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Optimal Markdown Pricing and Inventory Allocation for Retail Chains with Inventory Dependent Demand

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  • Stephen A. Smith

    (Department of Operations Management and Information Systems, Leavey School of Business, Santa Clara University, Santa Clara, California 95053)

  • Narendra Agrawal

    (Department of Operations Management and Information Systems, Leavey School of Business, Santa Clara University, Santa Clara, California 95053)

Abstract

We analyze how inventory dependence of demand affects the optimal pricing and distribution of inventory across multiple retail locations. Our problem formulation corresponds to the typical markdown management decisions in a retail chain. The solution methodology determines the optimal combinations of three important operational levers—pricing, inventory allocation, and store consolidation. Inventory dependence causes the optimal price trajectory to decrease over time as the current on-hand inventory decreases. The optimal inventory allocation shifts more inventory to the larger stores as the inventory effect becomes stronger. Solution methodologies are also developed for the case of optimal constant prices, which leads to the same optimal inventory allocations. If all stores have the same seasonal variations and the inventory is allocated optimally, then all stores have identical price trajectories. Optimal consolidation of the inventory to a subset of the stores is also determined. Consolidation of inventory to a subset of stores can be beneficial even when there are no fixed costs of stocking a store. Using some typical parameter values for retail items, our calculations show that significant benefits can result from taking the inventory effect into account in pricing, inventory allocation, and store consolidation decisions.

Suggested Citation

  • Stephen A. Smith & Narendra Agrawal, 2017. "Optimal Markdown Pricing and Inventory Allocation for Retail Chains with Inventory Dependent Demand," Manufacturing & Service Operations Management, INFORMS, vol. 19(2), pages 290-304, May.
    • Handle: RePEc:inm:ormsom:v:19:y:2017:i:2:p:290-304
    DOI: 10.1287/msom.2016.0609
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    1. Adam J. Mersereau & Dan Zhang, 2012. "Markdown Pricing with Unknown Fraction of Strategic Customers," Manufacturing & Service Operations Management, INFORMS, vol. 14(3), pages 355-370, July.
    2. Ruth N. Bolton, 1989. "The Relationship Between Market Characteristics and Promotional Price Elasticities," Marketing Science, INFORMS, vol. 8(2), pages 153-169.
    3. Urban, Timothy L. & Baker, R. C., 1997. "Optimal ordering and pricing policies in a single-period environment with multivariate demand and markdowns," European Journal of Operational Research, Elsevier, vol. 103(3), pages 573-583, December.
    4. Urban, Timothy L., 2005. "Inventory models with inventory-level-dependent demand: A comprehensive review and unifying theory," European Journal of Operational Research, Elsevier, vol. 162(3), pages 792-804, May.
    5. 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.
    6. Narendra Agrawal & Stephen A. Smith, 2015. "Multi-location Inventory Models for Retail Supply Chain Management," International Series in Operations Research & Management Science, in: Narendra Agrawal & Stephen A. Smith (ed.), Retail Supply Chain Management, edition 2, chapter 0, pages 319-347, Springer.
    7. Yigal Gerchak & Yunzeng Wang, 1994. "Periodic‐review inventory models with inventory‐level‐dependent demand," Naval Research Logistics (NRL), John Wiley & Sons, vol. 41(1), pages 99-116, February.
    8. Gabriel Bitran & René Caldentey, 2003. "An Overview of Pricing Models for Revenue Management," Manufacturing & Service Operations Management, INFORMS, vol. 5(3), pages 203-229, August.
    9. Nathan C. Craig & Ananth Raman, 2016. "Improving Store Liquidation," Manufacturing & Service Operations Management, INFORMS, vol. 18(1), pages 89-103, February.
    10. Felipe Caro & Jérémie Gallien, 2012. "Clearance Pricing Optimization for a Fast-Fashion Retailer," Operations Research, INFORMS, vol. 60(6), pages 1404-1422, December.
    11. Padmanabhan, G. & Vrat, Prem, 1995. "EOQ models for perishable items under stock dependent selling rate," European Journal of Operational Research, Elsevier, vol. 86(2), pages 281-292, October.
    12. Koschat, Martin A., 2008. "Store inventory can affect demand: Empirical evidence from magazine retailing," Journal of Retailing, Elsevier, vol. 84(2), pages 165-179.
    13. Kirthi Kalyanam, 1996. "Pricing Decisions Under Demand Uncertainty: A Bayesian Mixture Model Approach," Marketing Science, INFORMS, vol. 15(3), pages 207-221.
    14. Marshall Fisher & Kumar Rajaram, 2000. "Accurate Retail Testing of Fashion Merchandise: Methodology and Application," Marketing Science, INFORMS, vol. 19(3), pages 266-278, June.
    15. Stephen A. Smith, 2015. "Clearance Pricing in Retail Chains," International Series in Operations Research & Management Science, in: Narendra Agrawal & Stephen A. Smith (ed.), Retail Supply Chain Management, edition 2, chapter 0, pages 387-408, Springer.
    16. Gabriel Bitran & René Caldentey & Susana Mondschein, 1998. "Coordinating Clearance Markdown Sales of Seasonal Products in Retail Chains," Operations Research, INFORMS, vol. 46(5), pages 609-624, October.
    17. Anantaram Balakrishnan & Michael S. Pangburn & Euthemia Stavrulaki, 2008. "Integrating the Promotional and Service Roles of Retail Inventories," Manufacturing & Service Operations Management, INFORMS, vol. 10(2), pages 218-235, July.
    18. Marcel Corstjens & Peter Doyle, 1981. "A Model for Optimizing Retail Space Allocations," Management Science, INFORMS, vol. 27(7), pages 822-833, July.
    19. Chakravarthi Narasimhan, 1984. "A Price Discrimination Theory of Coupons," Marketing Science, INFORMS, vol. 3(2), pages 128-147.
    20. Amit, R.K. & Mehta, Peeyush & Tripathi, Rajeev R., 2015. "Optimal shelf-space stocking policy using stochastic dominance under supply-driven demand uncertainty," European Journal of Operational Research, Elsevier, vol. 246(1), pages 339-342.
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