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      Machine Learning ModelsRemove Machine Learning Models →

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      • March 2019
      • Case

      DayTwo: Going to Market with Gut Microbiome

      By: Ayelet Israeli and David Lane
      DayTwo is a young Israeli startup that applies research on the gut microbiome and machine learning algorithms to deliver personalized nutritional recommendations to its users in order to minimize blood sugar spikes after meals. After a first year of trial rollout in... View Details
      Keywords: Start-up Growth; Startup; Positioning; Targeting; Go To Market Strategy; B2B2C; B2B Vs. B2C; Health & Wellness; AI; Machine Learning; Female Ceo; Female Protagonist; Science-based; Science And Technology Studies; Ecommerce; Applications; DTC; Direct To Consumer Marketing; US Health Care; "USA,"; Innovation; Pricing; Business Growth; Segmentation; Distribution Channels; Growth and Development Strategy; Business Startups; Science-Based Business; Health; Innovation and Invention; Marketing; Information Technology; Business Growth and Maturation; E-commerce; Applications and Software; Health Industry; Technology Industry; Insurance Industry; Information Technology Industry; Food and Beverage Industry; Israel; United States
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      Israeli, Ayelet, and David Lane. "DayTwo: Going to Market with Gut Microbiome." Harvard Business School Case 519-010, March 2019.
      • February 2019
      • Case

      Miroglio Fashion (A)

      By: Sunil Gupta and David Lane
      Francesco Cavarero, chief information officer of Miroglio Fashion, Italy’s third-largest retailer of women’s apparel, was trying to bring analytical rigor to the company’s forecasting and inventory management decisions. But fashion is inherently hard to predict. Can... View Details
      Keywords: Inventory Management; Demand Forecasting; Artificial Intelligence; Machine Learning; Forecasting and Prediction; Operations; Management; Decision Making; AI and Machine Learning; Apparel and Accessories Industry; Fashion Industry
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      Gupta, Sunil, and David Lane. "Miroglio Fashion (A)." Harvard Business School Case 519-053, February 2019.
      • 2020
      • Working Paper

      Overcoming the Cold Start Problem of CRM Using a Probabilistic Machine Learning Approach

      By: Eva Ascarza
      The success of Customer Relationship Management (CRM) programs ultimately depends on the firm's ability to understand consumers' preferences and precisely capture how these preferences may differ across customers. Only by understanding customer heterogeneity, firms can... View Details
      Keywords: Customer Management; Targeting; Deep Exponential Families; Probabilistic Machine Learning; Cold Start Problem; Customer Relationship Management; Customer Value and Value Chain; Consumer Behavior; Analytics and Data Science; Mathematical Methods; Retail Industry
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      Padilla, Nicolas, and Eva Ascarza. "Overcoming the Cold Start Problem of CRM Using a Probabilistic Machine Learning Approach." Harvard Business School Working Paper, No. 19-091, February 2019. (Revised May 2020. Accepted at the Journal of Marketing Research.)
      • Article

      Multivariate Unsupervised Machine Learning for Anomaly Detection in Enterprise Applications

      By: Daniel Elsner, Pouya Aleatrati Khosroshahi, Alan MacCormack and Robert Lagerström
      Existing application performance management (APM) solutions lack robust anomaly detection capabilities and root cause analysis techniques that do not require manual efforts and domain knowledge. In this paper, we develop a density-based unsupervised machine learning... View Details
      Keywords: Big Data; Data Science And Analytics Management; Governance And Compliance; Organizational Systems And Technology; Anomaly Detection; Application Performance Management; Machine Learning; Enterprise Architecture; Analytics and Data Science
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      Elsner, Daniel, Pouya Aleatrati Khosroshahi, Alan MacCormack, and Robert Lagerström. "Multivariate Unsupervised Machine Learning for Anomaly Detection in Enterprise Applications." Proceedings of the Hawaii International Conference on System Sciences 52nd (2019): 5827–5836.
      • 2019
      • Article

