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  • August 2021
  • Supplement

Coats: Supply Chain Challenges
By: Willy C. Shih
Coats, the largest thread maker in the world, transformed its business to digital colour measurement so that it could respond better to customer demand in the garment industry for rapid product cycles and more fragmented colour choices. Its embrace of digital colour...  View Details
Keywords: Inventory Management; Supply Chain; Inventory; Supply Chain Management; Operations; Apparel and Accessories Industry; Asia
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  • August 2021
  • Supplement

Coats: Supply Chain Challenges: Spreadsheet Supplement
By: Willy C. Shih
Coats, the largest thread maker in the world, transformed its business to digital colour measurement so that it could respond better to customer demand in the garment industry for rapid product cycles and more fragmented colour choices. Its embrace of digital colour...  View Details
Keywords: Inventory Management; Supply Chain; Inventory; Supply Chain Management; Operations; Growth and Development Strategy; Forecasting and Prediction; Demand and Consumers; Consolidation; Apparel and Accessories Industry; Asia
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  • June 2021
  • Technical Note

Introduction to Linear Regression
By: Michael Parzen and Paul J. Hamilton
This technical note introduces (from an applied point of view) the theory and application of simple and multiple linear regression. The motivation for the model is introduced, as well as how to interpret the summary output with regard to prediction and statistical...  View Details
Keywords: Analysis; Forecasting and Prediction; Risk and Uncertainty; Theory
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  • May 2021 (Revised July 2021)
  • Case

Coats: Supply Chain Challenges
By: Willy C. Shih and Adina Wong
Coats, the largest thread maker in the world, transformed its business to digital colour measurement so that it could respond better to customer demand in the garment industry for rapid product cycles and more fragmented colour choices. Its embrace of digital colour...  View Details
Keywords: Inventory Management; Supply Chains; Digital; Operations; Supply Chain Management; Apparel and Accessories Industry; Asia
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  • 2020
  • Working Paper

Is Accounting Useful for Forecasting GDP Growth? A Machine Learning Perspective
By: Srikant Datar, Apurv Jain, Charles C.Y. Wang and Siyu Zhang
We provide a comprehensive examination of whether, to what extent, and which accounting variables are useful for improving the predictive accuracy of GDP growth forecasts. We leverage statistical models that accommodate a broad set of (341) variables—outnumbering the...  View Details
Keywords: Big Data; Elastic Net; GDP Growth; Machine Learning; Macro Forecasting; Short Fat Data; Accounting; Economic Growth; Forecasting and Prediction
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  • February 2021
  • Tutorial

Assessing Prediction Accuracy of Machine Learning Models
By: Michael Toffel and Natalie Epstein
This video describes how to assess the accuracy of machine learning prediction models, primarily in the context of machine learning models that predict binary outcomes, such as logistic regression, random forest, or nearest neighbor models. After introducing and...  View Details
Keywords: Machine Learning; Statistics; Experiments; Forecasting and Prediction; Performance Evaluation
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  • 2021
  • Working Paper

Real Credit Cycles
By: Pedro Bordalo, Nicola Gennaioli, Andrei Shleifer and Stephen J. Terry
We incorporate diagnostic expectations, a psychologically founded model of overreaction to news, into a workhorse business cycle model with heterogeneous firms and risky debt. A realistic degree of diagnosticity, estimated from the forecast errors of managers of U.S....  View Details
Keywords: Econometric Models; Business Cycles; Credit
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  • January 2021
  • Article

Using Models to Persuade
By: Joshua Schwartzstein and Adi Sunderam
We present a framework where "model persuaders" influence receivers’ beliefs by proposing models that organize past data to make predictions. Receivers are assumed to find models more compelling when they better explain the data, fixing receivers’ prior beliefs. Model...  View Details
Keywords: Model Persuasion; Data and Data Sets; Forecasting and Prediction; Mathematical Methods; Framework
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  • October 2020 (Revised March 2021)
  • Supplement

Migros Turkey: Scaling Online Operations During COVID-19 (C)
By: Antonio Moreno and Gamze Yucaoglu
The case opens in August 2020 as Ozgur Tort and Mustafa Bartin, CEO and chief large-format and online retail officer of Migros Ticaret A.S. (Migros), Turkey’s oldest and one of its largest supermarket chains, are navigating Migros through COVID-19 and the unprecedented...  View Details
Keywords: Business Model; Strategy; Technology Platform; Information Technology; Technology Adoption; Value Creation; Globalization; Competition; Expansion; Logistics; Profit; Resource Allocation; Diversification; Corporate Strategy; Crisis Management; Health Pandemics; Strategic Planning; Food and Beverage Industry; Turkey
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  • August 2020 (Revised September 2020)
  • Technical Note

