Overview

The fourth edition of this widely used textbook offers a new perspective. Previously titled Business Process Modeling, Simulation and Design, as the new title suggests, this book is about analytical business process modeling and design. However, this new edition introduces analytics to the title and to the presentation. The main objective of this book is to provide students with a comprehensive understanding of the multitude of analytical tools that can be used to model, analyze, understand, and ultimately design business processes. The most flexible and powerful of these tools, although not always the most appropriate, is discrete-event simulation. The wide range of approaches covered in this book include graphical flowcharting tools, deterministic models for cycle time analysis and capacity decisions, and analytical queuing methods, as well as machine learning. The authors focus on business processes as opposed to just manufacturing processes or general operations management problems and emphasize on simulation modeling using state-of-the-art commercial simulation software. Business Process Analytics: Modeling, Simulation, and Design can be thought of as a hybrid between traditional books on process management, operations management, and simulation. The growing interest in simulation-based tools suggests that an understanding of simulation modeling, its potential as well as its limitations for analyzing and designing processes, is of key importance to students looking for a future career in operations management. Changes from the previous edition include the following: New section on data-driven process improvement (with data visualization) Added a subsection of control charts to the 6-sigma section Replaced business process reengineering with business process management Updated all text, figures, examples, and exercises to ExtendSim10 (current version) More coverage on design of experiments More coverage of machine learning and neural networks

Full Product Details

Author:   Manuel Laguna (University of Colorado, Boulder, USA) ,  Johan Marklund (Lund University, Sweden)
Publisher:   Taylor & Francis Ltd
Imprint:   Chapman & Hall/CRC
Edition:   4th edition
Weight:   1.340kg
ISBN:  

9781032595429

ISBN 10:   1032595426
Pages:   620
Publication Date:   31 January 2025
Audience:   College/higher education ,  Professional and scholarly ,  Tertiary & Higher Education ,  Professional & Vocational
Format:   Hardback
Publisher's Status:   Active
Availability:   In Print  
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Table of Contents

