Knowledge that Transforms

Discrete‐choice models are widely used to model consumer purchase behavior in assortment optimization and revenue management. In many applications, each customer segment is associated with a consideration set that represents the set of products that customers in this segment consider for purchase. The firm has to make a decision on what assortment to offer at each point in time without the ability to identify the customer's segment. A linear program called the Choice‐based Deterministic Linear Program ( CDLP) has been proposed to determine these offer sets. Unfortunately, its size grows exponentially in the number of products and it is NP‐hard to solve when the consideration sets of the segments overlap. The Segment‐based Deterministic Concave Program with some additional consistency equalities ( SDCP+) is an approximation of CDLP that provides an upper bound on CDLP's optimal objective value. SDCP+ can be solved in a fraction of the time required to solve CDLP and often achieves the same optimal objective value. This raises the question under what conditions can one guarantee equivalence of CDLP and SDCP+. In this study, we obtain a structural result to this end, namely that if the segment consideration sets overlap with a certain tree structure or if they are fully nested, CDLP can be equivalently replaced with SDCP+. We give a number of examples from the literature where this tree structure arises naturally in modeling customer behavior.

In the automotive industry, many firms source key components from different suppliers, even though the components may function interdependently. In this study, we investigate how component level interdependence impacts quality performance and analyze how various operational factors moderate this relation. We synthesize information from several case studies to model the quality challenges faced by an automotive firm. For several sub‐assemblies that go into its products, the firm sourced key components from two different suppliers. The sub‐assemblies would fail whenever a component fails, but due to interdependent operations, failure of one component could cause the failure of the other. The firm found it challenging to improve the suppliers' quality performance as it was difficult to trace the failures to specific components. Our analysis reveals that – (i) the impact of interdependence is governed by the supply chain structure: reducing the interdependence between components improves quality when suppliers provide the components, but reducing interdependence worsens quality when the firm manufactures the entire sub‐assembly; and (ii) the relation between interdependence and quality performance is moderated by factors such as penalties, production costs, and interdependence costs. Additionally, we find that quality performance is lower when the firm outsources the components than when the firm manufactures the entire sub‐assembly. We identify coordinating mechanisms that leverage incentives and penalties to bridge the quality performance gap.

The increasing pervasiveness of social networks allows users to share purchase behaviors with their online friends. In this study, we examine optimal pricing strategies of a monopolistic firm using an analytical model that accounts for behavioral observational learning in social networks. We show that a seller could potentially control the information available to future customers and induce behavioral observational learning, using an information‐revealing pricing strategy. This result suggests that offering introductory discounts is not always an effective method to boost purchases in social networks. It could prevent the behavioral observational learning that would increase future customers' willingness to pay.