Demand Shaping Through Bundling and Product Configuration: A Dynamic Multiproduct Inventory-Pricing Model

Jing-Sheng Song () and Zhengliang Xue ()
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Jing-Sheng Song: Fuqua School of Business, Duke University, Durham, North Carolina 27708;
Zhengliang Xue: T.J. Watson Research Center, IBM, Yorktown, New York 10598

Operations Research, 2021, vol. 69, issue 2, 525-544

Abstract: In today’s digital age, with the aid of the internet and data mining, many firms use vertically differentiated product bundling to influence demand to match up with inventory status, especially in industries with short product life cycles. Despite this practice, there is little understanding on how exactly the inventory dynamics impact the bundling strategy and, in turn, how the bundling strategy affects the firm’s inventory decisions. To fill this gap, we present a dynamic model to analyze the optimal joint replenishment, pricing, and bundling decisions over time. A key enabler of our analysis is a novel demand model that transfers the discrete bundling decision and the corresponding pricing decision into a continuous market share decision. We show that the optimal policy is dictated by a no-order set in each period. 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. We also characterize how the optimal bundling decision depends on item complementarity, cost structure, inventory levels, demand uncertainty, and supply responsiveness.

Keywords: inventory; dynamic pricing; bundle; vertical differentiation; consumer choice; market share; bundle-to-stock; bundle-to-order (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (2)

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