| Supporting manufacturing with simulation: model design, development, and deployment |
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Winter Simulation Conference
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Proceedings of the 28th conference on Winter simulation
table of contents
Coronado, California, United States
Pages: 114 - 121
Year of Publication: 1996
ISBN:0-7803-3383-7
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Authors
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Frank Chance
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Chance Industrial Solutions, 6900G Lake Drive, Dublin, California
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Jennifer Robinson
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University of Massachusetts, 581 VFW Parkway, Chestnut Hill, Massachusetts
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John W. Fowler
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Arizona State University, Dept. of I.M.S.E., Box 875906, Tempe, Arizona
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IEEE Computer Society
Washington, DC, USA
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| Bibliometrics |
Downloads (6 Weeks): 2, Downloads (12 Months): 20, Citation Count: 6
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ABSTRACT
In this paper, we identify and discuss the features we believe are key to the successful use of simulation as a manufacturing support tool. The discussion begins with three sample projects drawn from the authors' industrial and consulting experiences. Using these projects as motivation, we discuss the ideal project lifecycle model design, development, and deployment. For model design, we emphasize the importance of a clear and consistent specification, articulated in a written document. This specification should identify project customers, goals, and deliverables. We next review a range of model development options, stressing the existence of many non-simulation alternatives. We also discuss methods for model verification and validation. Finally, we consider the difficulties of model deployment, including simulation output analysis, data maintenance, and model integration. We close with several suggestions on how best to present simulation results to a management audience.
REFERENCES
Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.
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Leachman, R., R. F, Benson, C. Liu, and D. J. Rarr. 1996. IMPRESS: An Automated Production Planning and Delivery Quotation System at Harris Corporation - Semiconductor Sector. Interfaces 26# 1 (Jan/Feb): 6- 37.
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Nelson, B. L. 1992. Statistical Analysis of Simulation Results. In Handbook of Industrial Engineering, 2nd Edition: 2567-2593.
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Schruben, L. W. 1980. Establishing the Credibility of Simulation. Simulation 34: 101-105.
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Schruben, L. W., H. Singh, and L. Tiemey. 1983. Optimal Tests for Initialization Bias in Simulation Output. Operations Research 31:1167-1178.
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CITED BY 6
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Andreas Peikert , Josef Thoma , Steven Brown, A rapid modeling technique for measurable improvements in factory performance, Proceedings of the 30th conference on Winter simulation, p.1011-1016, December 13-16, 1998, Washington, D.C., United States
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Navdeep S. Grewal , Alvin C. Bruska , Timbur M. Wulf , Jennifer K. Robinson, Validating simulation model cycle times at Seagate Technology, Proceedings of the 31st conference on Winter simulation: Simulation---a bridge to the future, p.843-849, December 05-08, 1999, Phoenix, Arizona, United States
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Juergen Potoradi , Gerald Winz , Lee Weng Kam, Determining optimal lot-size for a semiconductor back-end factory, Proceedings of the 31st conference on Winter simulation: Simulation---a bridge to the future, p.720-726, December 05-08, 1999, Phoenix, Arizona, United States
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Joerg Domaschke , Steven Brown , Jennifer Robinson , Franz Leibl, Effective implementation of cycle time reduction strategies for semiconductor back-end manufacturing, Proceedings of the 30th conference on Winter simulation, p.985-992, December 13-16, 1998, Washington, D.C., United States
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