Chapman and Hall/CRC Business Process Analytics: Modeling, Simulation and Design (Textbooks in Mathematics)

The fourth edition of this widely used textbook offers a new perspective. Previously titled Business Process Modeling, Simulation and Design, as the new title suggests, this book is about analytical business process modeling and design. However, this new edition introduces analytics to the title and to the presentation. The main objective of this book is to provide students with a comprehensive understanding of the multitude of analytical tools that can be used to model, analyze, understand, and ultimately design business processes. The most flexible and powerful of these tools, although not always the most appropriate, is discrete-event simulation. The wide range of approaches covered in this book include graphical flowcharting tools, deterministic models for cycle time analysis and capacity decisions, and analytical queuing methods, as well as machine learning. The authors focus on business processes as opposed to just manufacturing processes or general operations management problems and emphasize on simulation modeling using state-of-the-art commercial simulation software. Business Process Analytics: Modeling, Simulation, and Design can be thought of as a hybrid between traditional books on process management, operations management, and simulation. The growing interest in simulation-based tools suggests that an understanding of simulation modeling, its potential as well as its limitations for analyzing and designing processes, is of key importance to students looking for a future career in operations management. Changes from the previous edition include the following: New section on data-driven process improvement (with data visualization) Added a subsection of control charts to the 6-sigma section Replaced business process reengineering with business process management Updated all text, figures, examples, and exercises to ExtendSim10 (current version) More coverage on design of experiments More coverage of machine learning and neural networks