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• Case | HBS Case Collection | September 2016 (Revised May 2018)

  United Airlines: More Out-and-Back Flying?

  Ryan W. Buell, Willy Shih and Mike Toffel

  This case looks at United Airlines when it is facing a decision on whether to shift its aircraft routing to more "out-and-back" routing in order to try to improve its on-time performance. As one of the world's largest airlines, United had a very large fleet and hub-and-spoke network that provided passengers with a wide range of destination choices, but as with any complex system unless everything ran perfectly all the time, it inevitably faced cascading delays and missed passenger connection problems. While out-and-back routing tended to isolate operational issues, more traditional linear routings tended to offer high equipment utilization. The case offers students an opportunity to examine the effects of variability on different routing strategies.

  Keywords: service excellence; service delivery; service management; service quality; service quality competition; services; service operations; airline industry; airlines; operational complexity; operational disruptions; operational effectiveness; operations; operations improvement; operations management; operations strategy; Air Transportation; Operations; Service Operations; Service Delivery; Performance Effectiveness; Performance Improvement; Complexity; Air Transportation Industry; United States;

  Citation:

  Buell, Ryan W., Willy Shih, and Mike Toffel. "United Airlines: More Out-and-Back Flying?" Harvard Business School Case 617-010, September 2016. (Revised May 2018.)  View Details

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• Case | HBS Case Collection | October 2017 (Revised April 2018)

  Improving Worker Safety in the Era of Machine Learning (A)

  Michael W. Toffel, Dan Levy, Jose Ramon Morales Arilla and Matthew S. Johnson

  Managers make predictions all the time: How fast will my markets grow? How much inventory do I need? How intensively should I monitor my suppliers? Which potential customers will be most responsive to a particular marketing campaign? Which job candidates should I employ? Machine learning, data science, big data, and predictive analytics all use statistical techniques to predict an outcome. This case enables students to begin using data to make predictions and teaches the core metrics to evaluate how accurate predictions are. It helps students understand how to choose among alternative model specifications and introduces the concepts of overfitting and in-sample versus out-of-sample prediction. The case discussion also promotes an understanding of factors beyond prediction accuracy—such as transparency and perceived fairness—that managers need to consider when deciding which predictive algorithm to deploy. The class discussion also helps students appreciate the differences between prediction, correlation, and causation. The case protagonist recently joined a new data science team at the U.S. Occupational Safety and Health Administration (OSHA), a government agency, and needs to evaluate and recommend one of several alternative approaches that OSHA should use to improve how it targets its government inspections of workplaces to better assure safe working conditions. The case includes a dataset and exercise.

  Keywords: Machine learning; policy implementation; Empirical Research; inspection; safety; occupational safety; occupational health; regulation; Analysis; Forecasting and Prediction; Policy; Operations; Supply Chain Management; Safety; Manufacturing Industry; Construction Industry; United States;

  Citation:

  Toffel, Michael W., Dan Levy, Jose Ramon Morales Arilla, and Matthew S. Johnson. "Improving Worker Safety in the Era of Machine Learning (A)." Harvard Business School Case 618-019, October 2017. (Revised April 2018.)  View Details

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• Supplement | HBS Case Collection | April 2018

  Improving Worker Safety in the Era of Machine Learning (B)

  Michael W. Toffel, Dan Levy, Astrid Camille Pineda, Jose Ramon Morales Arilla and Matthew S. Johnson

  Supplements the (A) case.

  Citation:

  Toffel, Michael W., Dan Levy, Astrid Camille Pineda, Jose Ramon Morales Arilla, and Matthew S. Johnson. "Improving Worker Safety in the Era of Machine Learning (B)." Harvard Business School Supplement 618-064, April 2018.  View Details

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