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 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 on
- Business process simulation and how it can enable business analytics
- How business process simulation can provide speed, cost, dependability, quality, and flexibility metrics
- 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
- State-of-the-art developments in business process simulation regarding the use of big data, simulating advanced services and modeling people’s behavior
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 performance is improved.
|Product dimensions:||6.69(w) x 9.45(h) x (d)|
|Age Range:||18 Years|
About the Author
Andrew Greasley lectures in Simulation Modeling and Operations Management at Aston Business School, Birmingham, UK. He has taught in the UK, Europe, and Africa for a number of institutions. Dr. Greasley has over 100 publications including five books (Operations Management, Wiley; Operations Management: Sage Course Companion, Sage; Simulation Modeling for Business, Ashgate; Business Information Systems, Pearson Education (co-author); and Enabling a Simulation Capability in the Organisation, Springer Verlag). He has provided a simulation modeling consultancy service for 30 years to a number of companies in the public and private sectors including ABB Transportation Ltd. (now Bombardier), Derbyshire Constabulary, GMT Hunslet Ltd., Golden Wonder Ltd., Hearth Woodcraft Ltd., Luxfer Gas Cylinders Ltd., Pall-Ex Holdings Ltd., Rolls Royce Ltd. (Industrial Power Group), Stanton Valves Ltd., Tecquipment Ltd., Textured Jersey Ltd. and Warwickshire Police.
Table of Contents
- Introduction to Process Simulation (10000)
- Why is Process Simulation Needed for Analytics? (5000)
- Enabling a Process Simulation Capability (5000)
- Undertaking a Process Simulation Study (25000)
- Process Simulation Case Studies (30000)
- Challenges and the Future (15000)
Introduction to Simulation in Business
The 3 main types of Simulation – Process Simulation (Discrete Event Simulation), Agent Based Modeling and System Dynamics
The History of Process Simulation
Application Areas of Process Simulation
Static (LP, Regression, Forecasting) vs. Dynamic (Process Simulation) Modeling
Variability and Interdependence of Processes in Business Systems
What-If vs Optimization of Processes with Analytics
Analytics Performance Metrics – Speed, Cost, Dependability, Quality, Flexibility
Selection of process simulation software and training needs
Preparing a Process Simulation Proposal
Building the Model
The booking clerk: An illustrative example
Speed: Warwickshire Police Force
Cost: Derbyshire Police Force
Dependability: Pallex Ltd.
Quality: Jinsheng Group Ltd.
Flexibility: Golden Wonder Ltd.
Modeling People’s behavior
Incorporating Big Data
Servitization and Advanced Services