Traceability Technology Adoption in Supply Chain Networks

43 Pages Posted: 17 Mar 2021 Last revised: 15 Jun 2022

See all articles by Philippe Blaettchen

Philippe Blaettchen

Bayes Business School (formerly Cass)

Andre Calmon

Georgia Institute of Technology - Operations Management Area; INSEAD - Technology and Operations Management

Georgina Hall

INSEAD - Decision Sciences

Date Written: June 14, 2022

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

Suggested Citation

Blaettchen, Philippe and Calmon, Andre and Hall, Georgina, Traceability Technology Adoption in Supply Chain Networks (June 14, 2022). Available at SSRN: https://ssrn.com/abstract=3805040 or http://dx.doi.org/10.2139/ssrn.3805040

Philippe Blaettchen (Contact Author)

Bayes Business School (formerly Cass) ( email )

United Kingdom

Andre Calmon

Georgia Institute of Technology - Operations Management Area ( email )

800 West Peachtree St.
Atlanta, GA 30308
United States

INSEAD - Technology and Operations Management ( email )

Boulevard de Constance
77 305 Fontainebleau Cedex
France

Georgina Hall

INSEAD - Decision Sciences ( email )

United States

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