Document Type

Article

Publication Date

2-25-2026

Publication Title

International Journal of Production Research

First Page

1

Last Page

33

DOI

https://doi.org/10.1080/00207543.2026.2636664

Abstract

The integration of Generative Artificial Intelligence (GenAI) into Supply Chain Management (SCM) has accelerated rapidly. However, limited understanding exists on how GenAI-enabled capabilities should be prioritised to create sustained value. Existing research predominantly describes applications but overlooks the hierarchical structure of underlying capabilities required for effective adoption. In response, this study develops a capability-oriented framework grounded in the Task Technology Fit (TTF) perspective. A Systematic Literature Review identified capabilities, which were refined via Fuzzy Delphi and structured using Interpretive Structural Modeling (ISM) with Fuzzy MICMAC. The resulting framework was corroborated through secondary case analyses of DHL Supply Chain and Walmart, generating empirically derived propositions regarding adoption mechanisms. Findings reveal a four-stage progression from data consolidation to operational intelligence, adaptability, and differentiation. Real-time data integration serves as the enabling factor, supporting intermediate automation capabilities, while adaptability and differentiation emerge as dependent outcomes that represent the strategic value frontier. The study extends TTF by offering a capability mediated perspective that provides a prescriptive roadmap for prioritising capability building and guiding the design of GenAI-based systems in supply chains.

Rights

This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Production Research on February 25, 2026, available online: http://wwww.tandfonline.com/[Article DOI] This article is distributed under a Creative Commons Attribution License: CC BY 4.0

Notes

Publisher Policy: This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in are pository by the author(s) or with their consent.

Download

Share

COinS