Spare Parts Inventory Control for Non-Repairable Items Based on Prognostics and Health Monitoring Information

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Published Mar 26, 2021
Leonardo Ramos Rodrigues Takashi Yoneyama

Abstract

The application of PHM (Prognostics and Health Monitoring) techniques can provide a wide range of benefits to aircraft operators. Since the primary goal of PHM systems is to estimate the health state of components and equipments, as well as forecasting their RUL (Remaining Useful Life), they are often closely associated with the reduction in the number of unscheduled maintenance tasks. Indeed, the avoidance of unscheduled maintenance is a very important factor, but this technology may potentially lead to considerable further savings in other fields. The usage of PHM information by the logistics team for spare parts inventory control is a good example to illustrate that a PHM system can potentially provide benefits for other teams besides the maintenance team. The purpose of this work is to present a comparison between two different inventory control policies for non-repairable parts in terms of average total cost required and service level achieved. The well known [R, Q] (re-order point, economic order quantity) inventory model will be used as a reference. This model will be compared with a model based on information obtained from a PHM system. Discrete event simulation will be used in order to simulate and assess the performance of both models.

How to Cite

Ramos Rodrigues, L., & Yoneyama, T. (2021). Spare Parts Inventory Control for Non-Repairable Items Based on Prognostics and Health Monitoring Information. Annual Conference of the PHM Society, 4(1). Retrieved from https://papers.phmsociety.org/index.php/phmconf/article/view/2118
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Keywords

health monitoring, prognostics, inventory management

References
Ballou, R. H. (2006). Gerenciamento da Cadeia de Suprimentos / Logística Empresarial. In 5th ed. Porto Alegre.
Feldman, K., Jazouli, T. & Sandborn, P. (2009). A Methodology for Determining the Return on Investment Associated with Prognostics and Health Management. IEEE Transactions on Reliability, Vol. 58, No. 2.
Hess, A., Frith, P. & Suarez, E. (2006). Challenges, Issues and Lessons Learned Implementing Prognostics for Propulsion Systems. In Proceedings of ASME Turbo Expo Power for Land, Sea and Air. Hillier, F. S. & Lieberman, G. J. (2005). Introduction to Operations Research. In 8th ed. New York.
Ho, J. C., Chang, Y. L. & Solis, A. O. (2006). Two Modifications of the Least Cost per Period Heuristic for Dynamic Lot Sizing. Jounal of Operation Research Society, Volume 57.

Kacprzynski, G. J., Roemer, M. J. & Hess, A. J. (2002).Health Management System Design: Development, Simulation and Cost/Benefit Optimization. In Proceedings of IEEE Aerospace Conference, Big Sky. Leão, B. P., Yoneyama, T., Rocha, G. C. & Fitzgibbon, K.

T. (2008). Prognostics Performance Metrics and their Relation to Requirements, Design, Verification and Cost-Benefit. In Proceedings of International Conference on Prognostics and Health Management, Denver.

Luna, J. J. (2009). Metrics, Models, and Scenarios for Evaluating PHM Effects on Logistics Support. In Proceedings of Annual Conference of the Prognostics and Health Management Society.

Omar, M. & Deris, M. M. (2001). The Silver-Meal Heuristic Method for Deterministic Time-Varying Demand. Journal of Matematika, Volume 17.

Omar, M & Supadi, S. S. (2003). A Lot-for-Lot Model with Multiple Installments for a Production System under Time-Varying Demand Process. Journal Matematika.
Puttini, L. C. (2009). Gerenciamento da Saúde de Sistemas Aeronáuticos: Conceitos e Visão de Futuro. In Proceedings of VIII Sitraer, São Paulo.

Rodrigues, L. R., Gomes, J. P. P., Bizarria, C. O. Galvão, R.K. H. & Yoneyama, T. (2010). Using Prognostic System Forecasts and Decision Analysis Techniques in Aircraft Maintenance Cost-Benefit Models. In Proceedings of IEEE Aerospace Conference, Big Sky.
Roemer, M. J., Byington, C. S., Kacprzynski, G. J. & Vachtsevanos, G. (2005). An Overview of Selected Prognostic Technologies with Reference to an Integrated PHM Architecture. In Proceedings of the First International Forum on Integrated System Health Engineering and Management in Aerospace.
Sakaguchi, M. & Kodama, M. (2009). Sensitivity Analysis of an Economic Order Quantity for Dynamic Inventory Models with Discrete Demand. International Journal of Manufacturing Technology and Management, Volume18.

Sandborn, P. A. & Wilkinson, C. (2007). A Maintenance Planning and Business Case Development Model for the Application of Prognostics and Health Management (PHM) to Electronic Systems. Microelectronics Reliability, Volume 47, Issue 12, Electronic system prognostics and health management.
Syntetos, A. A., Boyland, J. E. & Disney, S. M. (2009). Forecasting for Inventory Planning: A 50 Year Review. Journal of the Operational Research Society, Volume 60. Vachtsevanos, G., Lewis, F. L., Roemer, M., Hess, A., & Wu, B. (2006). Intelligent Fault Diagnosis and Prognosis for Engineering Systems. In 1st ed. Hoboken. Wagner, H. M., Whitin, T. M. (1958). Dynamic Version of the Economic Lot Size Model. Management Science, Volume 5.
Wongmongkolrit, S. & Rassameethes, B. (2011). The Modification of EOQ Model under the Spare Parts Discrete Demand: A Case Study of Slow Moving Items. In Proceedings of the World Congress on Engineering and Computer Science, Volume 2, San Francisco.
Yong, Z. W., Ying, X. & Bing, S. (2011). Study on Spare Parts Inventory Control by Quantitative Analysis in the Environment of ERP System. In Proceedings of the International Conference of Business Management and Electronic Information (BMEI).
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Technical Papers

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