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Optimal inventory management for a retail chain with diverse store demands

Listed author(s):
  • Agrawal, Narendra
  • Smith, Stephen A.
Registered author(s):

    Item demands at individual retail stores in a chain often differ significantly, due to local economic conditions, cultural and demographic differences and variations in store format. Accounting for these variations appropriately in inventory management can significantly improve retailers’ profits. For example, it is shown that having greater differences across the mean store demands leads to a higher expected profit, for a given inventory and total mean demand. If more than one inventory shipment per season is possible, the analysis becomes dynamic by including updated demand forecasts for each store and re-optimizing store inventory policies in midseason. In this paper, we formulate a dynamic stochastic optimization model that determines the total order size and the optimal inventory allocation across nonidentical stores in each period. A generalized Bayesian inference model is used for demands that are partially correlated across the stores and time periods. We also derive a normal approximation for the excess inventory from the previous period, which allows the dynamic programming formulation to be easily solved. We analyze the tradeoffs between obtaining information and profitability, e.g., stocking more stores in period 1 provides more demand information for period 2, but does not necessarily lead to higher total profit. Numerical analyses compare the expected profits of alternative supply chain strategies, as well as the sensitivity to different distributions of demand across the stores. This leads to novel strategic insights that arise from adopting inventory policies that vary by store type.

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    Article provided by Elsevier in its journal European Journal of Operational Research.

    Volume (Year): 225 (2013)
    Issue (Month): 3 ()
    Pages: 393-403

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    Handle: RePEc:eee:ejores:v:225:y:2013:i:3:p:393-403
    DOI: 10.1016/j.ejor.2012.10.006
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    1. Katy S. Azoury, 1985. "Bayes Solution to Dynamic Inventory Models Under Unknown Demand Distribution," Management Science, INFORMS, vol. 31(9), pages 1150-1160, September.
    2. Warren H. Hausman & Rein Peterson, 1972. "Multiproduct Production Scheduling for Style Goods with Limited Capacity, Forecast Revisions and Terminal Delivery," Management Science, INFORMS, vol. 18(7), pages 370-383, March.
    3. Nesim Erkip & Warren H. Hausman & Steven Nahmias, 1990. "Optimal Centralized Ordering Policies in Multi-Echelon Inventory Systems with Correlated Demands," Management Science, INFORMS, vol. 36(3), pages 381-392, March.
    4. Marshall Fisher & Kumar Rajaram, 2000. "Accurate Retail Testing of Fashion Merchandise: Methodology and Application," Marketing Science, INFORMS, vol. 19(3), pages 266-278, June.
    5. Paterson, Colin & Teunter, Ruud & Glazebrook, Kevin, 2012. "Enhanced lateral transshipments in a multi-location inventory system," European Journal of Operational Research, Elsevier, vol. 221(2), pages 317-327.
    6. A. Gürhan Kök & Kevin H. Shang, 2007. "Inspection and Replenishment Policies for Systems with Inventory Record Inaccuracy," Manufacturing & Service Operations Management, INFORMS, vol. 9(2), pages 185-205, February.
    7. George R. Murray, Jr. & Edward A. Silver, 1966. "A Bayesian Analysis of the Style Goods Inventory Problem," Management Science, INFORMS, vol. 12(11), pages 785-797, July.
    8. Nicole DeHoratius & Adam J. Mersereau & Linus Schrage, 2008. "Retail Inventory Management When Records Are Inaccurate," Manufacturing & Service Operations Management, INFORMS, vol. 10(2), pages 257-277, November.
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