Traceability Technology Adoption in Supply Chain Networks

49 Pages Posted: 17 Mar 2021 Last revised: 16 Aug 2021

See all articles by Philippe Blaettchen

Philippe Blaettchen

INSEAD

Andre Calmon

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

Georgina Hall

INSEAD - Decision Sciences

Date Written: March 15, 2021

Abstract

Modern traceability technologies promise to improve supply chain management by simplifying recalls, increasing demand visibility, or ascertaining sustainable supplier practices. Managers in the 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 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.

We introduce a model of the dynamics of traceability technology adoption in supply chain networks to tackle the problem of selecting the smallest set of early adopters guaranteeing broad dissemination. Our model builds on extant diffusion models while incorporating that a firm's adoption decision depends on previous adoption decisions throughout its supply chains. We show that the problem 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 and provide evidence that it is tractable for real-world supply chain networks. We further propose a random generative model that outputs networks consistent with real-world supply chain networks. The networks obtained display, whp, structures that allow us to find the optimal seed set in subexponential time using our algorithm. Our generative model also provides approximate seed sets when information on the network is limited.

Keywords: supply chain traceability, technology adoption, network diffusion, computational complexity

Suggested Citation

Blaettchen, Philippe and Calmon, Andre and Hall, Georgina, Traceability Technology Adoption in Supply Chain Networks (March 15, 2021). Available at SSRN: https://ssrn.com/abstract=3805040 or http://dx.doi.org/10.2139/ssrn.3805040

Philippe Blaettchen (Contact Author)

INSEAD ( email )

Boulevard de Constance
Fontainebleau, 77305
France

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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