Kris Johnson Ferreira - Faculty & Research - Harvard Business School
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Kris Johnson Ferreira

Assistant Professor of Business Administration

Technology and Operations Management

Kris Ferreira is an assistant professor of business administration in the Technology and Operations Management (TOM) Unit and teaches the TOM course in the MBA required curriculum.

In her research, Professor Ferreira focuses on online markets and retail, seeking to build operations management models that inform practice, as well as to provide theoretical insights. In particular, she employs a combination of machine learning and optimization techniques to help companies use their data to make better tactical and strategic decisions. Her work with the online retailer Rue La La received the 2014 INFORMS Revenue Management and Pricing Section Practice Award and was named a finalist in the 2015 Innovative Applications in Analytics competition.

Professor Ferreira earned her PhD in operations research at the Massachusetts Institute of Technology and her BS in industrial and systems engineering at the Georgia Institute of Technology. Before entering graduate school, she was a supply chain consultant for Alvarez & Marsal and a project manager for UPS Supply Chain Solutions.

Journal Articles
  1. Online Network Revenue Management Using Thompson Sampling

    Kris J. Ferreira, David Simchi-Levi and He Wang

    We consider a network revenue management problem where an online retailer aims to maximize revenue from multiple products with limited inventory constraints. As common in practice, the retailer does not know the consumer's purchase probability at each price and must learn the mean demand from sales data. We propose an efficient and effective dynamic pricing algorithm, which builds upon the Thompson sampling algorithm used for multi-armed bandit problems by incorporating inventory constraints into the model and algorithm. Our algorithm proves to have both strong theoretical performance guarantees and promising numerical performance results when compared to other algorithms developed for the same setting. More broadly, our paper contributes to the literature on the multi-armed bandit problem with resource constraints, since our algorithm applies directly to this setting when the inventory constraint is interpreted as a general resource constraint.

    Keywords: Revenue; online marketing; revenue management; Revenue; Management; Marketing; Internet; Price; Mathematical Methods;


    Ferreira, Kris J., David Simchi-Levi, and He Wang. "Online Network Revenue Management Using Thompson Sampling." Operations Research (forthcoming).  View Details
  2. Analytics for an Online Retailer: Demand Forecasting and Price Optimization

    Kris J. Ferreira, Bin Hong Alex Lee and David Simchi-Levi

    We present our work with an online retailer, Rue La La, as an example of how a retailer can use its wealth of data to optimize pricing decisions on a daily basis. Rue La La is in the online fashion sample sales industry, where they offer extremely limited-time discounts on designer apparel and accessories. One of the retailer's main challenges is pricing and predicting demand for products that it has never sold before, which account for the majority of sales and revenue. To tackle this challenge, we use machine learning techniques to estimate historical lost sales and predict future demand of new products. The nonparametric structure of our demand prediction model, along with the dependence of a product's demand on the price of competing products, pose new challenges on translating the demand forecasts into a pricing policy. We develop an algorithm to efficiently solve the subsequent multi-product price optimization that incorporates reference price effects, and we create and implement this algorithm into a pricing decision support tool for Rue La La's daily use. We conduct a field experiment and find that sales do not decrease due to implementing tool recommended price increases for medium and high price point products. Finally, we estimate an increase in revenue of the test group by approximately 9.7% with an associated 90% confidence interval of (2.3%, 17.8%).

    Keywords: Online Technology; Price; Forecasting and Prediction; Revenue; Sales; Retail Industry;


    Ferreira, Kris J., Bin Hong Alex Lee, and David Simchi-Levi. "Analytics for an Online Retailer: Demand Forecasting and Price Optimization." Manufacturing & Service Operations Management 18, no. 1 (Winter 2016): 69–88.  View Details
  3. Analyzing Scrip Systems

    Kris Johnson, David Simchi-Levi and Peng Sun

    Scrip systems provide a nonmonetary trade economy for exchange of resources. We model a scrip system as a stochastic game and study system design issues on selection rules to match potential trade partners over time. We show the optimality of one particular rule in terms of maximizing social welfare for a given scrip system that guarantees players' incentives to participate. We also investigate the optimal number of scrips to issue under this rule. In particular, if the time discount factor is close enough to one, or trade benefits one partner much more than it costs the other, the maximum social welfare is always achieved no matter how many scrips are in the system. When the benefit of trade and time discount are not sufficiently large, on the other hand, injecting more scrips in the system hurts most participants; as a result, there is an upper bound on the number of scrips allowed in the system, above which some players may default. We show that this upper bound increases with the discount factor as well as the ratio between the benefit and cost of service. Finally, we demonstrate similar properties for a different service provider selection rule that has been analyzed in previous literature.

