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
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.
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
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.
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
Data Collection
Process mapping
Input Modeling
Building the Model
Output Modeling
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