Lean thinking, as well as associated processes and tools, have involved into a ubiquitous perspective for improving systems particularly in the manufacturing arena. With application experience has come an understanding of the boundaries of lean capabilities and the benefits of getting beyond these boundaries to further improve performance. Discrete event simulation is recognized as one beyond-the-boundaries of lean technique. Thus, the fundamental goal of this text is to show how discrete event simulation can be used in addition to lean thinking to achieve greater benefits in system improvement than with lean alone.

Realizing this goal requires learning the problems that simulation solves as well as the methods required to solve them. The problems that simulation solves are captured in a collection of case studies. These studies serve as metaphors for industrial problems that are commonly addressed using lean and simulation.

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The text attempts to make simulation accessible to as many students and other professionals as possible. Experience seems to indicate that students learn new methods best when they are presented in the context of realistic applications that motivate interest and retention. Only the most fundamental simulation statistical methods, as defined in Law (2007) are presented. For example, the t-confidence interval is the primary technique employed for the statistical analysis of simulation results. References to more advanced simulation statistical analysis techniques are given as appropriate. Only the most basic simulation modeling methods are presented, plus extensions as needed for each particular application study.

The text is intended to help prepare those who read it to effectively perform simulation applications.