Preface

As the title suggests, this book is about analytical business process modeling and design. It is the result of several years of teaching a business process design and simulation course at the undergraduate and the MBA levels in the Leeds School of Business at the University of Colorado at Boulder. We have also drawn from one of the author’s experiences delivering a simulation course in the Industrial Engineering and Management program at Lund University in Sweden.

The main motivation for writing this textbook stems from our struggle to find a book that approaches business process design from a broad, quantitative modeling perspective. The main objective with this book is thus 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. We believe that 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 the use of data envelopment analysis (DEA) for benchmarking purposes. What distinguishes this textbook from general operations management (OM) books, most of which cover many of the same topics, is its focus on business processes as opposed to just manufacturing processes or general OM problems, and its emphasis on simulation modeling using state-of-the-art commercial simulation software. Essentially, Business Process Modeling, Simulation, and Design can be thought of as a hybrid between traditional books on process management, operations management, and simulation. Although it would be desirable for all students in operations management to take several courses in each of these topics, the reality is that few business school curricula today allow that. In our experience, simulation especially tends to be shoved to the side simply because it is perceived to be too technical. However, our firm belief, manifested by our writing of this book, is that this need not and should not be the case. The rapid development of user-friendly simulation software with graphical interfaces has made the use of simulation accessible even to those lacking computer programming skills, and it provides a great medium for illustrating and understanding implications of capacity restrictions and random variation on process performance. Furthermore, the growing interest in simulation-based tools in industry suggests that an understanding of simulation modeling, its potential as well as its limitations for analyzing and designing processes, is of key importance for students looking for a future career in operations management.

Before proceeding with a discussion of how we picture this book being used, it is worthwhile to summarize what the book is not. It is not a traditional, qualitatively oriented book on process management, although we dedicate one chapter to these important issues. It is not a traditional book on operations management, although it covers some of the traditional tools found in most OM books. Furthermore, it is not a traditional book on simulation, although discrete event simulation is used extensively. It is a book that attempts to bring these topics together by placing an analytical modeling perspective on process design and particularly emphasizing the power of simulation modeling as a vehicle for analyzing and designing business processes.

This textbook is ideal for a one-semester undergraduate or MBA course within an operations management business school curriculum. The MBA course typically would cover some of the more advanced topics in greater depth, such as DEA (which does not require that students have some previous exposure to linear programming, but it is helpful) simulation optimization, and queuing. The MBA course also could include a more involved simulation project. In addition, we envision this book being used for an undergraduate course in industrial engineering or within an engineering management program. In terms of requirements, the textbook assumes that students have taken a basic course in business statistics. However, if students have been exposed to a! basic course in operations management and have some prior knowledge of quantitative techniques, this gives an additional opportunity to explore some of the topics covered in this textbook in more detail. In terms of how to cover the material, we recommend following the chapter sequence.

The text is organized as follows.

• Chapter 1 sets the stage by defining what we mean by a business process and business process design. It also points to the importance of these issues for overall business performance and the organization’s strategic positioning.
• Chapter 2 explains some fundamental principles for successful process management and also takes a closer look at two of the more influential, process-oriented improvement programs in recent years: Six Sigma and Business Process Reengineering. The rationale is that although the focus of the book is on analytical modeling and design rather than implementation and change management, even the best possible design is of little use if it is not implemented and managed properly.
• Chapter 3 presents a simulation-based methodology for business process design projects. The approach identifies a number of steps or issues that typically need to be dealt with during a process design project from initialization to implementation. The approach can be seen as a road map to the remainder of the book in the sense that the tools and methods discussed in the following chapters can be used to address some of the specified issues.
• Chapter 4 deals with a range of basic tools for analyzing and designing processes that display limited variability in demand and activity times (i.e., deterministic as opposed to stochastic models). The first part of the chapter discusses several graphical tools for charting and describing processes. These tools are particularly useful for understanding existing processes. The second part of the chapter investigates seven fundamental process design principles and associated methods of analysis.
• Chapter 5 focuses on how to manage process flows, particularly with regard to cycle time and capacity analysis. As in Chapter 4, we consider only deterministic situations with perfect information regarding demand, resource availability, and activity times.
• Chapter 6 introduces queuing and simulation as means for explicitly incorporating randomness and variability into the process analysis. The queuing sections discuss queuing strategies, properties of the exponential distribution, the Poisson process, traditional birth and death processes, and the corresponding queuing models including single and multiple servers with and without restrictions on the queuing population and/or the queue lengths. We also discuss waiting/shortage costs and applications to process design situations. The simulation sections introduce the concept of simulation and illustrate it by a simple spreadsheet model of a single-server queuing model.
• Chapter 7 provides an introduction to the simulation software Extend that is used for simulation modeling. The focus is on how to get a model up and running, including working with simple animation, modeling random processing times, and limited labor resources. The chapter also discusses how to collect data and use the built-in tools for statistical analysis.
• Chapter 8 continues exploring how to use Extend to model more complex business processes and capture features like prioritization, attribute assignment, blocking, balking and reneging queues, routing through multiple and parallel paths, batching, resource allocation, activity-based costing, and cycle time analysis.
• Chapter 9 deals with the important issue of statistical analysis of input and output data. Topics covered include determination of input data distributions, random number generation, and how to analyze output data. Of particular importance is how to compare the performance of alternative process designs.
• Chapter 10 discusses state-of-the-art methods for optimizing design parameters using simulation.
• Chapter 11 shows how to use data envelopment analysis (DEA) for benchmarking purposes. The chapter is accompanied by an Excel add-in tool for solving the underlying linear programming models.

The simulation modeling is done entirely with the Extend software, enabling the students to work hands-on with models of their own. A potential criticism associated with this software-integrated approach is that the acquired knowledge may be thought of as being software dependent. However, our experience is that after initially investing some time in understanding the software, the main challenge and learning lie in creating abstract models of processes, a conceptual framework that is software independent. Furthermore, because the functionality of most modern simulation software is very similar, exposure to one product promotes a valuable understanding of the potential and limitations of simulation modeling in general.

The textbook includes a CD with a student version of Extend OR V6 and the Excel add-in for solving DEA problems. For instructors, an additional CD is available that includes solutions to end-of-chapter problems, prebuilt Extend models, and some additional simulation exercises/projects with suggested solutions.

There are many individuals among our colleagues, families, and friends who have contributed to this book as sources of inspiration, encouragement, and support. Our deepest gratitude goes to all of you. Others have had a more direct influence on this book, and we take this opportunity to express our appreciation for their valuable help. Special thanks go to Pat Diamond and others at Imagine That Inc., who worked with us on the simulation models and allowed us to distribute the student version of Extend LT with our text. We would also like to thank the following reviewers, who provided many valuable suggestions in shaping this textbook:

George E. Monahan, University of Illinois

Charles Munson, Washington State University

C. Surrender Reddy, Saginaw Valley State University

Joseph Sarkis, Clark University

Shane J. Schvaneveldt, Weber State University

Our gratitude also goes to past and present students in the undergraduate and graduate programs at the Leeds School of Business who have helped shape this book into what it is today. Special thanks go to Marco Better for helping us put together the instructor’s manual. We are indebted to our Leeds School of Business colleagues Thomas Vossen, Cathleen S. Burns, and David E. Monarchi for providing helpful criticism and feedback on earlier drafts.

And thank you to Tom Tucker at Prentice Hall who encouraged us to pursue this project and provided help and advice along the way. Finally, our thanks to Maureen Wilson and Dawn Stapleton at Prentice Hall, and Jennifer Welsch at BookMasters, who capably guided our manuscript through production. Thank you for making this book a reality!