Supply chain cognitive resilience through AI-powered risk mitigation and autonomous resilience

Purpose This study aims to develop a comprehensive framework for supply chain cognitive resilience by integrating artificial intelligence, collaboration and coordination, autonomous resilience maturity and organizational sensemaking agility. It addresses critical gaps in existing literature, including the limited empirical focus on the interaction between AI-driven automation and cognitive resilience, especially within economically vulnerable regions. Design/methodology/approach This study uses a quantitative research design, using covariance-based structural equation modeling (CB-SEM) in Analysis of Moment Structures (AMOS) to empirically validate the proposed conceptual framework. Data was collected through a structured survey using cluster and systematic random sampling techniques, resulting in 679 valid responses being obtained from supply chain professionals across SME manufacturers operating in digitally constrained regions of the United States (Mississippi, West Virginia and Arkansas). Findings This study reveals that supply chain resilience in small and medium-sized enterprises is achieved by integrating artificial intelligence and human interpretation, enabling proactive adaptation to disruptions such as port strikes. Artificial intelligence enhances risk detection through real-time analytics, while collaboration and autonomous systems drive rapid responses, validated through collective interpretation. Sensemaking agility mediates these processes, ensuring context-sensitive actions and fostering resilience as anticipatory adaptability rather than mere recovery, particularly in resource-constrained regions. Originality/value This research bridges two major theoretical domains, DCV and sensemaking, to present a unified, human–AI collaborative approach to resilience. It introduces a maturity-based resilience model and addresses the underexplored role of CC in AI-enabled adaptation. Moreover, it contributes new insights relevant to digitally disadvantaged supply chains, offering a pathway for inclusive and scalable resilience strategies in the face of systemic disruptions.

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Publication Details

Journal
Supply Chain Management An International Journal
Published
2025-12-17
DOI
https://doi.org/10.1108/scm-07-2025-0656
Citations
2
Primary Topic
Supply Chain Resilience and Risk Management
Type
article
Field-Weighted Citation Impact
4.45
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article

Supply chain cognitive resilience through AI-powered risk mitigation and autonomous resilience

Rizwana Rasheed, Aamir Rashid
2 citations
Supply Chain Management An International Journal
Supply Chain Resilience and Risk Management
4.45
article

Supply chain cognitive resilience through AI-powered risk mitigation and autonomous resilience

Rizwana Rasheed, Aamir Rashid
article en
2 citations

Abstract

Purpose This study aims to develop a comprehensive framework for supply chain cognitive resilience by integrating artificial intelligence, collaboration and coordination, autonomous resilience maturity and organizational sensemaking agility. It addresses critical gaps in existing literature, including the limited empirical focus on the interaction between AI-driven automation and cognitive resilience, especially within economically vulnerable regions. Design/methodology/approach This study uses a quantitative research design, using covariance-based structural equation modeling (CB-SEM) in Analysis of Moment Structures (AMOS) to empirically validate the proposed conceptual framework. Data was collected through a structured survey using cluster and systematic random sampling techniques, resulting in 679 valid responses being obtained from supply chain professionals across SME manufacturers operating in digitally constrained regions of the United States (Mississippi, West Virginia and Arkansas). Findings This study reveals that supply chain resilience in small and medium-sized enterprises is achieved by integrating artificial intelligence and human interpretation, enabling proactive adaptation to disruptions such as port strikes. Artificial intelligence enhances risk detection through real-time analytics, while collaboration and autonomous systems drive rapid responses, validated through collective interpretation. Sensemaking agility mediates these processes, ensuring context-sensitive actions and fostering resilience as anticipatory adaptability rather than mere recovery, particularly in resource-constrained regions. Originality/value This research bridges two major theoretical domains, DCV and sensemaking, to present a unified, human–AI collaborative approach to resilience. It introduces a maturity-based resilience model and addresses the underexplored role of CC in AI-enabled adaptation. Moreover, it contributes new insights relevant to digitally disadvantaged supply chains, offering a pathway for inclusive and scalable resilience strategies in the face of systemic disruptions.

Supply Chain Management An International JournalVol. 31(2)
College of Staten Island (US), York College, City University of New York (US)
Openalex Percentile: Top 5%
Supply Chain Resilience and Risk Management
4.45
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