Simulating Business Processes for Descriptive, Predictive, and Prescriptive Analytics - Andrew Greasley

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Résumé :
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 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 generation of simulation analytics using process mining 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 that performance is improved.

Biographie:
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...

Sommaire:

1. Introduction to Process Simulation (10000)

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

2. Why is Process Simulation Needed for Analytics? (5000)

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

3. Enabling a Process Simulation Capability (5000)

Selection of process simulation software and training needs

Preparing a Process Simulation Proposal

4. Undertaking a Process Simulation Study (25000)

Data Collection

Process mapping

Input Modeling

Building the Model

Output Modeling

The booking clerk: An illustrative example

5. Process Simulation Case Studies (30000)

Speed: Warwickshire Police Force

Cost: Derbyshire Police Force

Dependability: Pallex Ltd.

Quality: Jinsheng Group Ltd.

Flexibility: Golden Wonder Ltd.

6. Challenges and the Future (15000)

Modeling People's behavior

Incorporating Big Data

Servitization and Advanced Services