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Incorporating Stochastic Lead Times Into the Guaranteed Service Model of Safety Stock Optimization

Author

Listed:
  • Salal Humair

    (Harvard School of Public Health, Boston, Massachusetts 02115; and School of Science and Engineering, Lahore University of Management Sciences, D.H.A. Lahore 54792, Pakistan)

  • John D. Ruark

    (Logility, Inc., Burlington, Massachusetts 01803)

  • Brian Tomlin

    (Tuck School of Business, Dartmouth College, Hanover, New Hampshire 03755)

  • Sean P. Willems

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

Abstract

Effective end-to-end supply chain management and network inventory optimization must account for service levels, demand volatility, lead times, and lead-time variability. Most inventory models incorporate demand variability, but far fewer rigorously account for lead-time variability, particularly in multiechelon supply chain networks. Our research extends the guaranteed service model of safety stock placement to allow random lead times. The main methodological contribution is the creation of closed-form equations for the expected safety stock in the system; this includes a derivation for the early-arrival stock in the system. The main applied contributions are the demonstration of real stochastic lead times in practice and a discussion of how our approach outperforms more traditional heuristics that either ignore lead-time variability or consider the maximum lead time at each stage.

Suggested Citation

  • Salal Humair & John D. Ruark & Brian Tomlin & Sean P. Willems, 2013. "Incorporating Stochastic Lead Times Into the Guaranteed Service Model of Safety Stock Optimization," Interfaces, INFORMS, vol. 43(5), pages 421-434, October.
    • Handle: RePEc:inm:orinte:v:43:y:2013:i:5:p:421-434
    DOI: 10.1287/inte.2013.0699
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    References listed on IDEAS

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    Citations

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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. Tan Wang & L. Jeff Hong, 2023. "Large-Scale Inventory Optimization: A Recurrent Neural Networks–Inspired Simulation Approach," INFORMS Journal on Computing, INFORMS, vol. 35(1), pages 196-215, January.
    3. 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).
    4. Fernando Bernstein & Yang Li & Kevin Shang, 2016. "A Simple Heuristic for Joint Inventory and Pricing Models with Lead Time and Backorders," Management Science, INFORMS, vol. 62(8), pages 2358-2373, August.
    5. Jörn Grahl & Stefan Minner & Daniel Dittmar, 2016. "Meta-heuristics for placing strategic safety stock in multi-echelon inventory with differentiated service times," Annals of Operations Research, Springer, vol. 242(2), pages 489-504, July.
    6. Bandaly, Dia & Satir, Ahmet & Shanker, Latha, 2016. "Impact of lead time variability in supply chain risk management," International Journal of Production Economics, Elsevier, vol. 180(C), pages 88-100.
    7. Achkar, Victoria G. & Brunaud, Braulio B. & Pérez, Héctor D. & Musa, Rami & Méndez, Carlos A. & Grossmann, Ignacio E., 2024. "Extensions to the guaranteed service model for industrial applications of multi-echelon inventory optimization," European Journal of Operational Research, Elsevier, vol. 313(1), pages 192-206.
    8. Schuster Puga, Matías & Minner, Stefan & Tancrez, Jean-Sébastien, 2019. "Two-stage supply chain design with safety stock placement decisions," International Journal of Production Economics, Elsevier, vol. 209(C), pages 183-193.
    9. Aouam, Tarik & Kumar, Kunal, 2019. "On the effect of overtime and subcontracting on supply chain safety stocks," Omega, Elsevier, vol. 89(C), pages 1-20.
    10. Kumar, Kunal & Aouam, Tarik, 2019. "Extending the strategic safety stock placement model to consider tactical production smoothing," European Journal of Operational Research, Elsevier, vol. 279(2), pages 429-448.
    11. Klosterhalfen, Steffen T. & Willems, Sean P. & Dittmar, Daniel, 2023. "Safety stock placement in supply chains with expediting," European Journal of Operational Research, Elsevier, vol. 307(2), pages 745-757.
    12. Kumar, Anupam & Evers, Philip T., 2015. "Setting safety stock based on imprecise records," International Journal of Production Economics, Elsevier, vol. 169(C), pages 68-75.
    13. Mahdavi, Mojtaba & Mahmoudzadeh, Mahdi & Olsen, Tava Lennon, 2023. "Efficient vs. Responsive supplier selection for functional and innovative products: A quantitative study," International Journal of Production Economics, Elsevier, vol. 255(C).
    14. 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.
    15. Goldbeck, Nils & Angeloudis, Panagiotis & Ochieng, Washington, 2020. "Optimal supply chain resilience with consideration of failure propagation and repair logistics," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 133(C).

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