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Managing Inventory in Supply Chains with Nonstationary Demand

Author

Listed:
  • John J. Neale

    (School of Management, Boston University, Boston, Massachusetts 02215)

  • Sean P. Willems

    (School of Management, Boston University, Boston, Massachusetts 02215)

Abstract

Many companies experience nonstationary demand because of short product life cycles, seasonality, customer buying patterns, or other factors. We present a practical model for managing inventory in a supply chain facing stochastic, nonstationary demand. Our model is based on the guaranteed service modeling framework. We first describe how inventory levels should adapt to changes in demand at a single stage. We then show how nonstationary demand propagates in a supply chain, allowing us to link stages and apply a multiechelon optimization algorithm designed originally for stationary demand. We describe two successful applications of this model. The first is a tactical implementation to support monthly safety stock planning at Microsoft. The second is a strategic project to evaluate the benefits of using an inventory pool at Case New Holland.

Suggested Citation

  • John J. Neale & Sean P. Willems, 2009. "Managing Inventory in Supply Chains with Nonstationary Demand," Interfaces, INFORMS, vol. 39(5), pages 388-399, October.
    • Handle: RePEc:inm:orinte:v:39:y:2009:i:5:p:388-399
    DOI: 10.1287/inte.1090.0442
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    References listed on IDEAS

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    Cited by:

    1. de Kok, Ton & Grob, Christopher & Laumanns, Marco & Minner, Stefan & Rambau, Jörg & Schade, Konrad, 2018. "A typology and literature review on stochastic multi-echelon inventory models," European Journal of Operational Research, Elsevier, vol. 269(3), pages 955-983.
    2. Daniel Y. Mo & Stephen C. H. Ng & David Tai, 2019. "Revamping NetApp’s Service Parts Operations by Process Optimization," Service Science, INFORMS, vol. 49(6), pages 407-421, November.
    3. Sima M. Fortsch & Jeong Hoon Choi & Elena A. Khapalova, 2022. "Competition can help predict sales," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(2), pages 331-344, March.
    4. Kilic, Onur A. & Tunc, Huseyin, 2019. "Heuristics for the stochastic economic lot sizing problem with remanufacturing under backordering costs," European Journal of Operational Research, Elsevier, vol. 276(3), pages 880-892.
    5. Barros, Júlio & Cortez, Paulo & Carvalho, M. Sameiro, 2021. "A systematic literature review about dimensioning safety stock under uncertainties and risks in the procurement process," Operations Research Perspectives, Elsevier, vol. 8(C).
    6. Stößlein, Martin & Kanet, John Jack & Gorman, Mike & Minner, Stefan, 2014. "Time-phased safety stocks planning and its financial impacts: Empirical evidence based on European econometric data," International Journal of Production Economics, Elsevier, vol. 149(C), pages 47-55.
    7. Gihan S. Edirisinghe & Thamer Almutairi, 2023. "Multi-Echelon Inventory Optimization for Practitioners: a Predictive Global Sensitivity Analysis Approach," SN Operations Research Forum, Springer, vol. 4(2), pages 1-20, June.
    8. Zied Bahroun & Nidhal Belgacem, 2019. "Determination of dynamic safety stocks for cyclic production schedules," Operations Management Research, Springer, vol. 12(1), pages 62-93, June.
    9. Ingrid Farasyn & Salal Humair & Joel I. Kahn & John J. Neale & Oscar Rosen & John Ruark & William Tarlton & Wim Van de Velde & Glenn Wegryn & Sean P. Willems, 2011. "Inventory Optimization at Procter & Gamble: Achieving Real Benefits Through User Adoption of Inventory Tools," Interfaces, INFORMS, vol. 41(1), pages 66-78, February.
    10. Walid W. Nasr, 2022. "Inventory systems with stochastic and batch demand: computational approaches," Annals of Operations Research, Springer, vol. 309(1), pages 163-187, February.
    11. Yang, Liu & Liu, Kanglin & Zhang, Juan & Zelbst, Pamela J., 2024. "Inventory management with actual palletized transportation costs and lost sales," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 184(C).
    12. Mitchell A. Millstein & James F. Campbell, 2018. "Total Hockey Optimizes Omnichannel Facility Locations," Interfaces, INFORMS, vol. 48(4), pages 340-356, August.
    13. Pauls-Worm, Karin G.J. & Hendrix, Eligius M.T. & Haijema, René & van der Vorst, Jack G.A.J., 2014. "An MILP approximation for ordering perishable products with non-stationary demand and service level constraints," International Journal of Production Economics, Elsevier, vol. 157(C), pages 133-146.
    14. Kristianto, Yohanes & Helo, Petri & Jiao, Jianxin (Roger) & Sandhu, Maqsood, 2012. "Adaptive fuzzy vendor managed inventory control for mitigating the Bullwhip effect in supply chains," European Journal of Operational Research, Elsevier, vol. 216(2), pages 346-355.
    15. Nasr, Walid W. & Elshar, Ibrahim J., 2018. "Continuous inventory control with stochastic and non-stationary Markovian demand," European Journal of Operational Research, Elsevier, vol. 270(1), pages 198-217.
    16. Xiang, Mengyuan & Rossi, Roberto & Martin-Barragan, Belen & Tarim, S. Armagan, 2023. "A mathematical programming-based solution method for the nonstationary inventory problem under correlated demand," European Journal of Operational Research, Elsevier, vol. 304(2), pages 515-524.
    17. Yang, Liu & Li, Haitao & Campbell, James F. & Sweeney, Donald C., 2017. "Integrated multi-period dynamic inventory classification and control," International Journal of Production Economics, Elsevier, vol. 189(C), pages 86-96.
    18. Eruguz, Ayse Sena & Sahin, Evren & Jemai, Zied & Dallery, Yves, 2016. "A comprehensive survey of guaranteed-service models for multi-echelon inventory optimization," International Journal of Production Economics, Elsevier, vol. 172(C), pages 110-125.
    19. Kilic, Onur A. & Tarim, S. Armagan, 2024. "A simple heuristic for computing non-stationary inventory policies based on function approximation," European Journal of Operational Research, Elsevier, vol. 316(3), pages 899-905.

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