Improving Store Liquidation
Executive Summary — Store liquidation, defined as the time-constrained divestment of retail stores through an in-store sale of inventory, is a critical aspect of the retail industry for both defunct and going concerns. Store liquidation is important for firms and investors, affecting everything from retailer performance to how retailers are financed and how investors are compensated. Further, store liquidation is fundamental to innovation in the retail sector, since extracting value from defunct stores and firms is a key step in the process of creative destruction. In this paper, the authors introduce methods for increasing the efficiency of store liquidations operated by retail asset disposition firms, and they thus extend management science techniques to a consequential problem that has not yet been addressed by the literature. These methods were developed through a collaboration with GBG, a prominent liquidator, during the liquidation of over $3B of inventory. Key concepts include:
- This paper introduces a method for improving the efficiency of store liquidations, i.e., for increasing the net orderly liquidation value (NOLV) of retail stores, with a focus on liquidations conducted by asset disposition firms.
- The method comprises a dynamic program that informs markdown, inventory, and store closing decisions as well as a demand forecasting model.
- In each of three recent applications, the authors show that their method provided a significant improvement over prior practice.
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 efforts 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 differs 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 profit 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 identifies ways in which current practice in store liquidation differs from the optimal decisions identified in the paper and traces the consequences of these differences.