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Show Results For
-
All HBS Web
(1,313)
- People (1)
- News (260)
- Research (801)
- Events (13)
- Multimedia (7)
- Faculty Publications (639)
- March 1983 (Revised February 1986)
- Teaching Note
Norman Machinery Products, Inc. (A), Teaching Note
By: Henry B. Reiling
- July 2012
- Case
Owen's Precision Machining
By: Ramana Nanda and James McQuade
For the second time in fourteen months, Christopher Owen, the second-generation owner of Owen's Precision Machining (OPM), found himself running out of cash. Owen wondered what he was doing wrong. How much additional money would he need to raise to get OPM through the...
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Keywords:
Family Business;
Cash Flow;
Mergers and Acquisitions;
Decision Making;
Problems and Challenges;
Business Strategy;
Corporate Finance;
Manufacturing Industry;
Massachusetts
Nanda, Ramana, and James McQuade. "Owen's Precision Machining." Harvard Business School Case 813-036, July 2012.
- Research Summary
Technology and Knowledge Transfer in the Evolution of China’s Machine Industry
This work-in-progress provides a historical perspective on the role of foreign companies as providers of machinery equipment and facilitators of technology transfer in China from the last decade of the 19th century to the early 1950s. The project focuses on...
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- Research Summary
Making Machine Learning Models Fair
The goal of this research direction is to ensure that the machine learning models we build and deploy do not discriminate against individuals from minority groups.
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- February 1987 (Revised July 1988)
- Background Note
Machine Tool Industry and Industrial Policy--1955-82
By: George C. Lodge and David J. Collis
Lodge, George C., and David J. Collis. "Machine Tool Industry and Industrial Policy--1955-82." Harvard Business School Background Note 387-145, February 1987. (Revised July 1988.)
- Research Summary
Making Machine Learning Models Interpretable
I work on developing various tools and methodologies which can help decision makers (e.g., doctors, managers) to better understand the predictions of machine learning models.
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- October 2017 (Revised April 2018)
- Case
Improving Worker Safety in the Era of Machine Learning (A)
By: 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...
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Keywords:
Machine Learning;
Policy Implementation;
Empirical Research;
Inspection;
Occupational Safety;
Occupational Health;
Regulation;
Analysis;
Forecasting and Prediction;
Policy;
Operations;
Supply Chain Management;
Safety;
Manufacturing Industry;
Construction Industry;
United States
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.)
- 14 Mar 2023
- Cold Call Podcast
Can AI and Machine Learning Help Park Rangers Prevent Poaching?
- December 2010 (Revised March 2015)
- Case
The Wright Brothers and Their Flying Machines
By: Tom Nicholas and David Chen
Wilbur (1867-1912) and Orville (1871-1948) Wright were fascinated by the mystery of flight and they built on the ideas of prominent earlier figures such as Octave Chanute (1832-1910) the French-born American who was influential in fostering the free exchange of ideas...
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Keywords:
Entrepreneurship;
Business History;
Technological Innovation;
Patents;
Knowledge Sharing;
Air Transportation;
Air Transportation Industry;
Europe;
United States
Nicholas, Tom, and David Chen. "The Wright Brothers and Their Flying Machines." Harvard Business School Case 811-034, December 2010. (Revised March 2015.)
- 1999
- Working Paper
Machines and Mindlessness: Social Responses to Computers
By: Clifford Nass and Youngme Moon
Making Workplaces Safer Through Machine Learning
Government agencies can use machine learning to improve the effectiveness of regulatory inspections. Our study found that OSHA could prevent as much as twice as many injuries—translating to up to 16,000 fewer workers injured and nearly $800 million in social... View Details
- 2020
- Working Paper
Is Accounting Useful for Forecasting GDP Growth? A Machine Learning Perspective
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...
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Keywords:
Big Data;
Elastic Net;
GDP Growth;
Machine Learning;
Macro Forecasting;
Short Fat Data;
Accounting;
Economic Growth;
Forecasting and Prediction;
Analytics and Data Science
Datar, Srikant, Apurv Jain, Charles C.Y. Wang, and Siyu Zhang. "Is Accounting Useful for Forecasting GDP Growth? A Machine Learning Perspective." Harvard Business School Working Paper, No. 21-113, December 2020.
- March 1988
- Background Note
Machine Tool Industry and Industrial Policy--1955-82 (Condensed)
By: Norman A. Berg
Berg, Norman A. "Machine Tool Industry and Industrial Policy--1955-82 (Condensed)." Harvard Business School Background Note 388-117, March 1988.
- July 1988
- Supplement
LTV Aerospace and Defense: Flexible Machining Cell, Video
By: David A. Garvin
Garvin, David A. "LTV Aerospace and Defense: Flexible Machining Cell, Video." Harvard Business School Video Supplement 889-501, July 1988.
Work Mate Marry Love: How Machines Shape Our Human Destiny
What will happen to our notions of marriage and parenthood as reproductive technologies increasingly allow for newfangled ways of creating babies? What will happen to our understanding of gender as medical advances enable individuals to transition from one set of...
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- 02 Aug 2017
- Working Paper Summaries
Machine Learning Methods for Strategy Research
Keywords:
by Mike Horia Teodorescu
- June 1983
- Teaching Note
Note on the Paper Machinery Industry, Teaching Note
Teaching Note for (9-383-185).
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Keywords:
Pulp and Paper Industry
- October 2021
- Article
Overcoming the Cold Start Problem of CRM Using a Probabilistic Machine Learning Approach
By: Nicolas Padilla and 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...
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Keywords:
Customer Management;
Targeting;
Deep Exponential Families;
Probabilistic Machine Learning;
Cold Start Problem;
Customer Relationship Management;
Programs;
Consumer Behavior;
Analysis
Padilla, Nicolas, and Eva Ascarza. "Overcoming the Cold Start Problem of CRM Using a Probabilistic Machine Learning Approach." Journal of Marketing Research (JMR) 58, no. 5 (October 2021): 981–1006.