Improving Store Liquidation

Posted: 17 May 2013 Last revised: 2 Jul 2015

Nathan Craig

Ohio State University (OSU) - Fisher College of Business

Ananth Raman

Harvard University - Technology & Operations Management Unit

Date Written: May 3, 2013


Store liquidation is the time-constrained divestment of retail outlets through an in-store sale of inventory. The retail industry depends extensively on store liquidation, not only as a means for investors to recover capital from failed ventures, but also to allow managers of going concerns to divest stores in eff orts to enhance performance and to change strategy. Recent examples of entire chains being liquidated include Borders Group in 2012, Circuit City in 2009, and Linens 'n Things in 2008; the value of inventory sold during these liquidations alone is $3B. The store liquidation problem is related to but also di ffers substantially from the markdown optimization problem that has been studied extensively in the literature. This paper introduces the store liquidation problem to the literature and presents a technique for optimizing key decision variables, such as markdown, inventory, and store closing decisions during liquidations. We show that our approach could improve net recovery on cost (i.e., the profi t obtained during liquidations stated as a percentage of the cost value of liquidated assets) by 2 to 7 percentage points in the cases we examined. The paper also identi fies ways in which current practice in store liquidation di ffers from the optimal decisions identi fied in the paper and traces the consequences of these di fferences.

Suggested Citation

Craig, Nathan and Raman, Ananth, Improving Store Liquidation (May 3, 2013). Available at SSRN: or

Nathan Craig (Contact Author)

Ohio State University (OSU) - Fisher College of Business ( email )

2100 Neil Avenue
Columbus, OH 43210-1144
United States

Ananth Raman

Harvard University - Technology & Operations Management Unit ( email )

Boston, MA 02163
United States
617-495-6937 (Phone)
617-496-4059 (Fax)

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