Managing Retail Shelf and Backroom Inventories When Demand Depends on the Shelf-Stock Level

Weili Xue, Ozgun Caliskan Demirag, Frank Y. Chen, Yi Yang

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

Inventory displayed on the retail sales floor not only performs the classical supply function but also plays a role in affecting consumers’ buying behavior and hence the total demand. Empirical evidence from the retail industry shows that for some types of products, higher levels of on-shelf inventory have a demand-increasing effect (“billboard effect”) while for some other types of products, higher levels of on-shelf inventory have a demand-decreasing effect (“scarcity effect”). This suggests that retailers may use the amount of shelf stock on display as a tool to influence demand and operate a store backroom to hold the inventory of items not displayed on the shelves, introducing the need for efficient management of the backroom and on-shelf inventories. The purpose of this study is to address such an issue by considering a periodic-review inventory system in which demand in each period is stochastic and depends on the amount of inventory displayed on the shelf. We first analyze the problem in a finite-horizon setting and show under a general demand model that the system inventory is optimally replenished by a base-stock policy and the shelf stock is controlled by two critical points representing the target levels to raise up/drop down the on-shelf inventory level. In the infinite-horizon setting, we find that the optimal policies simplify to stationary base-stock type policies. Under the billboard effect, we further show that the optimal policy is monotone in the system states. Numerical experiments illustrate the value of smart backroom management strategy and show that significant profit gains can be obtained by jointly managing the backroom and on-shelf inventories.

Original languageEnglish (US)
Pages (from-to)1685-1704
Number of pages20
JournalProduction and Operations Management
Volume26
Issue number9
DOIs
StatePublished - Sep 2017

All Science Journal Classification (ASJC) codes

  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering
  • Management of Technology and Innovation

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