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A Dynamic Inventory Model with the Right of Refusal

Author

Listed:
  • Sreekumar Bhaskaran

    (Information Technology and Operations Management, Cox School of Business, Southern Methodist University, Dallas, Texas 75275)

  • Karthik Ramachandran

    (Information Technology and Operations Management, Cox School of Business, Southern Methodist University, Dallas, Texas 75275)

  • John Semple

    (Information Technology and Operations Management, Cox School of Business, Southern Methodist University, Dallas, Texas 75275)

Abstract

We consider a dynamic inventory (production) model with general convex order (production) costs and excess demand that can be accepted or refused by the firm. Excess demand that is accepted is backlogged and results in a backlog cost whereas demand that is refused results in a lost sales charge. Endogenizing the sales decision is appropriate in the presence of general convex order costs so that the firm is not forced to backlog a unit whose subsequent satisfaction would reduce total profits. In each period, the firm must determine the optimal order and sales strategy. We show that the optimal policy is characterized by an optimal buy-up-to level that increases with the initial inventory level and an order quantity that decreases with the initial inventory level. More importantly, we show the optimal sales strategy is characterized by a critical threshold, a backlog limit, that dictates when to stop selling. This threshold is independent of the initial inventory level and the amount purchased. We investigate various properties of this new policy. As demand stochastically increases, the amount purchased increases but the amount backlogged decreases, reflecting a shift in the way excess demand is managed. We develop two regularity conditions, one that ensures some backlogs are allowed in each period, and another that ensures the amount backlogged is nondecreasing in the length of the planning horizon. We illustrate the buy-up-to levels in our model are bounded above by buy-up-to levels from the pure lost sales and pure backlogging models. We explore additional extensions using numerical experiments.

Suggested Citation

  • Sreekumar Bhaskaran & Karthik Ramachandran & John Semple, 2010. "A Dynamic Inventory Model with the Right of Refusal," Management Science, INFORMS, vol. 56(12), pages 2265-2281, December.
  • Handle: RePEc:inm:ormnsc:v:56:y:2010:i:12:p:2265-2281
    DOI: 10.1287/mnsc.1100.1257
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    References listed on IDEAS

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    Cited by:

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    3. Adam N. Elmachtoub & Retsef Levi, 2016. "Supply Chain Management with Online Customer Selection," Operations Research, INFORMS, vol. 64(2), pages 458-473, April.
    4. Yongbo Xiao, 2018. "Dynamic pricing and replenishment: Optimality, bounds, and asymptotics," Naval Research Logistics (NRL), John Wiley & Sons, vol. 65(1), pages 3-25, February.
    5. Timothy M. Sweda & Irina S. Dolinskaya & Diego Klabjan, 2017. "Optimal Recharging Policies for Electric Vehicles," Transportation Science, INFORMS, vol. 51(2), pages 457-479, May.
    6. Yanyi Xu & Doğan A. Serel & Arnab Bisi & Maqbool Dada, 2022. "Coping with Demand Uncertainty: The Interplay between Dual Sourcing and Endogenous Partial Backordering," Production and Operations Management, Production and Operations Management Society, vol. 31(4), pages 1560-1575, April.

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