      Fair Algorithms for Learning in Allocation Problems

      By: Hadi Elzayn, Shahin Jabbari, Christopher Jung, Michael J Kearns, Seth Neel, Aaron Leon Roth and Zachary Schutzman
      Settings such as lending and policing can be modeled by a centralized agent allocating a scarce resource (e.g. loans or police officers) amongst several groups, in order to maximize some objective (e.g. loans given that are repaid, or criminals that are apprehended).... View Details
      Keywords: Allocation Problems; Algorithms; Fairness; Learning
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      Elzayn, Hadi, Shahin Jabbari, Christopher Jung, Michael J Kearns, Seth Neel, Aaron Leon Roth, and Zachary Schutzman. "Fair Algorithms for Learning in Allocation Problems." Proceedings of the Conference on Fairness, Accountability, and Transparency (2019): 170–179.
      • January 2019 (Revised October 2019)
      • Case

      Liulishuo: AI English Teacher

      By: John J-H Kim and Shu Lin
      Educators and entrepreneurs alike are excited about the potential for artificial intelligence (AI) and machine learning to change the way learning will look like in the future. There is a confluence of factors such as the availability of large sources of rich,... View Details
      Keywords: AI; Artificial Intelligence; Education Technology; Information Technology; Education; Entrepreneurship; AI and Machine Learning; Education Industry; China
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      Kim, John J-H, and Shu Lin. "Liulishuo: AI English Teacher." Harvard Business School Case 319-090, January 2019. (Revised October 2019.)
      • 2019
      • Article

      An Empirical Study of Rich Subgroup Fairness for Machine Learning

      By: Michael J Kearns, Seth Neel, Aaron Leon Roth and Zhiwei Steven Wu
      Kearns et al. [2018] recently proposed a notion of rich subgroup fairness intended to bridge the gap between statistical and individual notions of fairness. Rich subgroup fairness picks a statistical fairness constraint (say, equalizing false positive rates across... View Details
      Keywords: Machine Learning; Fairness; AI and Machine Learning
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      Kearns, Michael J., Seth Neel, Aaron Leon Roth, and Zhiwei Steven Wu. "An Empirical Study of Rich Subgroup Fairness for Machine Learning." Proceedings of the Conference on Fairness, Accountability, and Transparency (2019): 100–109.
      • Article

      Faithful and Customizable Explanations of Black Box Models

      By: Himabindu Lakkaraju, Ece Kamar, Rich Caruana and Jure Leskovec
      As predictive models increasingly assist human experts (e.g., doctors) in day-to-day decision making, it is crucial for experts to be able to explore and understand how such models behave in different feature subspaces in order to know if and when to trust them. To... View Details
      Keywords: Interpretable Machine Learning; Black Box Models; Decision Making; Framework
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      Lakkaraju, Himabindu, Ece Kamar, Rich Caruana, and Jure Leskovec. "Faithful and Customizable Explanations of Black Box Models." Proceedings of the AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society (2019).
      • Article

      From Orientation to Behavior: The Interplay Between Learning Orientation, Open-mindedness, and Psychological Safety in Team Learning

      By: Jean-François Harvey, Kevin J. Johnson, Kathryn S. Roloff and Amy C. Edmondson
      Do teams with motivation to learn actually engage in the behaviors that produce learning? Though team learning orientation has been found to be positively related to team learning, we know little about how and when it actually fosters team learning. It is obviously not... View Details
      Keywords: Emergent States; Goal Orientation; Open-mindedness; Psychological Safety; Team Learning; Teams; Groups and Teams; Learning; Goals and Objectives
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      Harvey, Jean-François, Kevin J. Johnson, Kathryn S. Roloff, and Amy C. Edmondson. "From Orientation to Behavior: The Interplay Between Learning Orientation, Open-mindedness, and Psychological Safety in Team Learning." Human Relations 72, no. 11 (November 2019): 1726–1751.
      • December 2018 (Revised March 2021)
      • Background Note

      Modern Automation (A): Artificial Intelligence

      By: William R. Kerr and James Palano
      This primer is meant to be a field guide to the late 2010s' surge in business use of "Artificial Intelligence" (AI), or enterprise software based in machine learning. First, it provides an overview of the key trends—digitization, connectivity, the continuation of... View Details
      Keywords: Artificial Intelligence; Digitization; Connectivity; Computing; Future Of Work; Automation; AI and Machine Learning
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      Kerr, William R., and James Palano. "Modern Automation (A): Artificial Intelligence." Harvard Business School Background Note 819-084, December 2018. (Revised March 2021.)
      • December 2018
      • Teaching Note

      Autonomous Vehicles: The Rubber Hits the Road…but When?

