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Faculty & Research


Traceability Technology Adoption in Supply Chain Networks

Journal Article
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./p> The authors 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, the authors 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 the authors show to be low in real-world supply chain networks. The algorithm also enables the authors to derive easily computable bounds on the cost of selecting an optimal seed set. The authors leverage their 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

Assistant Professor of Decision Sciences