About

Fernando Bernstein is the William and Sue Gross Professor of Business Administration in the Operations Management area of the Duke University Fuqua School of Business. He obtained a Ph.D. in Operations Management from the Graduate School of Business at Columbia University and joined Duke University in July 2000. Prof. Bernstein has published papers in leading journals such as Operations Research, Management Science, and Manufacturing and Service Operations Management, and has served as an Associate Editor for these journals. His teaching includes the core Operations Management course for the Weekend Executive MBA program at Duke University, along with various executive courses on operations management and health care operations. He has earned the Excellence in Teaching Award for a core course at Duke.

Research topics

• Computer Science
• Business
• Marketing
• Microeconomics
• Economics
• Finance
• Mathematics
• Industrial organization
• World Wide Web

Selected publications

• Frontiers in Operations: Show-Up Profiles for Scheduled Services: Estimation and Applications

  Manufacturing & Service Operations Management · 2026-02-18

  article1st authorCorresponding

  Problem definition: Motivated by passenger arrivals at the security checkpoint of the Raleigh-Durham International Airport, we develop methods to study arrivals to a system in which they are tied to scheduled events, such as flights. A key concept for modeling arrivals in such systems is the “show-up profile,” a probability distribution describing how far in advance passengers arrive for their flights. These profiles can be combined based on a known flight schedule to yield an aggregate passenger arrival forecast. Existing industry practice and academic work estimate show-up profiles using surveys or other data that are typically not available to U.S. airports. This motivates our study of an easy to implement and dynamic method for estimating show-up profiles. Methodology/results: We introduce an innovative solution for estimating show-up profiles using infrared-beam people-counting sensors and a structural estimation approach that does not require a mapping of passengers to flights. A direct maximum likelihood approach is intractable, but we propose a tractable approximation and prove that it yields consistent estimates of the underlying show-up profile parameters. Our approach produces forecasting results comparable to pure machine learning methods, yields significantly improved adaptive forecasts when combined with machine learning methods, and reveals empirical insights about passenger behavior variations across different times of day and flight destinations. Managerial implications: Our work presents a novel application of Internet of Things technology to service operations with incomplete data and demonstrates the value of integrating known operational structure with black box forecasting approaches. Show-up profiles are used at airports for decision making, for example, for crowd management, and our methodology has the potential to drive significant improvements in airport operations. The methods we develop can be readily applied at U.S. airports and other transportation hubs, and they can be adapted to other event-driven service environments such as theaters, healthcare facilities, and museums. History: This paper has been accepted in the Manufacturing & Service Operations Management Frontiers in Operations Initiative. Funding: This work was supported by the 2021 Triangle Impact Challenge. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2024.1575 .

  PublisherDOI

• A Customer Choice Model of Impulse Buying in Social Commerce

  Naval Research Logistics (NRL) · 2025-01-08 · 2 citations

  article1st author

  ABSTRACT Social commerce integrates user interactions and user‐generated content with commercial activities in the context of social media platforms. Examples include Instagram Shopping and TikTok Shopping, where brands attach product tags directly to their posts, enabling users to complete purchases within the social media platform. A user's on‐site purchase decision can be driven by the impulse to buy upon seeing a product, but is constrained by their limited attention. In this paper, we propose a choice model that captures users' impulsive behavior and limited attention span during purchase decisions on social media platforms. We explore a retailer's product display problem on a social media product page. We show that the size of the optimal display set can be monotone decreasing or increasing on the level of impulsiveness of the customer population, depending on the interplay between the population's inclination for impulse buying and their attention span. When the influence of impulse buying outweighs the effect of attention span, it can be optimal for the retailer to display a larger set of products. Additionally, we examine two strategies for retailers to stimulate purchases on social media platforms. One is to invest in environmental cues to enhance user impulse buying behavior, and the other considers leveraging influencer promotion of a specific product.

