Technology & Operations Management

- September 2024
- Case
Anker Innovations (A)

By: Feng Zhu, Jiangyong Lu and Nancy Hua Dai

An 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 Dai

An 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,...
- 2024
- Working Paper
Empirical Guidance: Data Processing and Analysis with Applications in Stata, R, and Python

By: Melissa Ouellet and Michael W. Toffel

This 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. Toffel

This 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
- Article
Learning Under Random Distributional Shifts

By: Kirk Bansak, Elisabeth Paulson and Dominik Rothenhäusler

Algorithmic 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 Rothenhäusler

Algorithmic 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

By: Iavor I Bojinov

September 2024 |
Supplement |
Faculty Research

Citation

Purchase

How to Pay Family Employees in a Family Business

By: Christina R. Wing, Maryann G Bell and Kara A Perusse

September 2024 |
Technical Note |
Faculty Research

Family businesses play a pivotal role in global economies, contributing significantly to employment and wealth creation. However, managing compensation for family members within these enterprises can be complex. Family employees frequently intertwine their roles as both employees and owners, thereby complicating the formulation of effective compensation strategies. This note aims to provide a comprehensive understanding of the factors involved in compensation, emphasizing fairness, transparency, and alignment with business objectives.

Citation

Educators

Anker Innovations (B)

By: Feng Zhu, Jiangyong Lu and Nancy Hua Dai

September 2024 |
Supplement |
Faculty Research

An 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.

Keywords: Decision Choices and Conditions; Product; Distribution; Product Launch; Electronics Industry; Consumer Products Industry

Citation

Anker Innovations (A)

By: Feng Zhu, Jiangyong Lu and Nancy Hua Dai

September 2024 |
Case |
Faculty Research

Keywords: Decision Choices and Conditions; Product; Distribution; Product Launch; Electronics Industry; Consumer Products Industry

Citation

Educators

Note on CEO Succession in Family Enterprises

By: Christina R. Wing, Kara A. Perusse and Hillary B. Sieber

September 2024 |
Technical Note |
Faculty Research

This note aims to offer guidance and insights into navigating CEO succession, whether involving family or non-family executives, and emphasizes the importance of providing clarity on the criteria for selection. By understanding the nuances and best practices of CEO succession, family enterprises can ensure a seamless transition and long-term sustainability.

Citation

Educators

Empirical Guidance: Data Processing and Analysis with Applications in Stata, R, and Python

By: Melissa Ouellet and Michael W. Toffel

2024 |
Working Paper |
Faculty Research

This 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.

Keywords: Empirical Methods; Empirical Operations; Statistical Methods And Machine Learning; Statistical Interferences; Research Analysts; Analytics and Data Science; Mathematical Methods

Citation

Read Now

Building an AI First Snack Company: A Hands-on Generative AI Exercise

By: Iavor I. Bojinov

September 2024 |
Exercise |
Faculty Research

Although the term 'Generative AI' (GenAI) is widely recognized, its practical application in daily workflows has yet to be understood. This exercise introduces students to GenAI tools, demonstrating how they can be seamlessly integrated into professional work practices to co-invent, analyze data, generate images, summarize text, etc. The exercise guides students through developing a fictional snack company, showcasing the versatility of GenAI in tasks such as market analysis, brand development, and the formulation of marketing strategies. The exercise culminates with students creating a comprehensive design document and presentation that can be used to pitch to investors. Key takeaways include understanding how GenAI works, incorporating AI into developing and launching a new product, and offering valuable lessons on AI's potential and limitations in the modern workplace.

Citation

Purchase

Learning Under Random Distributional Shifts

By: Kirk Bansak, Elisabeth Paulson and Dominik Rothenhäusler

2024 |
Article |
Proceedings of the 27th International Conference on Artificial Intelligence and Statistics

Algorithmic 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.

Citation

Read Now

More Publications