Traceability Technology Adoption in Supply Chain Networks

TL;DR

This paper addresses the challenge of selecting the most cost-effective firms to adopt traceability technology in supply chains, proposing a novel algorithm that leverages supply chain network structure to optimize diffusion strategies.

Contribution

It introduces a linear programming-based algorithm for seed set selection in supply chain networks, with fixed-parameter tractability based on network treewidth, and provides practical insights for managers.

Findings

The seed set selection problem is computationally hard to approximate.

The proposed algorithm is effective in real-world supply chain networks with low treewidth.

Large-scale experiments reveal how network structure impacts technology diffusion.

Abstract

Modern traceability technologies promise to improve supply chain management by simplifying recalls, increasing visibility, or 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. Finally, we leverage our algorithms 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.