.3.1 The Runs Test9.4 Generation of Random Variates9.5 Analysis of Simulation Output Data9.5.1 Nonterminating Processes9.5.2 Terminating Processes9.5.3 Confidence Intervals9.5.4 Sample Size Calculation9.5.5 Comparing Output Variables for Different Process Designs9.6 Modeling and Analysis of Process Design Cases9.6.1 Process Design of a Call Center for Software Support9.6.2 Design of a Hospital Admissions Process9.7 Summary9.8 Training cases9.8.1 CASE 1: IMPROVING THE X-RAY PROCESS AT COUNTY HOSPITAL9.8.2 CASE 2: PROCESS MODELING AND ANALYSIS IN AN ASSEMBLY FACTORY9.8.3 CASE 3: REDESIGN OF A CREDIT APPLICATIONS PROCESS9.8.4 CASE 4: REDISIGNING THE ADOPTION PROCESS IN A HUMANE SOCIETY9.8.5 CASE 5: PERFORMANCE ANALYSIS AND IMPROVEMENT OF AN INTERNET ORDERING PROCESSAppendix 9A: Hypothesis Testing, Confidence Intervals, and Statistical Tables9A.1 Goodness-of-Fit Tests9A.1.1 The Chi-Square Test9A.1.2 The Kolmogorov-Smirnov Test9A.2 Confidence Interval for a Population Proportion9A.3 Hypothesis Testing9A.4 Statistical TablesExercisesReferences10. PRESCRIPTIVE ANALYTICS FOR PROCESS PERFORMANCE OPTIMIZATION10.1 Identifying the Main Drivers of Process Performance10.1.1 Factorial Design for Simulation Models10.1.2 Illustrative Example of Design of Experiments10.2 Business Process Optimization10.3 The Role of Simulation Optimization in Business Process Management104 Simulation-Optimization with ExtendSim10.4.1 Tutorial: Process Optimization with ExtendSim10.4.2 Alternative Optimization Models10.5 Optimization of Process Simulation Models10.5.1 Configuring a Hospital Emergency Room Process10.5.2 Staffing Levels for a Personal Insurance Claims Process10.6 SummaryAppendix 10A: Evolutionary ComputationExercisesSimulation-Optimization ProjectsProject 1: Emergency Room StaffingProject 2: Call Center ConfigurationProject 3: Loan Application ProcessProject 4: Process with Multiple Job Types and DeadlinesReferences11. BUSINESS PROCESS ANALYTICS11.1 Competing on Analytics11.2 Business Process Management Systems11.2.1 Business Rules11.2.2 Monitor and Control11.2.3 Process Mining11.3 Machine Learning11.3.1 Support Vector Machines11.3.2 k-Nearest Neighbor Classifier11.3.3 Neural Networks11.3.4 Classification Problems in Business ProcessesDiscussion Questions and ExercisesReferences