Simulating business processes for descriptive, predictive, and prescriptive analytics / Andrew Greasley.
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 sh...
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| Language: | English |
| Published: |
[Boston, Mass.?] :
De G Press,
[2019]
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| Physical Description: | x, 341 pages : illustrations ; 24 cm |
| Format: | Book |
Contents:
- Frontmatter
- Preface
- Acknowledgments
- About the author
- part 1: Understanding simulation and analytics. Analytics and simulation basics
- Simulation and business processes
- Build the conceptual model
- Build the simulation
- Use simulation for descriptive, predictive and prescriptive analytics
- part 2: Simulation case studies. Case study: a simulation of a police call center
- Case study: A simulation of a "Last Mile" logistics system
- Case Study: A simulation of an enterprise resource planning system
- Case study: A simulation of a snacks process production system
- Case study: A simulation of a police arrest process
- Case study: A simulation of a food retail distribution network
- Case study: A simulation of a proposed textile plant
- Case study: A simulation of a road traffic accident process
- Case study: A simulation of a rail carriage maintenance depot
- Case study: A simulation of a rail vehicle bogie production facility
- Case study: A simulation of advanced service provision
- Case study: Generating simulation analytics with process mining
- Chapter 18. Case study: Using simulation with data envelopment analysis
- Case study: Agent-based modeling in discrete-event simulation
- Appendix A
- Appendix B
- Index.