Pricing Analytics for Reusable Resources
By: Yunjie Sun
Publisher: [publisher not identified]
Published: 2019
Place of publication: [New York, N.Y.?]
Language: eng
Description: First, we consider a fundamental pricing model for a single type of reusable resource in which a fixed number of units are used to serve stochastically arriving customers. Customers choose to purchase the resource based on their willingness-to-pay and the current price. If purchased, occupy one unit of the reusable resources for a random amount of time. The firm seeks to maximize a weighted combination of profit, market share, and service level. We establish a series of theoretical results that characterize the strong universal performance of static pricing in such an environment. Second, we describe a comprehensive approach to pricing analytics for reusable resources in the context of rotable spare parts with an industrial partner. We discuss the process of instilling a new pricing culture and developing a scalable new pricing methodology at a major aircraft manufacturer. We develop a novel pricing analytics approach for all rotable spare parts. The new approach tackles the challenges of limited data availability, minimal demand information, and complex inventory dynamics.

We also present a successful large-scale implementation of our approach which led to significant profit gains. Third, we extend the pricing model for reusable resources to the setting of multiple customer classes. We describe two types of heuristics for this class of problem with accompanying numerical experiments. In addition, we provide a universal performance guarantee for a special case. We also discuss the role of substitution effects between different classes of customers.
Notes: Department: Industrial Engineering and Operations Research.

Thesis advisor: Adam N. Elmachtoub.

Thesis (Ph.D.)--Columbia University, 2019.