The growing importance of sustainability perceived by governments and the public puts pressure on organizations to adapt their value-creating processes. I study how organizations, using emerging technologies and new business models, can optimally redesign their operations to align efficiency and profitability objectives with social and environmental sustainability requirements. Guided by data and insights from practice, I examine organizations within their broader supply chain context, which moderates the potential of new technologies and business models.


Here you can find abstracts of my papers.

Modern traceability technologies promise to improve supply chain management by simplifying recall procedures, increasing demand visibility, or 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 that supply chains are interlinked in complex networks and that a supply chain effect is inherent to traceability technologies. More specifically, the benefits obtained from traceability are conditional on technology adoption throughout a product's supply chain. As a result, it is difficult to identify the smallest set of early adopters guaranteeing broad dissemination of the technology.
We introduce a model of the dynamics of traceability technology adoption in supply chain networks to tackle this problem. Our model builds on extant diffusion models while incorporating the fact that a firm's adoption decisions depend on previous adoption decisions throughout its supply chains. We show that the problem of selecting the smallest set of early adopters is NP-hard and that no approximation within a polylogarithmic factor can be guaranteed for any polynomial-time algorithm. Nevertheless, we introduce an algorithm that identifies an exact solution in polynomial time under certain assumptions on the network structure. We provide evidence that our algorithm is tractable for real-world supply chain networks. We then propose a random generative model that outputs networks consistent with real-world supply chain networks. We show that the networks obtained display, with high probability, structures that allow finding the optimal seed set in subexponential time using our algorithm.

With Andre Calmon (Georgia Tech) and Georgina Hall (INSEAD), available on SSRN.
Under review at Management Science.
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 (Harvard University), Harwin de Vries (Erasmus University), & Luk Van Wassenhove (INSEAD).
Published online ahead of print 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 (Cambridge University) & Sameer Hasija (INSEAD), available on SSRN (currently undergoing revision).
Invited for major 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).
A mix of global sourcing and sophisticated storage and transportation technologies allow grocery stores to stock fresh fruits and vegetables even in the middle of winter. However, environmental concerns, a desire to support local communities, and increasing supply chain risks mean local sourcing is becoming more important. When relying on local sourcing, grocery stores are more affected by seasonality in their supply chain planning. At the same time, new technologies like vertical farming allow managing seasonality locally but require the coordination of more small-scale supply sources.
Current approaches to supply chain planning are not amenable to considering seasonality at scale. Thus, drawing on work in semidefinite optimization, we propose a new framework for coordinating supply and demand in agri-food supply chains. Our framework provides planners with the flexibility to address continuous changes in yields and prices and incorporate vastly different supply sources. We exemplify our framework using an extensive data set for important seasonal crops and evaluate key policy decisions, such as environmental regulation of transport or buy-local requirements.

Joint work with Andre Calmon (Georgia Tech) & Georgina Hall (INSEAD).
Many resource allocation decisions are not based purely on utilitarian considerations but capture some notion of fairness. Technological developments in areas such as personalized medicine or machine learning have made such questions of fairness more pressing. At the same time, allocation decisions tend to have inherently uncertain impacts in these areas. Operations management has provided a deep understanding of the trade-off between fairness and efficiency but has often ignored the inherent uncertainty. We demonstrate that ignoring uncertainty in planning can systematically bias decisions away from already disadvantaged agents. Moreover, we show how fairness problems from different domains can be analyzed systematically from a unifying optimization perspective.

Joint work with Peter Zhang (Carnegie Mellon University).