Emerging technologies can help organizations identify improved trade-offs between profitability objectives and sustainability imperatives. However, the full potential of emerging technologies can be challenging to realize and network effects can hinder their adoption by organizations and consumers. Driven by data and insights from practice, my research focuses on enabling organizations to improve value chain efficiency and sustainability with the help of innovative technologies.


Here you can find abstracts of my papers.

Modern traceability technologies promise to improve supply chain management by simplifying recalls, increasing visibility, or verifying sustainable supplier practices. Initiatives leading the implementation of traceability technologies must choose the least-costly set of firms—or seed set—to target for early adoption. Choosing this seed set is challenging because firms are part of supply chains interlinked in complex networks, yielding an inherent supply chain effect: benefits obtained from traceability are conditional on technology adoption by a subset of firms in a product's supply chain. We prove that the problem of selecting the least-costly seed set in a supply chain network is hard to solve and even approximate within a polylogarithmic factor. Nevertheless, we provide a novel linear programming-based algorithm to identify the least-costly seed set. The algorithm is fixed-parameter tractable in the supply chain network's treewidth, which we show to be low in real-world supply chain networks. The algorithm also enables us to derive easily-computable bounds on the cost of selecting an optimal seed set. Finally, we leverage our algorithms to conduct large-scale numerical experiments that provide insights into how the supply chain network structure influences diffusion. These insights can help managers optimize their technology diffusion strategy.

With Andre Calmon (Georgia Tech) & Georgina Hall (INSEAD).
Published in Management Science, Articles in Advance.
Family planning services play a central role in the sustainable development agenda by improving health and education while reducing poverty and gender inequality. Marie Stopes International (MSI) reaches underserved clients in rural areas with the help of mobile healthcare units (MHUs). This approach is gaining traction more generally to enable broad access to health services for marginalized rural populations, thus stepping closer to fulfilling SDG 3: Good Health and Well-Being. Our research addresses the complex challenge of allocating limited mobile healthcare unit resources to create the largest possible impact in the long-term, especially in light of intricate demand dynamics.
We model the MHU resource allocation problem as the optimization of a sum of sigmoidal functions and derive new theoretical results with operationalizable managerial insights. We further develop a machine learning approach to predict demand in such a setting that provides interpretable results, a key requirement in the humanitarian context. Our empirical application combines MSI's client demand data with rich, publicly available data sources. We use the resulting estimates to demonstrate the benefits of our analytically derived insights.

With Andres Alban (Frankfurt School of Finance & Management), Harwin de Vries (Erasmus University), & Luk Van Wassenhove (INSEAD).
Published in Manufacturing & Service Operations Management, 2022, Vol. 24, Issue 6.
Finalist, 2022 MSOM Society Award for Responsible Research in Operations Management.
Technological advances enable new business models for heavy equipment manufacturers wherein customers access equipment without ownership. We seek to understand the profitability and environmental performance of different emerging business models in light of salient economic and operational factors. We develop a game-theoretic model to identify the optimal choice between a traditional ownership-based business model and two access-based models: servicization and peer-to-peer sharing. After-sales services, equipment characteristics, usage environments, and fuel prices affect this choice. We also provide a novel framework to analyze business models' environmental impact, which incorporates trade-offs between economic value and environmental costs and shows that all models may create win-win situations for the manufacturer and the environment.

With Niyazi Taneri (Cambridge University) & Sameer Hasija (INSEAD).
Accepted for publication at Operations Research.
Many optimization problems in supply chain management are formulated over graphs representing networks of interlinked supply chains. Solution approaches to these problems are often studied and tested on small, stylized supply chain networks, or on some of the few publicly available supply chain network dataset. This paucity of data means that fundamental questions about the relationship between the network structure and the properties of the problem solution such as quality and complexity are not addressed systematically.
Our paper fills this gap by introducing a random generative model of supply chain networks, in line with the computer science and social sciences literature, where random graph models are a popular tool for studying properties of "typical" networks. We show that our model, based on simple micro-foundations, generates network structures similar to those observed in practice. We propose that it supports (i) analyzing how network structure affects computational complexity, (ii) identifying new managerial insights, and (iii) benchmarking heuristics. We illustrate these benefits with a case study on safety stock optimization.

With Andre Calmon (Georgia Tech) & Georgina Hall (INSEAD).
Invited for presentation at the 2024 MSOM Supply Chain Management SIG.
A draft is available upon request.

Selected Works in Progress

Here you can find a description of some of my other research project.

Many everyday tasks require sequential, interlinked decisions that make it difficult for workers to identify the best strategy. As a result, Artificial Intelligence (AI)-based recommendation systems are applied increasingly to reduce complexity. However, due to human biases, there remains a gap between what is technically possible and what is utilized. This work seeks to understand how recommendation systems can help induce efficient behavior in sequential decision-making and contribute to long-term learning.
We develop a sequential decision-making task in the form of a virtual electric vehicle driving game. Participants, equipped with algorithm-generated recommendations, need to make sequential charging decisions while facing uncertain traffic. With the help of inverse reinforcement learning, we identiy participants' underlying decision-making strategies and how these are affected by recommendations. Our experimental results offer key insights into how humans make sequential decisions and respond to different types of recommendations, both immediately and in terms of long-term learning.

With Park Sinchaisri (UC, Berkeley).
Many resource allocation decisions are not based purely on utilitarian considerations but capture some notion of fairness. Operations Management has provided a deep understanding of the trade-off between fairness and efficiency but has often ignored the uncertainty inherent in evaluating resource allocations. One fundamental reason for this uncertainty stems from imperfect information gathering. For example, in a push to achieve net-zero emissions, governments increasingly impose restrictions, such as congestion charges, without accurately understanding the effects of such restrictions on different sub-populations.
Building on a distributionally robust optimization framework, we study how the "price of fairness" is affected by uncertainty in the impact of different policies. We demonstrate that ignoring uncertainty can systematically bias decisions away from disadvantaged sub-populations. We then analyze how information-gathering activities can mitigate this bias and how these activities should optimally be targeted.

With Peter Zhang (Carnegie Mellon University).
One of the key drivers of modern supply chain traceability solutions lies in the underlying information systems. However, it is unclear how centralized cloud-based systems and decentralized blockchain systems—both modern solutions that are believed to drive a wave of improved traceability adoption—compare to traditional ERP-powered traceability and each other.
We build a game-theoretic framework to show that differences between information systems profoundly impact both the adoption and effectiveness of traceability solutions. In particular, modern traceability systems allow for a different mode of connection (1:n instead of 1:1), and tend to be cheaper and quicker to adopt. They differ, moreover, in their approach to data interchange and ownership. We show that all these differences can have non-intuitive impacts on whether traceability is broadly adopted because they also affect long-term supplier relationships and commitments. For example, the degree of competition in a supply chain network largely determines the advantage of modern over traditional traceability systems. This is because modern systems' efficiency advantages coincide with issues in sustaining long-term supplier relationships. We also show that retention of data ownership, one of the key arguments for blockchain technology, may further compromise supply chain members' ability to commit to data sharing. This points to a potential pitfall in the usage of blockchain technology for traceability.

Work with Andre Calmon (Georgia Tech) & Sameer Hasija (INSEAD).

I discussed some of my research results in a tech talk hosted by digital@INSEAD. Together with my Ph.D. advisor Sameer Hasija, my colleague Dmitry Sumkin, and Aly Madhavji from the Blockchain Founders Fund, we analyzed different perspectives and distinguish the hype from reality.