Demand Learning in Two Operations Models
By: Yunru Han
Publisher: [publisher not identified]
Published: 2016
Place of publication: [New York, N.Y.?]
Language: eng
Description: The rapid advance of information technologies largely facilitated firms' data-driven decision making. Particularly, in operations management practices, firms could continuously collect information to refine their demand knowledge, and integrate this process into their relevant operational decisions, e.g. pricing, inventory, and market entry, known as demand learning. Demand learning in complex business systems is often tangled with complex strategic interactions, thus requiring a deep understanding of how it affects the strategic relationship among players in various business setups. This thesis aims to contribute to the demand-learning literature by studying the strategic interactions in two different business relationships, one vertical and the other horizontal. First, I consider the interactions between a retailer and a supplier in a supply chain subject to demand censorship (i.e. unobservable lost sales) when the retailer is engaging in demand learning through dynamic inventory experimentation.

I study the supplier's optimal wholesale prices when the retailer is in three different situations, and find that the retailer and the supply chain may actually benefit from either myopia or censorship in contrast to the existing results, due to the supplier's different collaborative or exploitative responses to the retailer's "willingness to learn". I also identify that, with demand censorship, the collaborative behavior between the players for information acquisition may improve the system's performance. Second, I study an online retail platform's learning process and entry policies as well as the independent seller's pricing distortion behavior to slow down this process, motivated by Amazon.com's unique dual role as both a marketplace and a merchant that allows it to use the transaction data generated by its third-party sellers to decide if to sell the same product itself. I developed a Bayesian statistical model for the platform's demand learning, proposed two types of heuristic entry policies for the platform owner.

The model predicts a pattern of price distortion, and describes the product offering choices made by the independent seller. These could potentially serve as testable results for empirical studies.
Notes: Department: Business.

Thesis advisor: Fangruo Chen.

Thesis (Ph.D.)--Columbia University, 2016.