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Inventory Management with Advance Demand Information and Flexible Delivery


  • Tong Wang

    () (Decision Sciences Area, INSEAD, 77305 Fontainebleau, France)

  • Beril L. Toktay

    () (College of Management, Georgia Institute of Technology, Atlanta, Georgia 30308)


This paper considers inventory models with advance demand information and flexible delivery. Customers place their orders in advance, and delivery is flexible in the sense that early shipment is allowed. Specifically, an order placed at time t by a customer with demand lead time T should be fulfilled by period t + T; failure to fulfill it within the time window [t, t + T] is penalized. We consider two situations: (1) Customer demand lead times are homogeneous and demand arriving in period t is a scalar d t to be satisfied within T periods. We show that state-dependent (s, S) policies are optimal, where the state represents advance demands outside the supply lead-time horizon. We find that increasing the demand lead time is more beneficial than decreasing the supply lead time. (2) Customers are heterogeneous in their demand lead times. In this case, demands are vectors and may exhibit crossover, necessitating an allocation decision in addition to the ordering decision. We develop a lower-bound approximation based on an allocation assumption, and propose protection-level heuristics that yield upper bounds on the optimal cost. Numerical analysis quantifies the optimality gaps of the heuristics (2% on average for the best heuristic) and the benefit of delivery flexibility (14% on average using the best heuristic), and provides insights into when the heuristics perform the best and when flexibility is most beneficial.

Suggested Citation

  • Tong Wang & Beril L. Toktay, 2008. "Inventory Management with Advance Demand Information and Flexible Delivery," Management Science, INFORMS, vol. 54(4), pages 716-732, April.
  • Handle: RePEc:inm:ormnsc:v:54:y:2008:i:4:p:716-732

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    References listed on IDEAS

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    Cited by:

    1. Du, Bisheng & Larsen, Christian, 2017. "Reservation policies of advance orders in the presence of multiple demand classes," European Journal of Operational Research, Elsevier, vol. 256(2), pages 430-438.
    2. Choi, Tsan-Ming & Sethi, Suresh, 2010. "Innovative quick response programs: A review," International Journal of Production Economics, Elsevier, vol. 127(1), pages 1-12, September.
    3. Mostard, Julien & Teunter, Ruud & de Koster, René, 2011. "Forecasting demand for single-period products: A case study in the apparel industry," European Journal of Operational Research, Elsevier, vol. 211(1), pages 139-147, May.
    4. Huang, Shuo & Axsäter, Sven & Dou, Yifan & Chen, Jian, 2011. "A real-time decision rule for an inventory system with committed service time and emergency orders," European Journal of Operational Research, Elsevier, vol. 215(1), pages 70-79, November.
    5. Xu, Haoxuan & Gong, Yeming (Yale) & Chu, Chengbin & Zhang, Jinlong, 2017. "Dynamic lot-sizing models for retailers with online channels," International Journal of Production Economics, Elsevier, vol. 183(PA), pages 171-184.
    6. Schoenherr, Tobias, 2012. "The role of environmental management in sustainable business development: A multi-country investigation," International Journal of Production Economics, Elsevier, vol. 140(1), pages 116-128.
    7. Fernando Bernstein & Gregory A. DeCroix & Yulan Wang, 2011. "The Impact of Demand Aggregation Through Delayed Component Allocation in an Assemble-to-Order System," Management Science, INFORMS, vol. 57(6), pages 1154-1171, June.


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