Newsvendor Decisions with Two-Sided Learning

Abstract

A substantial collection of work in operations management considers settings where a firm faces uncertain demand that depends on parameters that are ex ante unknown but can be learned by observing historical data. This work typically assumes that future demand is unaffected by the firm’s learning process. However, in the new era of social media, it is increasingly the case that the information used by the firm to gauge future demand (for example, sales and stockouts or consumer feedback) is now also observable to the consumers and may influence their purchase decisions. In this paper, we consider a newsvendor model where a product of ex ante unknown value is sold in an environment where learning is “two-sided” in that both the firm and the consumers learn the product’s value over time by observing the same information. The analysis establishes a consequential insight: When learning is two-sided, the value of information is often negative for the firm; as a result, the firm’ optimal stocking quantity is often lower than that under one-sided learning. Moreover, under certain conditions that we identify, we show that the optimal stocking quantity can be even lower than the critical fractile policy, in stark contrast to the recurring prescription found in existing literature of “stocking more” in the presence of learning. Additional results and numerical experiments suggest that the loss for the firm from failing to account for the two-sidedness of the learning process can be significant. This paper was accepted by Charles Corbett, operations management.