Industrial Engineering and Management Systems

Korean Institute of Industrial Engineers (대한산업공학회)
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Optimizing Concurrent Spare Parts Inventory Levels for Warships Under Dynamic Conditions

  • Received : 2016.08.13
  • Accepted : 2017.02.16
  • Published : 2017.03.30
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Abstract

The inventory level of concurrent spare parts (CSP) has a significant impact on the availability of a weapon system. A failure rate function might be of particular importance in deciding the CSP inventory level. We developed a CSP optimization model which provides a compromise between purchase costs and shortage costs on the basis of the Weibull and the exponential failure rate functions, assuming that a failure occurs according to the (non-) homogeneous Poisson process. Computational experiments using the data obtained from the Korean Navy identified that, throughout the initial provisioning period, the optimization model using the exponential failure rate tended to overestimate the optimal CSP level, leading to higher purchase costs than the one using the Weibull failure rate. A Pareto optimality was conducted to find an optimal combination of these two failure rate functions as input parameters to the model, and this provides a practical solution for logistics managers.

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References

  1. Abernethy, R. B. (2001), The new Weibull Handbook: Reliability & Statistical Analysis for Predicting Life, Safety, Survivability, Risk, Cost and Warranty Claims, 4th edn., North Palm Beach, FL, USA, Robert B. Abernethy.
  2. Das, K. (2008), A comparative study of exponential distribution vs weibull distribution in machine reliability analysis in a CMS design, Computer & Industrial Engineering, 54, 12-33. https://doi.org/10.1016/j.cie.2007.06.030
  3. Feldman, A. M. and Serrano, R. (1980), Welfare Economics and Social Choice Theory, 2nd edn., Boston, Kluwer Academic Publisher.
  4. Gams Development Corporation (2014), The General Algebraic Modeling System (GAMS).
  5. Graves, S. C. (1985), A multi-echelon inventory model for a repairable item with one-for-one replenishment, Management Science, 31, 1247-1256. https://doi.org/10.1287/mnsc.31.10.1247
  6. Hillestad, R. J. (1982), Dyna-METRIC: Dynamic Multiechelon Technique for Recoverable Item Control, Santa Monica, CA, USA, The RAND Corporation.
  7. Kaplan, A. J. (1987), Incorporating Redundancy Considerations Into Sotckage models, Philadelphia, PA, USA, US Army Inventory Research Office.
  8. Kim, M. and Kim, K. (2010), A study on determining the optimal number of concurrent spare parts under performance based logistics. Korean Institute of Industrial Engineering and Korean Operations Research and Management Science Society Spring Conference. Je-ju, ROK, Korean Institute of Industrial Engineering and Korean Operations Research and Management Science.
  9. Menhert, C. F. (1983), An Evaluation of the United States Army SESAME and Swedish OPUS VII Provisioning Models, Monterey, CA, USA, Naval Postgraduate School.
  10. Moon, S., Hicks, C., and Simpson, A. (2012), The development of a hierarchical forecasting method for predicting spare parts demand in the South Korean navy: A case study, International Journal of Production Economics, 140, 794-802. https://doi.org/10.1016/j.ijpe.2012.02.012
  11. Moon, S., Simpson, A., and Hicks, C. (2013), The development of a classification model for predicting the performance of forecasting methods for naval spare parts demand, International Journal of Production Economics, 143, 449-454. https://doi.org/10.1016/j.ijpe.2012.02.016
  12. Paik, S.-H., Chang, C., Gook, J., and Oh, S. (2012), The RAM Analysis Report for DDH-I/II Using Field Data, Seoul, Defense Agency for Technology and Quality.
  13. Rausand, M. and Hoyland, A. (2004), System reliability theory: models, statistical methods, and applications, Hoboken, New Jersey, USA, John Wiley & Son.
  14. Relex scandinavia (2014), Relex 7.5.
  15. Sherbrooke, C. C. (1968), Metric: A multi-echelon technique for recoverable item control, Operations Research, 16, 122-141. https://doi.org/10.1287/opre.16.1.122
  16. Sherbrooke, C. C. (1986), Vari-metric: Improved approximations for multi-indenture, multi-echelon availability models, Operations Research, 34, 311-319. https://doi.org/10.1287/opre.34.2.311
  17. Sherbrooke, C. C. (2004), Optimal Inventory Modeling of Systems: Multi-Echelon Techniques, New York, Kluwer Academic Publishers.
  18. Slack, N., Chambers, S., and Johnston, R. (2010), Operations Management, 6th edn., Essex, England, Prentice Hall.
  19. Slay, F. M., Bachman, T. C., Kline, R. C., O'malley, T. J., Eichorn, F. L., and King, R. M. (1996), Optimizing Spares Support: The Aircraft Sustainability Model, McLean, VA, USA, Logistics Management Institute.
  20. Tan, Z. (2009), A new approach to MLE of weibull distribution with interval data, Reliability Engineering and System Safety, 94, 394-403. https://doi.org/10.1016/j.ress.2008.01.010
  21. Verrijdt, J. H. C. M. and Kok, A. G. D. (1996), Distribution planning for a divergent depotless two-echelon network under service constraints, European Journal of Operational Research, 89, 341-354. https://doi.org/10.1016/0377-2217(94)00242-8
  22. Wang, Y., Jia, Y., and Jiang, W. (2001), Early failure analysis of machining centers: A case study, Reliability Engineering and System Safety, 72, 91-97. https://doi.org/10.1016/S0951-8320(00)00100-9
  23. Wessels, W. R. (2007), Use of the Weibull versus exponential to model part reliability. Proceedings Annual Reliability and Maintainability Symposium, Orlando, FL. USA.
  24. Xie, M. and Lai, C. D. (1996), Reliability analysis using an additive weibull model with bathtub-shaped failure rate function, Reliability Engineering and System Safety, 52, 87-93. https://doi.org/10.1016/0951-8320(95)00149-2
  25. Yoon, H. and Lee, S. (2011), The effect analysis of the improved vari-metric in multi-echelon inventory model, Korean Management Science Review, 28, 117-127.
  26. Yoon, K.-B. and Sohn, S.-Y. (2007), Finding the optimal CSP inventory level for multi-echelon system in Air Force using random effects regression model, European Journal of Operational Research, 180, 1076-1085. https://doi.org/10.1016/j.ejor.2006.05.006

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