Kris Johnson Ferreira

Professor Ferreira's research primarily focuses on how retailers can use algorithms to make better revenue management decisions, including pricing, product display, and assortment planning. In the retail industry, anticipating consumer demand is arguably one of the biggest challenges for a retailer's decision making. Errors in demand predictions typically lead to poor revenue management decisions, e.g., prices being set too high or too low, leading to lost profit from either excess inventory or stock-outs. Over the last decade, with the growing amount of data retailers capture, retailers are now considering how they can use these data to help predict demand and improve revenue management decisions. Professor Ferreira develops new machine learning and optimization algorithms to predict consumer demand and improve decision making, with theoretical, numerical, and/or experimental evidence that each algorithm performs well in practice. Taken together, her work demonstrates how retailers can achieve great benefits from using data and algorithms to help them make key revenue management decisions. In addition to her algorithmic work, Professor Ferreira also conducts research that develops and analyzes mathematical models that challenge the common belief held by managers at many e-commerce companies that they should make it as easy as possible for customers to search their catalog of products and make purchases. Her work has led to two key insights. First, online retailers may benefit from making it more difficult for customers to find their most preferred products. Second, limiting purchases in an online marketplace can improve system-wide customer experience and engagement. These findings offer insights for both retailers engaging in e-commerce as well as for digital platforms providing online marketplaces. By engaging with many managers across different industries, Professor Ferreira has been exposed to how they are thinking about using data and algorithms to drive decision making. Many managers recognize that algorithms are able to process significantly more data than humans, uncovering important relationships that drive predictions and making recommendations that would be difficult for humans to identify. That said, many managers also understand that data and algorithms cannot capture all of the nuances of a decision-making problem, and that employees often have additional information or contextual knowledge that they should consider in tandem with algorithmic recommendations when making decisions. In Professor Ferreira's newest line of research, she focuses on a critical challenge faced by many managers: employees equipped with algorithmic recommendations to aid their decision making often make errors by either discounting or adhering to algorithmic recommendations when they should not do so. This is a big problem: if users do not effectively incorporate data and algorithms in their decision making, the organization will not realize the algorithms' full potential or limitations. Professor Ferreira seeks to understand the root causes underlying this poor use of algorithmic recommendations and provide advice to managers as to how the potential of those algorithms might be better realized.

The Supply Chain Management (SCM) course builds on aspects of the first-year Technology and Operations Management (RC TOM) course. However, whereas RC TOM focuses primarily on developing and producing products and services, SCM emphasizes managing product availability, especially in the context of rapid product proliferation, short product life cycles, and global networks of suppliers and customers. Hence, topics not examined in RC TOM such as distribution economics, inventory management, demand forecasting, and supply chain design are explored in depth in SCM.

SCM also differs from RC TOM in that RC TOM concentrates primarily on material and information flows within an organization, whereas SCM focuses on managing material and information flows across both functional and organizational boundaries. SCM emphasizes the "general manager’s perspective" in managing supply chains. Cases in the course illustrate that barriers to integrating supply chains often relate to managerial issues (e.g., misaligned incentives or change-management challenges) and operational execution problems (e.g., inaccurate inventory records) that fall squarely in the domain of the general manager. The course makes clear that suitable information technology and appropriate use of analytical tools are necessary, but by no means sufficient, requirements for effective supply chain management.

"Warnings and Endorsements: Improving Human-AI Collaboration Under Covariate Shift" with Matthew DosSantos DiSorbo was awarded the 2024 Junior Scholar Paper Competition Prize from the College of Behavioral Operations at the Production and Operations Management Society (POMS).

Runner up for the 2023 Best Working Paper Award from the Behavioral Operations Management Division of the Institute for Operations Research and the Management Sciences (INFORMS) for “Improving Human-Algorithm Collaboration: Causes and Mitigation of Over- and Under-Adherence” with Maya Balakrishnan and Jordan Tong.

“Improving Human-Algorithm Collaboration: Causes and Mitigation of Over- and Under-Adherence” with Maya Balakrishnan and Jordan Tong was awarded the 2023 Junior Scholar Paper Competition Prize from the College of Behavioral Operations at the Production and Operations Management Society (POMS).

Inducted into the 2023 Council of Outstanding Young Engineering Alumni.

Winner of the 2021 MSOM Best Operations Management Paper in Operations Research from the Manufacturing & Service Operations Management Society (MSOM) with David Simchi-Levi and He Wang for "Online Network Revenue Management Using Thompson Sampling" (2018).

Finalist in the 2021 Manufacturing & Service Operations Management Society (MSOM) Practice Based Research Competition with Emily Mower for "Demand Learning and Pricing for Varying Assortments."

Finalist for the 2018 and 2019 Manufacturing & Service Operations Management (M&SOM) Best Paper Award with Bin Hong Alex Lee and David Simchi-Levi for "Analytics for an Online Retailer: Demand Forecasting and Price Optimization."

Finalist for the 2019 Institute for Operations Research and the Management Sciences (INFORMS) Revenue Management and Pricing Section Practice Award with Matt Capizzi, Arthur Hong, and Emily Mower, for "Demand Learning and Dynamic Pricing for Varying Assortments: Algorithm Development and Implementation at Zenrez."

Second Place Winner of the 2015 Innovative Applications in Analytics Award from the Institute for Operations Research and the Management Sciences (INFORMS) with Bin Hong Alex Lee, David Simchi-Levi, Murali Narayanaswamy, Philip Roizin, and Jonathan Waggoner for "Analytics for an Online Retailer: Demand Forecasting and Price Optimization."

Finalist for the 2015 IBM Service Science Best Student Paper Award from the Institute for Operations Research and the Management Sciences (INFORMS) for "Online Network Revenue Management Using Thompson Sampling" (HBS Working Paper 16-031, September 2015) with He Wang, David Simchi-Levi.

Winner of the 2014 Institute for Operations Research and the Management Sciences (INFORMS) Revenue Management and Pricing Section Practice Award with Murali Narayanaswamy, Philip Roizin, Jonathan Waggoner, Bin Hong Alex Lee, and David Simchi-Levi for "Analytics for an Online Retailer: Demand Forecasting and Price Optimization."

Recipient of a 2013 Graduate Student Award for Excellence in Engineering Systems Teaching Massachusetts Institute of Technology (MIT).