Technology & Operations Management
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- September 2024
- Case
Anker Innovations (A)
By: Feng Zhu, Jiangyong Lu and Nancy Hua DaiAn Amazon-native brand, Anker is the world’s No. 1 mobile charging brand and a leading consumer electronics company. Over the years, Anker developed an effective model of proving new products online first by leveraging customer insights from its proprietary Voice of Customer system before selling these products offline. In May 2021, Steven Yang, founder & CEO of Anker, and Dongping Zhao, President of Anker, need to decide whether to say yes or no to a request from Costco, the largest warehouse club in the US with over 500 stores, to supply three new smart home products.
- September 2024
- Case
Anker Innovations (A)
By: Feng Zhu, Jiangyong Lu and Nancy Hua DaiAn Amazon-native brand, Anker is the world’s No. 1 mobile charging brand and a leading consumer electronics company. Over the years, Anker developed an effective model of proving new products online first by leveraging customer insights from its proprietary Voice of Customer system before selling these products offline. In May 2021, Steven Yang,...
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- 2024
- Working Paper
Empirical Guidance: Data Processing and Analysis with Applications in Stata, R, and Python
By: Melissa Ouellet and Michael W. ToffelThis paper describes a range of best practices to compile and analyze datasets, and includes some examples in Stata, R, and Python. It is meant to serve as a reference for those getting started in econometrics, and especially those seeking to conduct data analyses in Stata, R, or Python.
- 2024
- Working Paper
Empirical Guidance: Data Processing and Analysis with Applications in Stata, R, and Python
By: Melissa Ouellet and Michael W. ToffelThis paper describes a range of best practices to compile and analyze datasets, and includes some examples in Stata, R, and Python. It is meant to serve as a reference for those getting started in econometrics, and especially those seeking to conduct data analyses in Stata, R, or Python.
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- 2024
- Article
Learning Under Random Distributional Shifts
By: Kirk Bansak, Elisabeth Paulson and Dominik RothenhäuslerAlgorithmic assignment of refugees and asylum seekers to locations within host countries has gained attention in recent years, with implementations in the U.S. and Switzerland. These approaches use data on past arrivals to generate machine learning models that can be used (along with assignment algorithms) to match families to locations, with the goal of maximizing a policy-relevant integration outcome such as employment status after a certain duration. Existing implementations and research train models to predict the policy outcome directly, and use these predictions in the assignment procedure. However, the merits of this approach, particularly in non-stationary settings, has not been previously explored. This study proposes and compares three different modeling strategies: the standard approach described above, an approach that uses newer data and proxy outcomes, and a hybrid approach. We show that the hybrid approach is robust to both distribution shift and weak proxy relationships—the failure points of the other two methods, respectively. We compare these approaches empirically using data on asylum seekers in the Netherlands. Surprisingly, we find that both the proxy and hybrid approaches out-perform the standard approach in practice. These insights support the development of a real-world recommendation tool currently used by NGOs and government agencies.
- 2024
- Article
Learning Under Random Distributional Shifts
By: Kirk Bansak, Elisabeth Paulson and Dominik RothenhäuslerAlgorithmic assignment of refugees and asylum seekers to locations within host countries has gained attention in recent years, with implementations in the U.S. and Switzerland. These approaches use data on past arrivals to generate machine learning models that can be used (along with assignment algorithms) to match families to locations, with the...
About the Unit
As the world of operations has changed, so have interests and priorities within the Unit. Historically, the TOM Unit focused on manufacturing and the development of physical products. Over the past several years, we have expanded our research, course development, and course offerings to encompass new issues in information technology, supply chains, and service industries.
The field of TOM is concerned with the design, management, and improvement of operating systems and processes. As we seek to understand the challenges confronting firms competing in today's demanding environment, the focus of our work has broadened to include the multiple activities comprising a firm's "operating core":
- the multi-function, multi-firm system that includes basic research, design, engineering, product and process development and production of goods and services within individual operating units;
- the networks of information and material flows that tie operating units together and the systems that support these networks;
- the distribution and delivery of goods and services to customers.
Recent Publications
Creating an AI-First Snack Company Exercise Data Supplement
- September 2024 |
- Supplement |
- Faculty Research
How to Pay Family Employees in a Family Business
- September 2024 |
- Technical Note |
- Faculty Research
Anker Innovations (B)
- September 2024 |
- Supplement |
- Faculty Research
Anker Innovations (A)
- September 2024 |
- Case |
- Faculty Research
Note on CEO Succession in Family Enterprises
- September 2024 |
- Technical Note |
- Faculty Research
Empirical Guidance: Data Processing and Analysis with Applications in Stata, R, and Python
- 2024 |
- Working Paper |
- Faculty Research
Building an AI First Snack Company: A Hands-on Generative AI Exercise
- September 2024 |
- Exercise |
- Faculty Research
Learning Under Random Distributional Shifts
- 2024 |
- Article |
- Proceedings of the 27th International Conference on Artificial Intelligence and Statistics
Harvard Business Publishing
Seminars & Conferences
There are no upcoming events.