Learning to Price Supply Chain Contracts against a Learning Retailer

53 Pages Posted: 15 Nov 2022

See all articles by Xuejun Zhao

Xuejun Zhao

Purdue University

Ruihao Zhu

Cornell University

William B. Haskell

Purdue University

Date Written: October 25, 2022

Abstract

The rise of big data analytics has automated the decision-making of companies and increased supply chain agility. In this paper, we study the supply chain contract design problem faced by a data-driven supplier who needs to respond to the inventory decisions of the downstream retailer. Both the supplier and the retailer are uncertain about the market demand and need to learn about it sequentially. The goal for the supplier is to develop data-driven pricing policies with sublinear regret bounds under a wide range of possible retailer inventory policies for a fixed time horizon.

To capture the dynamics induced by the retailer's learning policy, we first make a connection to non-stationary online learning by following the notion of variation budget. The variation budget quantifies the impact of the retailer's learning strategy on the supplier's decision-making environment. We then propose dynamic pricing policies for the supplier for both discrete and continuous demand. We also note that our proposed pricing policy only requires access to the support of the demand distribution, but critically, does not require the supplier to have any prior knowledge about the retailer's learning policy or the demand realizations. We examine several well-known data-driven policies for the retailer, including sample average approximation, distributionally robust optimization, and parametric approaches, and show that our pricing policies lead to sublinear regret bounds in all these cases.

At the managerial level, we answer affirmatively that there is a pricing policy with a sublinear regret bound under a wide range of retailer's learning policies, even though she faces a learning retailer and an unknown demand distribution. Our work also provides a novel perspective in data-driven operations management where the principal has to learn to react to the learning policies employed by other agents in the system.

Keywords: online learning, supply chain contracts, data analytics

Suggested Citation

Zhao, Xuejun and Zhu, Ruihao and Haskell, William Benjamin, Learning to Price Supply Chain Contracts against a Learning Retailer (October 25, 2022). Available at SSRN: https://ssrn.com/abstract=4265195 or http://dx.doi.org/10.2139/ssrn.4265195

Xuejun Zhao (Contact Author)

Purdue University ( email )

610 Purdue Mall
West Lafayette, IN 47907
United States

Ruihao Zhu

Cornell University ( email )

Ithaca, NY 14853
United States

William Benjamin Haskell

Purdue University ( email )

610 Purdue Mall
West Lafayette, IN 47907
United States

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