    Keywords: "Repeated games"; stochastic trust game; dynamic program; game theory; P2P Lending; scrip systems; artificial currency; non-monetary trade economies; Marketplace Matching; Currency; Operations; Game Theory;


    Johnson, Kris, David Simchi-Levi, and Peng Sun. "Analyzing Scrip Systems." Operations Research 62, no. 3 (May–June 2014): 524–534.  View Details
Working Papers
  1. Intermediation in the Supply of Agricultural Products in Developing Economies

    Kris Johnson Ferreira, Joel Goh and Ehsan Valavi

    Problem Definition: Farmers face several challenges in agricultural supply chains in emerging economies that contribute to extreme levels of poverty. One common challenge is that farmers only have access to one channel, often an auction, for which to sell their crops. Recently, e-intermediaries have emerged as alternate, technology-driven posted-price channels. We aim to develop insights into the structural drivers of farmer and supply chain profitability in emerging markets and understand the impact of e-intermediaries.
    Academic / Practical Relevance: In practice, much attention has been given to e-intermediaries and they have often been touted as for-profit social enterprises that improve farmers' welfare. Yet, studies in the operations literature that systematically analyze the impact of e-intermediaries are lacking. Our work fills this gap and answers practical questions regarding the responsible operations of e-intermediaries.
    Methodology: We develop an analytical model of a supply chain that allows us to study several key features of intermediated supply chains. We complement the model's insights with observations from a numerical study.
    Results: In the absence of an e-intermediary, auctions cause farmers to either overproduce or underproduce compared to their ideal production levels in a vertically integrated chain. The presence of an e-intermediary with limited market share improves farmers' profits; however, if the e-intermediary grows too large, it negatively impacts both farmers' and supply chain profits. Finally, as the number of farmers increases, farmers' profits approach zero, irrespective of the e-intermediary's presence.
    Managerial Implications: Our results provide a balanced perspective on the value of e-intermediation, compared to the generally positive views advanced by case studies. For-profit e-intermediaries that also aim to improve farmers' livelihoods cannot blindly operate as pure profit-maximizers, assuming that market forces alone will ensure that farmers benefit. Even when e-intermediation benefits farmers, it is insufficient to mitigate the negative effects of supply fragmentation, suggesting that for farmers, market power is more important than market access.

    Keywords: developing countries; agricultural supply chain; Intermediation; multiple cahnels; Walrasian auction; Developing Countries and Economies; Supply Chain; Distribution Channels; Profit; Agriculture and Agribusiness Industry;


    Ferreira, Kris Johnson, Joel Goh, and Ehsan Valavi. "Intermediation in the Supply of Agricultural Products in Developing Economies." Harvard Business School Working Paper, No. 18-033, October 2017.  View Details
  2. Assortment Rotation and the Value of Concealment

    Kris Johnson Ferreira and Joel Goh

    Assortment rotation—the retailing practice of changing the assortment of products offered to customers—has recently been used as a competitive advantage for both brick-and-mortar and online retailers. Fast-fashion retailers have differentiated themselves by rotating their assortment multiple times throughout a standard selling season. Interestingly, the entire online flash sales industry was created using this idea as a cornerstone of its business strategy. In this paper, we identify and investigate a new reason why frequent assortment rotations can be valuable to a retailer, particularly for products where consumers typically purchase multiple products in a given category during a selling season. Namely, by distributing its seasonal catalog of products over multiple assortments rotated throughout the season—as opposed to selling all products in a single assortment—the retailer effectively conceals a portion of its full product catalog from consumers. This injects uncertainty into the consumer's relative product valuations since she is unable to observe the entire catalog of products that the retailer will sell that season. Rationally acting consumers may respond to this additional uncertainty by purchasing more products, thereby generating additional sales for the retailer. We refer to this phenomenon as the value of concealment. A negative value of concealment is possible and represents the event that rationally acting consumers respond to the additional uncertainty by purchasing fewer products. We develop a consumer choice model and finite-horizon stochastic dynamic program to study when the value of concealment is positive or negative. We show that when consumers are myopic, the value of concealment is always positive. In contrast, we show that when consumers are strategic, the value of concealment is context dependent; we present insights and discuss intuition regarding which product categories likely lead to positive vs. negative values of concealment.

    Keywords: assortment planning; strategic consumers; consumer choice; Strategy; Consumer Behavior; Operations; Sales; Retail Industry;


    Ferreira, Kris Johnson, and Joel Goh. "Assortment Rotation and the Value of Concealment." Harvard Business School Working Paper, No. 17-041, November 2016. (Revised January 2018.)  View Details
Cases and Teaching Materials
  1. Flashion: Art vs. Science in Fashion Retailing

    Kris Ferreira and Karim Lakhani

    Kate Wilson, retail analytics manager at Flashion, a fashion flash-sale site, is tasked with developing analytics to optimize pricing for first-exposure products on the site. Many in the industry have relied on years of experience and intuition to determine pricing—can Wilson provide new insights?

    Keywords: analytics; e-commerce; pricing; data; Service Operations; Forecasting and Prediction; Internet; Online Technology; Technology Adoption; Mathematical Methods; Decision Making; Retail Industry; Fashion Industry; United States;


    Ferreira, Kris, and Karim Lakhani. "Flashion: Art vs. Science in Fashion Retailing." Harvard Business School Case 617-059, March 2017.  View Details