Efficient Computational Strategies for Dynamic Inventory Liquidation

Abstract

We examine the dynamic inventory liquidation problem, in which a retailer liquidates a fixed number of identical items over a time period by strategically setting prices periodically according to knowledge about stochastic demand. We propose to solve the liquidation problem by deriving a deterministic representation of stochastic demand. Assuming that customer arrival and valuations follow known statistical distributions (e.g., estimated from past transaction data), the expected arrivals and expected order statistics of valuation distributions represent informative and advantageous approximations of demand. Under the deterministic demand representation, we develop a greedy heuristic for finding the optimal liquidation strategy that result in maximum total revenue. The heuristic approach is computationally highly efficient and provides optimal solutions under deterministic demand representation when customer valuation follows various typical statistical distributions. Compared with two simple and commonly used liquidation strategies (i.e., the fixed-price strategy and the fixed-quantity strategy), our heuristic yields higher liquidation revenue. Compared with sophisticated approaches that can find optimal liquidation strategies under stochastic demand (e.g., stochastic dynamic programming), our approach runs several magnitudes faster and still yields near optimal expected revenue. Therefore, the heuristic approach can serve as a useful tool for managers to make liquidation-related decisions in realistic, stochastic demand scenarios.