Tuesday, July 24, 2018
Demand Shaping Through Bundling and Product Configuration: A Dynamic Multiproduct Inventory-Pricing Model
Jing-Sheng Jeannette Song, Duke University - Fuqua School of Business and Zhengliang Xue Song, IBM - T. J. Watson Research Center explores Demand Shaping Through Bundling and Product Configuration: A Dynamic Multiproduct Inventory-Pricing Model.
ABSTRACT: We present a dynamic, multi-item model to analyze the optimal joint inventory, pricing, and bundling decisions for a firm over a finite horizon. We develop a novel demand model that transfers the discrete bundling decision and the corresponding pricing decision into a market share decision. We show that the optimal policy is dictated by a no-order set. For items in this set, we do not place replenishment orders, because these items are overstocked. The rest of the policy parameters -- the order-up-to-levels for the items that we do order, the bundling and pricing decisions, and the bundle assembly quantity -- all depend on the overstock levels. Exploring the optimal policy features, we devise a branching algorithm that significantly simplifies the computation of the optimal policy. We also characterize how the optimal bundling decision depends on item complementarity, cost structure, inventory status, demand uncertainty, and supply responsiveness.