Simulating Business Processes for Descriptive, Predictive, and Prescriptive Analytics

Andrew Greasley

• Publisher: De Gruyter
• Year: 2019
• Print ISBN: 9781547416745
• eBook ISBN: 9781547400690
• Edition: 1
• Topic: Business
• Subtopic: Business Strategy
• Index: Business

This book outlines the benefits and limitations of simulation, what is involved in setting up a simulation capability in an organization, the steps involved in developing a simulation model and how to ensure that model results are implemented. In addition, detailed example applications are provided to show where the tool is useful and what it can offer the decision maker.

In Simulating Business Processes for Descriptive, Predictive, and Prescriptive Analytics, Andrew Greasley provides an in-depth discussion of

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• Business process simulation and how it can enable business analytics

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• How business process simulation can provide speed, cost, dependability, quality, and flexibility metrics

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• Industrial case studies including improving service delivery while ensuring an efficient use of staff in public sector organizations such as the police service, testing the capacity of planned production facilities in manufacturing, and ensuring on-time delivery in logistics systems

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• State-of-the-art developments in business process simulation regarding the generation of simulation analytics using process mining and modeling people's behavior

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Managers and decision makers will learn how simulation provides a faster, cheaper and less risky way of observing the future performance of a real-world system. The book will also benefit personnel already involved in simulation development by providing a business perspective on managing the process of simulation, ensuring simulation results are implemented, and that performance is improved.

Table of contents

• Preface
• Acknowledgments
• About the Author
• Contents
• Part 1: Understanding Simulation and Analytics
• Chapter 1. Analytics and Simulation Basics
• Chapter 2. Simulation and Business Processes
• Chapter 3. Build the Conceptual Model
• Chapter 4. Build the Simulation
• Chapter 5. Use Simulation for Descriptive, Predictive and Prescriptive Analytics
• Part 2: Simulation Case Studies
• Chapter 6. Case Study: A Simulation of a Police Call Center
• Chapter 7. Case Study: A Simulation of a “Last Mile” Logistics System
• Chapter 8. Case Study: A Simulation of an Enterprise Resource Planning System
• Chapter 9. Case Study: A Simulation of a Snacks Process Production System
• Chapter 10. Case Study: A Simulation of a Police Arrest Process
• Chapter 11. Case Study: A Simulation of a Food Retail Distribution Network
• Chapter 12. Case Study: A Simulation of a Proposed Textile Plant
• Chapter 13. Case Study: A Simulation of a Road Traffic Accident Process
• Chapter 14. Case Study: A Simulation of a Rail Carriage Maintenance Depot
• Chapter 15. Case Study: A Simulation of a Rail Vehicle Bogie Production Facility
• Chapter 16. Case Study: A Simulation of Advanced Service Provision
• Chapter 17. Case Study: Generating Simulation Analytics with Process Mining
• Chapter 18. Case Study: Using Simulation with Data Envelopment Analysis
• Chapter 19. Case Study: Agent-Based Modeling in Discrete-Event Simulation
• Appendix A
• Appendix B
• Index