Assessing Prediction Accuracy of Machine Learning Models
By: Michael W. Toffel, Natalie Epstein, Kris Ferreira and Yael Grushka-Cockayne
The note introduces a variety of methods to assess the accuracy of machine learning prediction models. The note begins by briefly introducing machine learning, overfitting, training versus test datasets, and cross validation. The following accuracy metrics and tools...  View Details
Keywords: Machine Learning; Statistics; Econometric Analyses; Experimental Methods; Data Analysis; Data Analytics; Forecasting and Prediction; Data and Data Sets; Analysis; Mathematical Methods
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  • June 2020 (Revised November 2020)
  • Case

Vanguard Retail Operations (A)
By: Willy C. Shih and Antonio Moreno
The first two cases in this series are set in the financial services industry, and explore whether it is better for back-office workers to be generalists who provide the flexibility of being able to handle the complete range of transactions that the company faces or...  View Details
Keywords: Pooling; Generalist Model; Specialist Model; Operations; Service Operations; Management; Financial Services Industry; United States
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  • June 2020 (Revised August 2020)
  • Supplement

Vanguard Retail Operations (B)
By: Willy C. Shih and Antonio Moreno
The first two cases in this series are set in the financial services industry, and explore whether it is better for back-office workers to be generalists who provide the flexibility of being able to handle the complete range of transactions that the company faces or...  View Details
Keywords: Pooling; Generalist Model; Specialist Model; Service Operations; Management; Financial Services Industry; United States
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  • March 2020
  • Supplement

People Analytics at Teach For America (B)
By: Jeffrey T. Polzer and Julia Kelley
This is a supplement to the People Analytics at Teach For America (A) case. In this supplement, situated one year after the A case, Managing Director Michael Metzger must decide how to apply his team's predictive models generated from the previous year’s data.  View Details
Keywords: Analytics; Human Resource Management; Data; Workforce; Hiring; Talent Management; Forecasting; Predictive Analytics; Organizational Behavior; Recruiting; Data and Data Sets; Forecasting and Prediction; Recruitment; Selection and Staffing; Talent and Talent Management
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  • 2020
  • Working Paper

Market Segmentation Trees
By: Ali Aouad, Adam Elmachtoub, Kris Ferreira and Ryan McNellis
As more commerce and media consumption are being conducted online, a wealth of new opportunities are emerging for personalized advertising. We propose a general methodology, Model Trees for Personalization (MTP), for tackling a broad class of personalized...  View Details
Keywords: Decision Trees; Computational Advertising; Market Segmentation; Online Advertising; Customization and Personalization; Segmentation; Decision Making
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  • 2019
  • Working Paper

Reflexivity in Credit Markets
By: Robin Greenwood, Samuel G. Hanson and Lawrence J. Jin
Reflexivity is the idea that investors' biased beliefs affect market outcomes, and that market outcomes in turn affect investors' beliefs. We develop a behavioral model of the credit cycle featuring such a two-way feedback loop. In our model, investors form beliefs...  View Details
Keywords: Reflexivity; Attitudes; Financial Markets; Forecasting and Prediction; Investment; Credit
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  • 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: Social Media; Machine Learning; Information; Sales; Forecasting and Prediction
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  • August 2018 (Revised September 2018)
  • Supplement

Predicting Purchasing Behavior at PriceMart (B)
By: Srikant M. Datar and Caitlin N. Bowler
Supplements the (A) case. In this case, Wehunt and Morse are concerned about the logistic regression model overfitting to the training data, so they explore two methods for reducing the sensitivity of the model to the data by regularizing the coefficients of the...  View Details
Keywords: Data Science; Data and Data Sets; Analysis; Customers; Household; Forecasting and Prediction
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  • August 2018 (Revised September 2018)
  • Case

LendingClub (A): Data Analytic Thinking (Abridged)
By: Srikant M. Datar and Caitlin N. Bowler
LendingClub was founded in 2006 as an alternative, peer-to-peer lending model to connect individual borrowers to individual investor-lenders through an online platform. Since 2014 the company has worked with institutional investors at scale. While the company assigns...  View Details
Keywords: Data Science; Data Analytics; Investing; Loans; Investment; Financing and Loans; Data and Data Sets; Analysis; Forecasting and Prediction; Business Model
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  • August 2018 (Revised September 2018)
  • Supplement

LendingClub (B): Decision Trees & Random Forests
By: Srikant M. Datar and Caitlin N. Bowler
This case builds directly on the LendingClub (A) case. In this case students follow Emily Figel as she builds two tree-based models using historical LendingClub data to predict, with some probability, whether borrower will repay or default on his loan.
...  View Details
Keywords: Data Science; Data Analytics; Decision Trees; Investment; Financing and Loans; Data and Data Sets; Analysis; Forecasting and Prediction
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  • August 2018 (Revised September 2018)
  • Supplement

LendingClub (C): Gradient Boosting & Payoff Matrix
By: Srikant M. Datar and Caitlin N. Bowler
This case builds directly on the LendingClub (A) and (B) cases. In this case students follow Emily Figel as she builds an even more sophisticated model using the gradient boosted tree method to predict, with some probability, whether a borrower would repay or default...  View Details
Keywords: Data Analytics; Data Science; Investment; Financing and Loans; Data and Data Sets; Analysis; Forecasting and Prediction
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