      By: William Kerr and James Palano
      The autonomous vehicles have enormous implications for business and society. But, despite the headline-laden attention paid to the technology, there remain more questions than answers. Students will learn about the complex industry and have explicit discussions about... View Details
      Keywords: Technology Management; Artificial Intelligence; General Management; Robotics; Technological Innovation; Transportation; Disruption; Information Technology; Management; Decision Making; AI and Machine Learning; Auto Industry; Technology Industry
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      Kerr, William, and James Palano. "Autonomous Vehicles: The Rubber Hits the Road…but When?" Harvard Business School Teaching Note 819-040, December 2018.
      • December 2018
      • Case

      Choosy

      By: Jeffrey J. Bussgang and Julia Kelley
      Founded in 2017, Choosy is a data-driven fashion startup that uses algorithms to identify styles trending on social media. After manufacturing similar items using a China-based supply chain, Choosy sells them to consumers through its website and social media pages.... View Details
      Keywords: Artificial Intelligence; Algorithms; Machine Learning; Neural Networks; Instagram; Influencer; Fast Fashion; Design; Customer Satisfaction; Customer Focus and Relationships; Decision Making; Cost vs Benefits; Innovation and Invention; Brands and Branding; Product Positioning; Demand and Consumers; Supply Chain; Production; Logistics; Business Model; Expansion; Internet and the Web; Mobile and Wireless Technology; Digital Platforms; Social Media; Technology Industry; Fashion Industry; North and Central America; United States; New York (state, US); New York (city, NY)
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      Bussgang, Jeffrey J., and Julia Kelley. "Choosy." Harvard Business School Case 819-054, December 2018.
      • December 2018
      • Case

      Bata versus Relaxo—Analyzing Performance

      By: Suraj Srinivasan, Iris Leung and Quinn Pitcher
      Set in 2016, “Bata India versus Relaxo—Analyzing Performance” compares the strategies and financial performance of two Indian footwear companies. Bata India had long been the market leader in footwear in India, but its leading market position was being challenged by... View Details
      Keywords: Finance; Strategy; Operations; Performance Evaluation; Financial Statements; Analysis; Apparel and Accessories Industry
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      Srinivasan, Suraj, Iris Leung, and Quinn Pitcher. "Bata versus Relaxo—Analyzing Performance." Harvard Business School Case 119-050, December 2018.
      • 2018
      • Working Paper

      Diagnostic Bubbles

      By: Pedro Bordalo, Nicola Gennaioli, Spencer Yongwook Kwon and Andrei Shleifer
      We introduce diagnostic expectations into a standard setting of price formation in which investors learn about the fundamental value of an asset and trade it. We study the interaction of diagnostic expectations with two well-known mechanisms: learning from prices and... View Details
      Keywords: Bubbles; Price Bubble; Mathematical Methods
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      Bordalo, Pedro, Nicola Gennaioli, Spencer Yongwook Kwon, and Andrei Shleifer. "Diagnostic Bubbles." NBER Working Paper Series, No. 25399, December 2018.
      • November–December 2018
      • Article

      Online Network Revenue Management Using Thompson Sampling

      By: 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... View Details
      Keywords: Online Marketing; Revenue Management; Revenue; Management; Marketing; Internet and the Web; Price; Mathematical Methods
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      Ferreira, Kris J., David Simchi-Levi, and He Wang. "Online Network Revenue Management Using Thompson Sampling." Operations Research 66, no. 6 (November–December 2018): 1586–1602.
      • October 2018
      • Case

