Traceability Technology Adoption in Supply Chain Networks

Modern traceability technologies promise to improve supply chain management by simplifying recall procedures, increasing demand visibility, or ascertaining sustainable supplier practices. Managers in the dozens of 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 \emph{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. As a result, it is difficult to identify the smallest set of early adopters guaranteeing broad dissemination of the technology. We introduce a model of the dynamics of traceability technology adoption in supply chain networks to tackle this problem. Our model builds on extant diffusion models while incorporating the fact that a firm's adoption decisions depend on previous adoption decisions throughout its supply chains. We show that the problem of selecting the smallest set of early adopters 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. We provide evidence that our algorithm is tractable for real-world supply chain networks. We then propose a random generative model that outputs networks consistent with real-world supply chain networks. We show that the networks obtained display, with high probability, structures that allow finding the optimal seed set in subexponential time using our algorithm.