Traceability Technology Adoption in Supply Chain Networks

Abstract

Modern traceability technologies promise to improve supply chain management by simplifying recalls, increasing visibility, or verifying sustainable supplier practices. Initiatives developing traceability technologies — and who hope to make their technologies the industry standard — must choose the least-costly set of firms to target as early adopters. This choice 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 (potentially large) subset of firms in a product’s supply chain.

We prove that the problem of selecting the least-costly set of early adopters 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 set of early adopters. The algorithm is fixed-parameter tractable in the supply chain network’s treewidth, a parameter which we show to be low in real-world supply chain networks. The algorithm also enables us to derive easily-computable bounds on the optimal cost of selecting early adopters as well as key managerial insights about which type of firm to select.

Keywords: supply chain traceability, technology adoption, network diffusion, computational complexity, fixed-parameter tractability, treewidth