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Easy Quantification of Improved Spare Parts Inventory Policies

Author

Listed:
  • Ulrich W. Thonemann

    (Institute of Supply Chain Management, University of Münster, D-48143 Münster, Germany Manugistics, San Mateo, California 94404)

  • Alex O. Brown

    (Department of Management Science & Engineering, Stanford University, Stanford, California 94305-4026)

  • Warren H. Hausman

    (Department of Management Science & Engineering, Stanford University, Stanford, California 94305-4026)

Abstract

This paper presents approximate analytical models to quantify the expected improvement in inventory investment when using a system approach to control inventory as opposed to a simpler item approach. A system approach ensures that a demand-weighted average fill rate is achieved at low inventory investment by assigning low fill rates to parts with high costs and high fill rates to parts with low costs. An item approach does not vary fill rates by parts but assigns identical fill rates to all parts. Using single-parameter functional representations of the skewness of unit costs and average demand across all parts in the system, simple approximate analytical expressions for the required inventory investment are derived for both approaches. The accuracy of the approximations is validated using data from a distribution center for computer spare parts. For these data, the solutions obtained by the approximations are very close to the exact values. The results show that inventory investments can be well approximated as a function of only a few cost and demand parameters. These expressions can be used to determine the percentage reduction in inventory investment for a particular target demand-weighted average fill rate when the superior system approach is used instead of the item approach. For increased ease of use, the percentage reduction in inventory when using a system as opposed to an item approach is computed over a range of realistic values for the key parameters of the model and a quadratic expression is fitted to the data. This fitted expression provides rough guidelines for the anticipated improvement with very limited data needed, prior to detailed modeling or implementation.

Suggested Citation

  • Ulrich W. Thonemann & Alex O. Brown & Warren H. Hausman, 2002. "Easy Quantification of Improved Spare Parts Inventory Policies," Management Science, INFORMS, vol. 48(9), pages 1213-1225, September.
  • Handle: RePEc:inm:ormnsc:v:48:y:2002:i:9:p:1213-1225
    DOI: 10.1287/mnsc.48.9.1213.173
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Christiane B. Haubitz & Ulrich W. Thonemann, 2021. "How to Change a Running System—Controlling the Transition to Optimized Spare Parts Inventory Policies," Production and Operations Management, Production and Operations Management Society, vol. 30(5), pages 1386-1405, May.
    2. Amin Khademi & Burak Eksioglu, 2018. "Spare Parts Inventory Management with Substitution-Dependent Reliability," INFORMS Journal on Computing, INFORMS, vol. 30(3), pages 507-521, August.
    3. De Schrijver, Steven K. & Aghezzaf, El-Houssaine & Vanmaele, Hendrik, 2013. "Aggregate constrained inventory systems with independent multi-product demand: Control practices and theoretical limitations," International Journal of Production Economics, Elsevier, vol. 143(2), pages 416-423.
    4. Christopher A. Boone & Benjamin T. Hazen & Joseph B. Skipper & Robert E. Overstreet, 2018. "A framework for investigating optimization of service parts performance with big data," Annals of Operations Research, Springer, vol. 270(1), pages 65-74, November.
    5. Wong, H. & van Houtum, G.J. & Cattrysse, D. & Oudheusden, D. Van, 2006. "Multi-item spare parts systems with lateral transshipments and waiting time constraints," European Journal of Operational Research, Elsevier, vol. 171(3), pages 1071-1093, June.
    6. Kleber, Rainer & Zanoni, Simone & Zavanella, Lucio, 2011. "On how buyback and remanufacturing strategies affect the profitability of spare parts supply chains," International Journal of Production Economics, Elsevier, vol. 133(1), pages 135-142, September.
    7. Kurata, Hisashi & Nam, Seong-Hyun, 2013. "After-sales service competition in a supply chain: Does uncertainty affect the conflict between profit maximization and customer satisfaction?," International Journal of Production Economics, Elsevier, vol. 144(1), pages 268-280.
    8. Bacchetti, Andrea & Saccani, Nicola, 2012. "Spare parts classification and demand forecasting for stock control: Investigating the gap between research and practice," Omega, Elsevier, vol. 40(6), pages 722-737.
    9. Topan, Engin & Bayındır, Z. Pelin & Tan, Tarkan, 2017. "Heuristics for multi-item two-echelon spare parts inventory control subject to aggregate and individual service measures," European Journal of Operational Research, Elsevier, vol. 256(1), pages 126-138.
    10. H Wong & G J van Houtum & D Cattrysse & D Van Oudheusden, 2005. "Simple, efficient heuristics for multi-item multi-location spare parts systems with lateral transshipments and waiting time constraints," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(12), pages 1419-1430, December.
    11. Teunter, R.H. & Syntetos, A.A. & Babai, M.Z., 2017. "Stock keeping unit fill rate specification," European Journal of Operational Research, Elsevier, vol. 259(3), pages 917-925.

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