Emerging technological developments can have a fundamental impact on how firms operate to create and capture value. At the same time, managers increasingly need to consider broader societal issues in their operations. In my research, I aim to understand how new business models, often enabled by technological progress, can help answer pressing societal questions while allowing organizations to remain competitive. Guided by data and insights from practice, I examine new business models and process designs at all levels, from individual organizations to entire supply chains.

Working Papers

Here you can find abstracts of my working papers.

Modern traceability technologies promise to improve supply chain management by simplifying recall procedures, increasing demand visibility, and ascertaining sustainable supplier practices. Managers in the dozens of traceability initiatives developing such technologies face a difficult question: which companies should they target as early adopters to ensure that their technology is broadly employed? To answer this question, managers must consider an extended supply chain effect that is inherent to traceability technologies. Namely, the benefits obtained from traceability are conditional on technology adoption throughout a product's supply chain. This effect, together with the fact that supply chains are interlinked in complex networks, makes the problem of choosing early adopters complex and difficult to solve.
We address the problem of selecting the smallest set of early adopters by constructing a model of the dynamics of traceability technology adoption in supply chain networks. Similar to extant diffusion models, our model specifies new adopters based on past adopters. Unlike other models, however, it incorporates extended supply chain effects. We show that the problem is NP-hard and that no useful approximation guarantees can be obtained for any polynomial-time algorithm. Nevertheless, we introduce a procedure that identifies an exact solution in polynomial time under certain parameterizations of the network structure. We provide evidence that our procedure is tractable for real-world supply chain networks. Our results further provide insights into the relationship between network structures and the optimal set of firms to target. In particular, they suggest that small, isolated firms may be favored over large, highly connected ones.

With Andre Calmon (INSEAD) & Georgina Hall (INSEAD), available upon request.
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 (INSEAD), Harwin de Vries (Erasmus University), & Luk Van Wassenhove (INSEAD), available upon request.
Invited for major revision at Manufacturing & Service Operations Management
Technological advances enable new business models for heavy equipment manufacturers, replacing or augmenting ownership-based models with access-based consumption. An ever-increasing list of peer-to-peer sharing platforms (e.g., Trringo by Mahindra, Yard Club by Caterpillar, or Coop by Ryder) shows that it is paramount for decision-makers to understand the economic implications of such business models. However, the efficacy of emerging models relying on access-based consumption is unclear. A lack of empirical evidence of their performance motivates the need for analytical insights. This paper focuses on understanding the performance of different emerging business models, particularly that of peer-to-peer product sharing, by considering salient economic and operational factors in the context of heavy equipment.
Although sharing business models are widely publicized, the optimal model for a heavy equipment manufacturer will depend on operational factors linked to after-sales services, a point which has been previously overlooked and deserves closer attention by decision-makers. We show when a heavy equipment manufacturer prefers setting up a sharing platform for its products, which provides a positive rationale for the emergence of such manufacturer-owned platforms in practice. We also describe the optimal design of a sharing platform, highlighting the need to subsidize one part of the business (sharing) to the benefit of another (after-sales services). Moreover, we provide insights into how manufacturers can leverage after-sales services when threatened by a third-party sharing platform's entrance.

With Niyazi Taneri & Sameer Hasija, available on SSRN

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 (INSEAD) & Sameer Hasija (INSEAD).
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 resulting trade-off between fairness and efficiency, for example, when allocating medical resources. Technological developments in personalized medicine, machine learning, and other areas have made questions of fairness more pressing. The same allocation decisions calling for fairness considerations have inherently uncertain impacts, which has often been ignored. To bridge this gap, we build a framework that unifies the study of allocation decisions in diverse fields. We then describe the fairness-efficiency trade-off as a function of the uncertainty characterization. For example, we show that the degree of uncertainty and the desired level of fairness are complementary in equalizing allocations.

Joint work with Peter Zhang (Carnegie Mellon University).