Philippe Blaettchen’s research while affiliated with City, University of London and other places

Finding the Right Business Model for Durable Goods Technological advances enable new ways for customers to access durable goods such as heavy equipment without the need for ownership. In “Business Model Choice for Heavy Equipment Manufacturers,” P. Blaettchen, N. Taneri, and S. Hasija analyze the resulting business models available to manufacturers, considering that manufacturers need to effectively coordinate their sales strategy with their highly profitable after-sales service activities and take into account secondary markets. Employing a game-theoretic framework, the authors show how different equipment characteristics lead to different optimal business model designs. They provide a comprehensive overview of when manufacturers should rely on emerging models based on servicization or peer-to-peer sharing and when it pays to retain a traditional model based on sales. Drawing on a novel framework for analyzing a business model’s environmental impact, they further show that emerging models can create win-win situations for the manufacturer and the environment.

Modern traceability technologies promise to improve supply chain management by simplifying recalls, increasing visibility, and 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. We leverage our toolbox 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. This paper was accepted by Chung Piaw Teo, optimization. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.01759 .

Problem definition: Achieving broad access to health services (a target within the sustainable development goals) requires reaching rural populations. Mobile healthcare units (MHUs) visit remote sites to offer health services to these populations. However, limited exposure, health literacy, and trust can lead to sigmoidal (S-shaped) adoption dynamics, presenting a difficult obstacle in allocating limited MHU resources. It is tempting to allocate resources in line with current demand, as seen in practice. However, to maximize access in the long term, this may be far from optimal, and insights into allocation decisions are limited. Academic/practical relevance: We present a formal model of the long-term allocation of MHU resources as the optimization of a sum of sigmoidal functions. We develop insights into optimal allocation decisions and propose pragmatic methods for estimating our model’s parameters from data available in practice. We demonstrate the potential of our approach by applying our methods to family planning MHUs in Uganda. Methodology: Nonlinear optimization of sigmoidal functions and machine learning, especially gradient boosting, are used. Results: Although the problem is NP-hard, we provide closed form solutions to particular cases of the model that elucidate insights into the optimal allocation. Operationalizable heuristic allocations, grounded in these insights, outperform allocations based on current demand. Our estimation approach, designed for interpretability, achieves better predictions than standard methods in the application. Managerial implications: Incorporating the future evolution of demand, driven by community interaction and saturation effects, is key to maximizing access with limited resources. Instead of proportionally assigning more visits to sites with high current demand, a group of sites should be prioritized. Optimal allocation among prioritized sites aims at equalizing demand at the end of the planning horizon. Therefore, more visits should generally be allocated to sites where the cumulative demand potential is higher and counterintuitively, often those where demand is currently lower. History: This paper has been accepted for the Manufacturing & Service Operations Management Special Section on Responsible Research in Operations Management. Supplemental Material: The e-companion is available at https://doi.org/10.1287/msom.2021.1020 .

Modern traceability technologies promise to improve supply chain management by simplifying recalls, increasing demand visibility, or ascertaining sustainable supplier practices. Managers in the 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. We introduce a model of the dynamics of traceability technology adoption in supply chain networks to tackle the problem of selecting the smallest set of early adopters guaranteeing broad dissemination. Our model builds on extant diffusion models while incorporating that a firm's adoption decision depends on previous adoption decisions throughout its supply chains. We show that the problem 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 and provide evidence that it is tractable for real-world supply chain networks. We further propose a random generative model that outputs networks consistent with real-world supply chain networks. The networks obtained display, whp, structures that allow us to find the optimal seed set in subexponential time using our algorithm. Our generative model also provides approximate seed sets when information on the network is limited.