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Beyond Lean: Simulation in Practice

Beyond Lean: Simulation in Practice

Beyond Lean: Simulation in Practice

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Year:2011
Publisher:Grand Valley State University
Pages:318 pages
Language:english
Since:21/06/2017
Size:5.50 MB
License:CC-BY-NC-SA

Content:

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.

Learning simulation requires doing simulation. Thus, a case problem is associated with each case study. Each case problem is designed to be a challenging and less than straightforward extension of the case study. Thus, solving the case problem using simulation requires building on and extending the information and knowledge gleaned from the case study. In addition, questions are provided with each case problem so that it may be discussed in a way similar to the traditional discussion of case problems used in business schools, for example.

An understanding of simulation methods is prerequisite to the case studies. A simulation project process, basic simulation modeling methods, and basic simulation experimental methods are presented in the first part of the text. An overview of how a simulation model is executed on a computer is provided. A discussion of how to select a probability distribution function to model a random quantity is included. Exercises are included to provide practice in using the methods.

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