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Supporting manufacturing with simulation: model design, development, and deployment
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Source Winter Simulation Conference archive
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
Authors
Frank Chance  Chance Industrial Solutions, 6900G Lake Drive, Dublin, California
Jennifer Robinson  University of Massachusetts, 581 VFW Parkway, Chestnut Hill, Massachusetts
John W. Fowler  Arizona State University, Dept. of I.M.S.E., Box 875906, Tempe, Arizona
Sponsors
INFORMS/CS : Computer Science TC
SIGSIM: ACM Special Interest Group on Simulation and Modeling
IIE : Institute of Industrial Engineers
SCS : Society for Computer Simulation
ASA : American Statistical Association
NIST : National Institue of Standards & Technology
IEEE-CS : Computer Society
IEEE-SMCS : Systems, Man & Cybernetics Society
ACM: Association for Computing Machinery
Publisher
IEEE Computer Society  Washington, DC, USA
Bibliometrics
Downloads (6 Weeks): 6,   Downloads (12 Months): 23,   Citation Count: 6
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abstract   references   cited by   collaborative colleagues  

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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.
 
3
Nelson, B. L. 1992. Statistical Analysis of Simulation Results. In Handbook of Industrial Engineering, 2nd Edition: 2567-2593.
 
4
Schruben, L. W. 1980. Establishing the Credibility of Simulation. Simulation 34: 101-105.
 
5
Schruben, L. W., H. Singh, and L. Tiemey. 1983. Optimal Tests for Initialization Bias in Simulation Output. Operations Research 31:1167-1178.

CITED BY  6
Collaborative Colleagues:
Frank Chance: colleagues
Jennifer Robinson: colleagues
John W. Fowler: colleagues