      Shield AI

      By: Mitchell Weiss and A.J. Steinlage
      Shield AI’s quadcopter – with no pilot and no flight plan – could clear a building and outpace human warfighters by almost five minutes. This was not to say that it was better than the warfighters or would replace their jobs, but it was evidence that autonomous robots... View Details
      Keywords: Public Entrepreneurship; Artificial Intelligence; AI; Entrepreneurial Sales; Government; Defense; Shield AI; Brandon Tseng; Ryan Tseng; Andrew Reiter; Robots; Robotics; UAV; UAVs; Government Sales; Entrepreneurship; Public Sector; Sales; Government Administration; National Security; Business and Government Relations; AI and Machine Learning; Technology Industry; United States
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      Weiss, Mitchell, and A.J. Steinlage. "Shield AI." Harvard Business School Case 819-062, October 2018.
      • October 2018
      • Case

      American Family Insurance and the Artificial Intelligence Opportunity

      By: Rajiv Lal and Scott Johnson
      Keywords: Artificial Intelligence; Machine Learning; Automation; Analytics; American Family; American Family Insurance; Insurance; Business Organization; Transformation; Talent and Talent Management; Employee Relationship Management; Innovation Strategy; Job Cuts and Outsourcing; Risk and Uncertainty; Mobile and Wireless Technology; Technology Adoption; Internet and the Web; Applications and Software; Corporate Strategy; AI and Machine Learning; Digital Transformation; Insurance Industry; Technology Industry; Wisconsin
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      Lal, Rajiv, and Scott Johnson. "American Family Insurance and the Artificial Intelligence Opportunity." Harvard Business School Case 519-028, October 2018.
      • October 2018
      • Article

      The Operational Value of Social Media Information

      By: Ruomeng Cui, Santiago Gallino, Antonio Moreno and Dennis J. Zhang
      While the value of using social media information has been established in multiple business contexts, the field of operations and supply chain management have not yet explored the possibilities it offers in improving firms' operational decisions. This study attempts to... View Details
      Keywords: Machine Learning; Information; Sales; Forecasting and Prediction; Social Media
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      Cui, Ruomeng, Santiago Gallino, Antonio Moreno, and Dennis J. Zhang. "The Operational Value of Social Media Information." Special Issue on Big Data in Supply Chain Management. Production and Operations Management 27, no. 10 (October 2018): 1749–1774.
      • September 2018 (Revised December 2019)
      • Case

      Zebra Medical Vision

      By: Shane Greenstein and Sarah Gulick
      An Israeli startup founded in 2014, Zebra Medical Vision developed algorithms that produced diagnoses from X-rays, mammograms, and CT-scans. The algorithms used deep learning and digitized radiology scans to create software that could assist doctors in making... View Details
      Keywords: Radiology; Machine Learning; X-ray; CT Scan; Medical Technology; Probability; FDA 510(k); Diagnosis; Business Startups; Health Care and Treatment; Information Technology; Applications and Software; Competitive Strategy; Product Development; Commercialization; Decision Choices and Conditions; Health Industry; Medical Devices and Supplies Industry; Technology Industry; Israel
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      Greenstein, Shane, and Sarah Gulick. "Zebra Medical Vision." Harvard Business School Case 619-014, September 2018. (Revised December 2019.)
      • September 2018
      • Article

      Discretionary Task Ordering: Queue Management in Radiological Services

      By: Maria Ibanez, Jonathan R. Clark, Robert S. Huckman and Bradley R. Staats
      Work-scheduling research typically prescribes task sequences implemented by managers. Yet employees often have discretion to deviate from their prescribed sequence. Using data from 2.4 million radiological diagnoses, we find that doctors prioritize similar tasks... View Details
      Keywords: Discretion; Scheduling; Queue; Healthcare; Learning; Experience; Decentralization; Operations; Service Operations; Service Delivery; Performance; Performance Effectiveness; Performance Efficiency; Performance Improvement; Performance Productivity; Decisions; Time Management; Cost vs Benefits; Health Industry
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      Ibanez, Maria, Jonathan R. Clark, Robert S. Huckman, and Bradley R. Staats. "Discretionary Task Ordering: Queue Management in Radiological Services." Management Science 64, no. 9 (September 2018): 4389–4407. (Working paper available here. Winner of the 2017 Best Paper Competition of the POMS College of Healthcare Operations Management. Featured in Forbes, Quartz, and Inc.)
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