Emerging technologies can help organizations identify improved trade-offs between profitability objectives and sustainability imperatives. However, their full potential can be challenging to realize. One reason is that contemporary firms are embedded in a complex network of interlinked supply chains. Driven by data and insights from practice, my research aims to answer the following questions: How can novel business models shift the frontier between profitability and sustainability? Can innovative technologies (such as AI, IoT, blockchain) support this shift, and how should firms manage adoption barriers? What role, precisely, does the complex supply chain network play in which the firm is embedded, and how can this network be represented effectively?

Publications

Here you can find links to my publications in Operations journals.

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.
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).
Published in Operations Research, 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.

Selected Works in Progress

Here you can find a description of some of my ongoing research projects.

Supply chain problems are frequently formulated as optimization problems over graphs representing complex networks of interlinked input-output relationships. Frequently, these problems are hard, so researchers rely on analyzing stylized structures or developing approximate solutions. Yet, a scarcity of real-world data has hindered our understanding of how exact and approximate solutions perform in practice and whether managerial insights carry over from these simplified settings. We address this critical gap by introducing RG4SC, a versatile random graph model for creating "test tracks" for supply chain management research. RG4SC's simple micro-foundations and interpretable input parameters allow for systematically generating diverse and realistic network structures. We demonstrate its empirical validity and that it more adequately represents real-world supply chain networks than existing random models. We then showcase RG4SC's utility for research through a case study on the Guaranteed Service Model (GSM), a widely used framework for safety stock optimization. Our analysis shows how RG4SC can be central to uncovering novel managerial insights, analyzing the computational complexity of algorithms, benchmarking heuristics, and training machine learning models. RG4SC is accessible through a user-friendly web interface at https://scngenerator.pythonanywhere.com/.

With Andre Calmon (Georgia Tech) & Georgina Hall (INSEAD).
Under review. A draft is available here.
Invited for presentation at the 2024 MSOM Supply Chain Management SIG.
Everyday business 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, which we differentiate based on their information content and precision. 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).
Writing in progress.
Firms and policy makers face many resource allocation decisions. Often, these decisions cannot be made purely based 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).
Analysis in progress.
We provide a novel analysis of the Guaranteed Service Model (GSM), one of the most widely applied models for multi-echelon inventory management. In particular, we develop a procedure to solve the GSM in time exponential in the treewidth of the underlying supply chain network graph, but linear in the number of nodes n of the graph. The treewidth of a graph describes the graph's similarity to a tree — it is one for a tree graph, and n-1 for a fully connected graph. Our procedure is based on solving a linear program, so it can easily be implemented with standard solvers. This allows solving the GSM for large and highly complex supply chain networks, as long as the underlying network's treewidth is relatively small — which, recent literature has identified, is the case for supply chain networks encountered in practice.
We then extend the GSM and our procedure to closed-loop supply chain networks, that is, networks with reverse flows. We show that individual reverse flows do not significantly affect the solution time. This opens the door to analyzing inventory management in the circular economy and evaluating the viability of different circular business models.

With Andre Calmon (Georgia Tech), Georgina Hall (INSEAD), & Mohit Tawarmalani (Daniels School of Business).
Analysis in progress. A draft with the core analytical results only is available here.

Contact

Email: philippe.blaettchen α city.ac.uk

Phone: +44 (0) 7456 512419

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