Delegation of stocking decisions under asymmetric demand information

Abstract

Problem definition: We consider the incentive design problem of a retailer that delegates stocking decisions to its store managers who are privately informed about local demand. Academic/practical relevance: Shortages are highly costly in retail, but are less of a concern for store managers, as their exact amounts are usually not recorded. In order to align incentives and attain desired service levels, retailers need to design mechanisms in the absence of information on shortage quantities. Methodology: The headquarters knows that the underlying demand process at a store is one of J possible Wiener processes, whereas the store manager knows the specific process. The store manager creates a single order before each period. The headquarters uses an incentive scheme that is based on the end-of-period leftover inventory and on a stock-out occasion at a prespecified inspection time before the end of a period. The problem for the headquarters is to determine the inspection time and the significance of a stock-out relative to leftover inventory in eval uating the performance of the store manager. We formulate the problem as a constrained nonlinear optimization problem in the single period setting and a dynamic program in the multiperiod setting. Results: We show that the proposed “early inspection” scheme leads to perfect alignment when J equals two under mild conditions. In more general cases, we show that the scheme performs strictly better than inspecting stock-outs at the end and achieves near-perfect alignment. Our numerical experiments, using both synthetic and real data, reveal that this scheme can lead to considerable cost reductions. Managerial im plications: Stock-out-related measures are typically not included in store managers’ performance scorecards in retail. We propose a novel, easy, and practical performance measurement scheme that does not depend on the actual amount of shortages. This new scheme incentivizes the store managers to use their private information in the retailer’s best interest and clearly outperforms centralized ordering systems that are common practice.