1. INTRODUCTION TO BUSINESS PROCESS DESIGN 1.1 What is a Business Process? 1.1.1 Process Types and Hierarchies 1.1.2 Determinants of the Process Architecture 1.1.3 Workflow Management Systems 1.2 The Essence of Business Process Design 1.2.1 Incremental Process Improvement and Process Design 1.2.2 An Illustrative Example 1.3 Business Process Design, Overall Business Performance and Strategy 1.3.1 Business Process Design and Overall Business Performance 1.3.2 Business Process Design and Strategy 1.4 Why do Inefficient and Ineffective Business Processes Exists 1.5 Summary Discussion Questions and Exercises References 2. DATA ANALYTICS AND PROCESS IMPROVEMENT 2.1 Process Management and a Process View 2.1.1 An Illustrative Example: Managing a Document Distribution Process 2.1.1.1 Assign Process Ownership 2.1.1.2 Analyze Boundaries and Interfaces 2.1.1.3 Define the Process 2.1.1.4 Establish Control Points 2.1.1.5 Develop and Implement Measures 2.1.1.6 Perform Feedback and Control 2.1.2 Summary and Final Remarks 2.2 Data-driven Process Improvement 2.2.1 Data Collection 2.2.2 Data Visualization 2.2.2.1 Charts and Diagrams 2.2.2.2 Heatmaps 2.3 Six Sigma Quality Programs 2.3.1 Six Sigma Definitions 2.3.2 The Six Sigma Cost and Revenue Rationale 2.3.2.1 The Cost or Efficiency Rationale 2.3.2.2 The Revenue or Effectiveness Rationale 2.3.3 Six Sigma in Product and Process Design 2.3.4 The Six Sigma Framework 2.3.4.1 Top Management Commitment 2.3.4.2 Stakeholder Involvement 2.3.4.3 Training 2.3.4.4 Measurement System 2.3.4.5 The Improvement Methodology 2.3.5 Control Charts 2.3.5.1 Average Waiting Time in a Call Center 2.3.5.2 Individual Waiting Time in a Call Center 2.3.6 Key Reasons for the Success of Six Sigma 2.4 Business Process Management 2.4.1 Types of BPM 2.4.2 BPM Lifecycle 2.4.3 BPM Potential Benefits 2.4.4 Typical Areas of Application 2.5 Evolutionary versus Revolutionary Change 2.6 Summary Discussion Questions and Exercises References 3. A FRAMEWORK FOR BUSINESS PROCESSES DESIGN PROJECTS 3.1 Step 1: Case for Action and Vision Statements 3.2 Step 2: Process Identification and Selection 3.3 Step 3: Obtaining Management Commitment 3.4 Step 4: Evaluation of Design Enablers 3.4.1 Example: The Internet Enabling Change at Chase Manhattan Bank 3.4.2 Example: New Technology as a Change Enabler in the Grocery Industry 3.5 Step 5: Acquiring Process Understanding 3.5.1 Understanding the Existing Process 3.5.2 Understanding the Customer 3.6 Step 6: Creative Process Design 3.6.1 Benchmarking 3.6.2 Design Principles 3.6.3 The Devil’s Quadrangle 3.7 Step 7: Process Modeling and Simulation 3.8 Step 8: Implementation of the New Process Design 3.9 Summary Discussion Questions and Exercises References 4. BASIC TOOLS FOR PROCESS DESIGN 4.1 Process Flow Analysis 4.1.1 General Process Charts 4.1.2 Process Flow Diagrams 4.1.3 Process Activity Charts 4.1.4 Flowcharts 4.1.5 Service System Maps 4.2 Workflow Design Principles and Tools 4.2.1 Establish a Product Orientation in the Process 4.2.2 Eliminate Buffers 4.2.3 Establish One-at-a-Time Processing 4.2.4 Balance the Flow to the Bottleneck 4.2.5 Minimize Sequential Processing and Handoffs 4.2.6 Establish an Efficient Processing of Work 4.2.7 Minimize Multiple Paths through Operations 4.3 Additional Diagramming Tools 4.4 From Theory to Practice: Designing an Order Picking Process 4.5 Summary Discussion Questions and Exercises References 5. MANAGING PROCESS FLOWS 5.1 Business Processes and Flows 5.1.1 Throughput Rate 5.1.2 Work-in-process 5.1.3 Cycle Time 5.1.4 Little’s Law 5.2 Cycle Time and Capacity Analysis 5.2.1 Cycle Time Analysis 5.2.1.1 Rework 5.2.1.2 Multiple Paths 5.2.1.3 Parallel Activities 5.2.2 Capacity Analysis 5.2.2.1 Rework 5.2.2.2 Multiple Paths 5.2.2.3 Parallel Activities 5.3 Managing Cycle Time and Capacity 5.3.1 Cycle Time Reduction 5.3.2 Increasing Process Capacity 5.4 Theory of Constraints 5.4.1 Drum-Buffer-Rope Systems 5.5 Summary Discussion Questions and Exercises References 6. INTRODUCTION TO QUEUING MODELING 6.1 Queuing Systems, the Basic Queuing Process and Queuing Strategies 6.1.1 The Basic Queuing Process 6.1.2 Strategies for Mitigating the Effects of Long Queues 6.2 Analytical Queuing Models 6.2.1 The Exponential Distribution and its Role in Queuing Theory 6.2.2 Terminology, Notation and Little’s Law Revisited 6.2.3 Birth and Death Processes 6.2.4 The M/M/1 Model 6.2.5 The M/M/c Model 6.2.6 The M/M/c/K Model 6.2.7 The M/M/c/¥/N