As the emphasis on sustainability by governments and the general public intensifies, organizations are increasingly under pressure to recalibrate their value proposition. Driven by data and insights from practice, my research focuses on enabling organizations to restructure their operations and business models. In particular, I analyze the role played by emerging technologies in identifying improved trade-offs between profitability objectives and socio-environmental sustainability imperatives.


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).
Invited for minor revision at Operations Research.

Selected Works in Progress

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

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.

Joint 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.
A large body of research in supply chain management revolves around optimization models and procedures over supply chain networks (SCNs). However, data on SCNs found in practice is scarce, making it challenging to test algorithms or gain new managerial insights. We fill this gap by proposing and making available online a random model for generating SCNs based on simple micro-foundations. We show empirically that our model is able to generate realistic SCNs while providing researchers with sufficient control to create networks with desired structural properties.
Our proposed random model (i) provides a toolkit to study how the SCN structure affects the complexity of solution approaches and the quality of heuristics, (ii) facilitates the discovery of new managerial insights, and (iii) allows creating a standard benchmark to compare different solution approaches. We illustrate these points by revisiting the Guaranteed Service Model, a well-established model for safety stock placement in SCNs. In particular, building on our toolkit, we obtain new complexity results, novel managerial insights, and standardized comparisons between competing solution algorithms.

Joint work with Andre Calmon (Georgia Tech) & Georgina Hall (INSEAD).
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.
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. 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. Based on these insights, we develop a recommendation system that takes into consideration possible deviations by drivers, with the objective of improving their long-term performance.

Joint work 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.

Joint work with Peter Zhang (Carnegie Mellon University).