Artificial intelligence (AI) for supply chain collaboration: implications on information sharing and trust

Abstract

Purpose

Managers and scholars alike claim that artificial intelligence (AI) represents a tool to enhance supply chain collaborations; however, existing research is limited in providing frameworks that categorise to what extent companies can apply AI capabilities and support existing collaborations. In response, this paper clarifies the various implications of AI applications on supply chain collaborations, focusing on the core elements of information sharing and trust. A five-stage AI collaboration framework for supply chains is presented, supporting managers to classify the supply chain collaboration stage in a company’s AI journey.

Design/methodology/approach

Using existing literature on AI technology and collaboration and its effects of information sharing and trust, we present two frameworks to clarify (a) the interrelationships between information sharing, trust and AI capabilities and (b) develop a model illustrating five AI application stages how AI can be used for supply chain collaborations.

Findings

We identify various levels of interdependency between trust and AI capabilities and subsequently divide AI collaboration into five stages, namely complementary AI applications, augmentative AI applications, collaborative AI applications, autonomous AI applications and AI applications replacing existing systems.

Originality/value

Similar to the five stages of autonomous driving, the categorisation of AI collaboration along the supply chain into five consecutive stages provides insight into collaborations practices and represents a practical management tool to better understand the utilisation of AI capabilities in a supply chain environment.

Citation

Weisz, E., Herold, D.M., Ostern, N.K., Payne, R. and Kummer, S. (2024), "Artificial intelligence (AI) for supply chain collaboration: implications on information sharing and trust", Online Information Review, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/OIR-02-2024-0083