Model 6.2.8 Queuing Theory and Process Design 6.3 Summary Appendix 6A: Mathematical Derivations and Models with Generally Distributed Service Times 6A.1 Mathematical Derivations of Key Results 6A.1.1 The exponential distribution 6A.1.2 Birth-and-death processes 6A.1.3 The M/M/1 Model 6A.2 Queuing Models with Generally Distributed Service Times 6A.2.1 The M/G/1 queuing model 6A.2.2 The M/G/¥ queuing model Discussion Questions and Exercises References 7. INTRODUCTION TO SIMULATION 7.1 Simulation Models 7.2 Discrete Event Simulation 7.3 Getting Started in Simulation Modeling 7.4 An Illustrative Example 7.5 Spreadsheet Simulation of a Process 7.6 Successful Simulation in Practice 7.7 When not to Simulate 7.8 Summary Discussion Questions and Exercises References 8. MODELING AND SIMULATING BUSINESS PROCESSES WITH ExtendSim 8.1 Developing a Simulation Model – Principles and Concepts 8.1.1 Model Verification 8.1.2 Model Validation 8.2 ExtendSim Elements 8.3 ExtendSim Tutorial: A Basic Queuing Model 8.4 Basic Data Collection and Statistical Analysis 8.5 Adding Randomness to Processing Times and the use of Attributes 8.6 Adding a Second Underwriting Team 8.7 Modeling Resources and Resource Pools 8.8 Customizing the Animation 8.9 Calculating Activity Based Costs 8.10 Cycle Time Analysis 8.11 Modeling Advanced Queuing Features 8.11.1 Blocking 8.11.2 Balking 8.11.3 Reneging 8.11.4 Priorities and Priority Queues 8.12 Modeling Routing in Multiple Paths and Parallel Paths 8.12.1 Multiple Paths 8.12.2 Parallel Paths 8.13 Model Documentation and Enhancements 8.14 Summary Discussion Questions and Exercises References 9. INPUT AND OUTPUT DATA ANALYSIS 9.1 Dealing with Randomness 9.2 Characterizing Probability Distributions of Field Data 9.2.1 Goodness-of-Fit Tests 9.2.2 Using Stat::Fit for Distribution Fitting 9.2.3 Choosing a Distribution in the Absence of Sample Data 9.3 Random Number Generators 9.3.1 The Runs Test 9.4 Generation of Random Variates 9.5 Analysis of Simulation Output Data 9.5.1 Nonterminating Processes 9.5.2 Terminating Processes 9.5.3 Confidence Intervals 9.5.4 Sample Size Calculation 9.5.5 Comparing Output Variables for Different Process Designs 9.6 Modeling and Analysis of Process Design Cases 9.6.1 Process Design of a Call Center for Software Support 9.6.2 Design of a Hospital Admissions Process 9.7 Summary 9.8 Training cases 9.8.1 CASE 1: IMPROVING THE X-RAY PROCESS AT COUNTY HOSPITAL 9.8.2 CASE 2: PROCESS MODELING AND ANALYSIS IN AN ASSEMBLY FACTORY 9.8.3 CASE 3: REDESIGN OF A CREDIT APPLICATIONS PROCESS 9.8.4 CASE 4: REDISIGNING THE ADOPTION PROCESS IN A HUMANE SOCIETY 9.8.5 CASE 5: PERFORMANCE ANALYSIS AND IMPROVEMENT OF AN INTERNET ORDERING PROCESS Appendix 9A: Hypothesis Testing, Confidence Intervals, and Statistical Tables 9A.1 Goodness-of-Fit Tests 9A.1.1 The Chi-Square Test 9A.1.2 The Kolmogorov-Smirnov Test 9A.2 Confidence Interval for a Population Proportion 9A.3 Hypothesis Testing 9A.4 Statistical Tables Exercises References 10. PRESCRIPTIVE ANALYTICS FOR PROCESS PERFORMANCE OPTIMIZATION 10.1 Identifying the Main Drivers of Process Performance 10.1.1 Factorial Design for Simulation Models 10.1.2 Illustrative Example of Design of Experiments 10.2 Business Process Optimization 10.3 The Role of Simulation Optimization in Business Process Management 104 Simulation-Optimization with ExtendSim 10.4.1 Tutorial: Process Optimization with ExtendSim 10.4.2 Alternative Optimization Models 10.5 Optimization of Process Simulation Models 10.5.1 Configuring a Hospital Emergency Room Process 10.5.2 Staffing Levels for a Personal Insurance Claims Process 10.6 Summary Appendix 10A: Evolutionary Computation Exercises Simulation-Optimization Projects Project 1: Emergency Room Staffing Project 2: Call Center Configuration Project 3: Loan Application Process Project 4: Process with Multiple Job Types and Deadlines References 11. BUSINESS PROCESS ANALYTICS 11.1 Competing on Analytics 11.2 Business Process Management Systems 11.2.1 Business Rules 11.2.2 Monitor and Control 11.2.3 Process Mining 11.3 Machine Learning 11.3.1 Support Vector Machines 11.3.2 k-Nearest Neighbor Classifier 11.3.3 Neural Networks 11.3.4 Classification Problems in Business Processes Discussion Questions and Exercises References

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Author Information

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.

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