  PublisherDOI

• Demand Information Sharing in Online Retail Marketplaces

  SSRN Electronic Journal · 2025-01-01

  preprintOpen access1st authorCorresponding
  PublisherDOI

• Demand Information Sharing in Online Retail Marketplaces

  SSRN Electronic Journal · 2025-01-01

  preprintOpen access1st authorCorresponding
  PublisherDOI

• Show-Up Profiles for Scheduled Services: Estimation and Applications

  SSRN Electronic Journal · 2025-01-01

  preprintOpen access1st authorCorresponding
  PublisherDOI

• Distribution Strategy, Capability Investment, and Government Regulation

  Production and Operations Management · 2025-01-30 · 1 citations

  article

  This paper investigates how a manufacturer’s internal sourcing capability and government regulation influence the supplier’s distribution strategy and the manufacturer’s sourcing and capability investment decisions. We consider a supply chain consisting of a supplier that produces a critical component, an independent manufacturer that also has the capability to produce the component in-house, and a dependent manufacturer without such capability. We first consider a scenario in which the supplier chooses its distribution strategy—either offering its component to both manufacturers or establishing an exclusive selling agreement with a single manufacturer. We explore how the supplier’s optimal distribution strategy (dual or exclusive selling) depends on the terms and process of the contract and on the independent manufacturer’s ability to produce a high-quality component in-house. We also show that, in equilibrium, the independent manufacturer may invest in a high capability level (leading to a high-quality component) as a strategic deterrent against the supplier’s decision to engage in an exclusive selling agreement with the dependent manufacturer. We further consider a setting with mandatory exclusion, imposed by government regulation, that affects free trade among parties. We show that mandatory exclusion can exacerbate or mitigate the independent manufacturer’s incentive to invest in internal sourcing capability, relative to a setting in which the supplier determines its distribution strategy in the absence of such government regulations. Moreover, we show that mandatory exclusion always hurts the supplier, but it can benefit or hurt the manufacturers as well as the consumers, depending on the independent manufacturer’s investment cost and the conditions leading to an exclusionary contract under free trade.

  PublisherDOI

• Data-driven Population Tracking in Large Service Systems

  SSRN Electronic Journal · 2024-01-01 · 2 citations

  articleOpen access1st authorCorresponding
  PublisherDOI

• Managing Customer Search: Assortment Planning for a Subscription Box Service

  Manufacturing & Service Operations Management · 2023 · 12 citations

  1st authorCorresponding
  • Computer Science
  • Computer Science
  • Business

  Problem definition: This paper focuses on subscription box services in which a provider selects the assortment of products to include in the box by taking into account the customer’s preferences. Customers interested in purchasing a product choose between engaging in active search (i.e., visit physical stores) or subscribing to a box delivery service. We study the subscription box company’s problem of selecting the optimal contents of the box to maximize expected revenue (by driving demand from customers). Methodology/results: Because a product may be both available at a store and included in the box, the assortment in a box affects the set of stores that a customer would visit under active search and, therefore, the customer’s subscription decision. We model such interaction by applying a cross-nested logit framework that correlates the contents in the box with the products available at the stores. We find that the box should include a collection of popular subsets of the store products for customers that experience a relatively low or relatively high search cost. If a preview of the box is available, we find that, for customers with intermediate values of the search cost, it may be optimal to include a so-called utility loss leader, that is, a product with relatively low valuation, to entice customers to subscribe to the box delivery service and therefore increase the likelihood of a sale. We use rational expectations to model a setting in which a preview of the box is not available. In such cases, it is never optimal to include a utility loss leader in the box. Managerial implications: Our model captures the impact of product overlap across different shopping channels on customer choice and the subscription box company assortment decision. We derive insights on how the subscription service provider should determine the contents of the box in anticipation of the customer’s search behavior. We also examine the decision of offering exclusive products in addition to branded items. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.1204 .

  PublisherDOI

• Quick Response and Advance Selling in Product Line Supply Chain with Uncertain Demand

  SSRN Electronic Journal · 2023-01-01

  articleOpen access1st authorCorresponding
  PublisherDOI

• Exploration Optimization for Dynamic Assortment Personalization under Linear Preferences

  SSRN Electronic Journal · 2022-01-01 · 3 citations

  articleOpen access1st authorCorresponding
  PublisherDOI

Frequent coauthors

• Awi Federgruen

  7 shared

• Gregory A. DeCroix

  University of Wisconsin–Madison

  7 shared

• A. Gürhan Kök

  4 shared

• N. Bora Keskin

  3 shared

• Sajad Modaresi

  University of North Carolina at Chapel Hill

  3 shared

• Denis Sauré

  3 shared

• Dinah Cohen-Vernik

  2 shared

• Soudipta Chakraborty

  University of Kansas

  2 shared

Labs

• Fernando Bernstein LabPI

Awards & honors

• Excellence in Teaching Award for a core course at Duke