Traceability Technology Adoption in Supply Chain Networks

Philippe Blaettchen (), Andre P. Calmon () and Georgina Hall ()
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Philippe Blaettchen: Bayes Business School (formerly Cass), City, University of London, London EC1Y 8TZ, United Kingdom
Andre P. Calmon: Scheller College of Business, Georgia Institute of Technology, Atlanta, Georgia 30308
Georgina Hall: Decision Sciences, INSEAD, 77305 Fontainebleau, France

Management Science, 2025, vol. 71, issue 1, 83-102

Abstract: 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.

Keywords: supply chain traceability; sustainability; technology adoption; network diffusion; computational complexity; fixed-parameter tractability; treewidth; linear programming (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:inm:ormnsc:v:71:y:2025:i:1:p:83-102

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