ACM Home Page
Please provide us with feedback. Feedback
A design of experiments approach to readiness risk analysis
Full text PdfPdf (468 KB)
Source Winter Simulation Conference archive
Proceedings of the 38th conference on Winter simulation table of contents
Monterey, California
SESSION: Military applications: military analysis II table of contents
Pages: 1332 - 1339  
Year of Publication: 2006
ISBN:1-4244-0501-7
Authors
Keebom Kang  Graduate School of Business & Public Policy, Naval Postgraduate School, Monterey, CA
Kenneth H. Doerr  Graduate School of Business & Public Policy, Naval Postgraduate School, Monterey, CA
Susan M. Sanchez  Graduate School of Business & Public Policy, Naval Postgraduate School, Monterey, CA
Sponsors
IEICE ESS : Institute of Electronics, Information and Communication Engineers, Engineering Sciences Society
IIE : Institute of Industrial Engineers
ASA : American Statistical Association
IEEE-CS\DATC : The IEEE Computer Society
INFORMS-CS : Institute for Operations Research and the Management Sciences-College on Simulation
NIST : National Institute of Standards and Technology
SIGSIM: ACM Special Interest Group on Simulation and Modeling
(SCS) : The Society for Modeling and Simulation International
Publisher
Winter Simulation Conference 
Bibliometrics
Downloads (6 Weeks): 4,   Downloads (12 Months): 33,   Citation Count: 1
Additional Information:

abstract   references   cited by   collaborative colleagues  

Tools and Actions: Review this Article  

ABSTRACT

We develop a simulation model to aid in identifying and evaluating promising alternatives to achieve improvements in weapon system-level availability when services for system components are outsourced. Two outcomes are valued: improvements in average operational availability for the weapon system, and reductions in the probability that operational availability of the weapon system falls below a given planning threshold (readiness risk). In practice, these outcomes must be obtained through performance-based agreements with logistics providers. The size of the state space, and the non-linear and stochastic nature of the outcomes, precludes the use of optimization approaches. Instead, we use designed experiments to evaluate simulation scenarios in an intelligent way. This is an efficient approach that enables us to assess average readiness and readiness risk outcomes of the alternatives, as well as to identify the components and logistics factors with the greatest impact on operational availability.


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.

 
1
Apgar IV, M. and J. M. Keane. 2004. New business with the new military. Harvard Business Review 82 (9): 45--56.
 
2
ASN-RDA {Assistant Secretary of the Navy - Research, Development and Acquisition}. 2003. Performance Based Logistics Guidance Document. Unpublished manuscript.
 
3
Berkowitz, D., J. N. D. Gupta, J. T. Simpson, J. McWilliams, L. Delayne, B. Brown, D. Cameron, and T. Sparks. 2003. Performance Based Logistics. Center for the Management of Science and Technology, Huntsville, AL.
 
4
Blanchard, B. S., D. Verma, and E. L. Peterson. 1995. Maintainability: A Key to Effective Serviceability and Maintenance Management. John Wiley & Sons, New York.
 
5
Cassady, C. R., E. A. Pohl, and S. Jin. 2004. Managing availability improvement efforts with importance measures and optimization. IMA Journal of Management Mathematics 15 (2): 161--174.
 
6
Cioppa, T. M. and T. W. Lucas. 2006. Efficient nearly orthogonal and space-filling Latin hypercubes. Technometrics, forthcoming.
 
7
 
8
Doerr, K. H., I. A. Lewis, and D. R. Eaton. 2005. Measurement issues in performance based logistics. Journal of Public Procurement 5 (2): 164--186.
 
9
Eaton, D. R., K. H. Doerr, and I. A. Lewis (in press). Performance based logistics: A warfighting focus, U. S. Naval Institute Proceedings.
 
10
Ebeling, C. E. 1996. Reliability and maintainability model (RAM). Report NASA-CR-203254, National Aeronautics and Space Administration Langley Research Center.
 
11
GAO {Government Accountability Office}. 2004. Opportunities to enhance the implementation of performance based logistics. Report of the Subcommittee on Readiness and Management Support, Committee on Armed Services, U. S. Senate. August 2004.
 
12
Kang, K., K. H. Doerr, M. Boudreau, and U. Apte. 2005. A decision support model for valuing proposed improvements in component reliability. Sponsored report NPS-LM 05--008, Naval Postgraduate School, Monterey, CA.
 
13
Kececioglu, D. 1991. Reliability Engineering Handbook, V. 2. Prentice-Hall, Englewood Cliffs, NJ.
 
14
Kim, S. H., M. A. Cohen, and S. Netessine. 2006. Performance contracting in after-sales service supply chains. Working Paper, Wharton School of Business, University of Pennsylvania.
 
15
Kleijnen, J. P. C., S. M. Sanchez, T. W. Lucas, and T. M. Cioppa. 2005. A user's guide to the brave new world of designing simulation experiments. INFORMS Journal on Computing 17 (3): 263--289 (with online companion).
 
16
Prueitt, G. C. and C. S. Park. 1997. Phased capacity expansion - using continuous distributions to model prior belief. The Engineering Economist 42 (2): 91--110.
 
17
 
18
 
19
SAS. 2002. JMP User's Guide, Version 5: SAS Institute Inc. Cary, NC.
 
20
Slay, F. M., T. C. Bachman, R. C. Kline, T. J. O'Malley, F. L. Eichorn, and R. M. King. 1996. Optimizing spares support: The aircraft sustainability model. Report AF501MRI, LMI Corporation, McClean, VA.
 
21

Collaborative Colleagues:
Keebom Kang: colleagues
Kenneth H. Doerr: colleagues
Susan M. Sanchez: colleagues