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Supply Chains Facing Atypical Demand: Optimal Operational Policies And Benefits Under Information Sharing

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  • Baruah, Pundarikaksha
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    Abstract

    Demand patterns for products with seasonality and or short life-cycles do not follow a clear discernible pattern (to allow predictive time-series modeling of demand) for individual sales events or seasons due to such factors as considerable demand volatility, product promotions, and unforeseen marketplace events. Suppliers supporting such atypical demand patterns typically incur higher holding costs, lower capacity utilization, and lower order fill-rates, particularly under long lead-times and uncertainty in effective capacity. Retailers on the other hand struggle with product overages and supply shortages. On the other hand, atypical demand settings bring huge financial opportunity to supply chain players, and are pervasive. It is suggested in the literature that an effective means to reap these benefits is through increased information sharing between retailers and suppliers, superior forecasting with forecast update techniques, proper replenishment, and custom designed inventory/manufacturing policies. We also believe that sharing of order forecasts, also known as soft-orders, in advance by the buyer could be beneficial to both parties involved. This dissertation in particular studies a two-player supply chain, facing atypical demand. Among the two-players is a buyer (retailer/distributor/vendor) that makes ordering decision(s) in the presence of upstream supply uncertainty and demand forecast revision(s). We propose a stochastic dynamic programming model to optimally deicide on soft-order(s) and a final firm-order under a deposit scheme for initial soft-order(s). While sharing of upstream soft-order inventory position information by the supplier before receiving a final order is not a common industrial practice, nor is it discussed in the literature, our analysis shows that such information sharing is beneficial under certain conditions. Second player of the supply chain is a supplier (manufacturer) that makes production release decision(s) in the presence of limited and random effective capacity, and final order uncertainty. Our stochastic dynamic programming model for optimal production release decision making reveals that substantial savings in order fulfillment cost (that includes holding, overage, and underage costs) can be realized in the presence of advance soft-order(s). Soft-orders can also be shown to improve order fill-rate for the buyer. This research explores complex interactions of factors that affect the operational decision making process, such as costs, demand uncertainty, supply uncertainty, effective capacity severity, information accuracy, information volatility, intentional manipulation of information etc. Through extensive analysis of the operational policies, we provide managerial insights, many of which are intuitively appealing, such as, additional information never increases cost of an optimal decision; many are also counterintuitive, for example, dynamic programming models cannot fully compensate for intentional soft-order inflation by the buyer, even under conditions of a stable and linear order inflation pattern, in the absence of deposits.

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    Bibliographic Info

    Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 16101.

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    Date of creation: 21 Dec 2006
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    Handle: RePEc:pra:mprapa:16101

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    Keywords: Supply Chain Economics; Information Sharing; Atypical Demand; Optimal Cost Model; Dynamic Program; Multi-player model;

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    References

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    1. Srinivasan Raghunathan, 2001. "Information Sharing in a Supply Chain: A Note on its Value when Demand Is Nonstationary," Management Science, INFORMS, vol. 47(4), pages 605-610, April.
    2. Mohebbi, Esmail, 2004. "A replenishment model for the supply-uncertainty problem," International Journal of Production Economics, Elsevier, vol. 87(1), pages 25-37, January.
    3. Stephen A. Smith & Dale D. Achabal, 1998. "Clearance Pricing and Inventory Policies for Retail Chains," Management Science, INFORMS, vol. 44(3), pages 285-300, March.
    4. Gary D. Eppen & Ananth. V. Iyer, 1997. "Backup Agreements in Fashion Buying---The Value of Upstream Flexibility," Management Science, INFORMS, vol. 43(11), pages 1469-1484, November.
    5. Gullu, Refik & Onol, Ebru & Erkip, Nesim, 1999. "Analysis of an inventory system under supply uncertainty," International Journal of Production Economics, Elsevier, vol. 59(1-3), pages 377-385, March.
    6. Christian Terwiesch & Z. Justin Ren & Teck H. Ho & Morris A. Cohen, 2005. "An Empirical Analysis of Forecast Sharing in the Semiconductor Equipment Supply Chain," Management Science, INFORMS, vol. 51(2), pages 208-220, February.
    7. Yunzeng Wang & Yigal Gerchak, 1996. "Periodic Review Production Models with Variable Capacity, Random Yield, and Uncertain Demand," Management Science, INFORMS, vol. 42(1), pages 130-137, January.
    8. Hirofumi Matsuo, 1990. "A Stochastic Sequencing Problem for Style Goods with Forecast Revisions and Hierarchical Structure," Management Science, INFORMS, vol. 36(3), pages 332-347, March.
    9. Xinxin Hu & Izak Duenyas (Advisor) & Roman Kapuscinski (Advisor), 2003. "Advance Demand Information and Safety Capacity as a Hedge Against Demand and Capacity Uncertainty," Manufacturing & Service Operations Management, INFORMS, vol. 5(1), pages 55-58.
    10. Karen L. Donohue, 2000. "Efficient Supply Contracts for Fashion Goods with Forecast Updating and Two Production Modes," Management Science, INFORMS, vol. 46(11), pages 1397-1411, November.
    11. Marshall Fisher & Kumar Rajaram & Ananth Raman, 2001. "Optimizing Inventory Replenishment of Retail Fashion Products," Manufacturing & Service Operations Management, INFORMS, vol. 3(3), pages 230-241, November.
    12. Kandel, Eugene, 1996. "The Right to Return," Journal of Law and Economics, University of Chicago Press, vol. 39(1), pages 329-56, April.
    13. Hau L. Lee & Kut C. So & Christopher S. Tang, 2000. "The Value of Information Sharing in a Two-Level Supply Chain," Management Science, INFORMS, vol. 46(5), pages 626-643, May.
    14. Gérard P. Cachon & Marshall Fisher, 2000. "Supply Chain Inventory Management and the Value of Shared Information," Management Science, INFORMS, vol. 46(8), pages 1032-1048, August.
    15. L. Beril Toktay & Lawrence M. Wein, 2001. "Analysis of a Forecasting-Production-Inventory System with Stationary Demand," Management Science, INFORMS, vol. 47(9), pages 1268-1281, September.
    16. G. D. Johnson & H. E. Thompson, 1975. "Optimality of Myopic Inventory Policies for Certain Dependent Demand Processes," Management Science, INFORMS, vol. 21(11), pages 1303-1307, July.
    17. Gérard P. Cachon & Martin A. Lariviere, 2001. "Contracting to Assure Supply: How to Share Demand Forecasts in a Supply Chain," Management Science, INFORMS, vol. 47(5), pages 629-646, May.
    18. Philip Kaminsky & Jayashankar M. Swaminathan, 2001. "Utilizing Forecast Band Refinement for Capacitated Production Planning," Manufacturing & Service Operations Management, INFORMS, vol. 3(1), pages 68-81, August.
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