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<p>In a dynamic business environment, firms face numerous uncertainties that significantly impact operational effectiveness. Recently, artificial intelligence (AI) has become a powerful toolkit for identifying and managing known and unknown uncertainties. This study examines how AI technology enhances operational performance amid uncertainty, a key concern for firms seeking to gain a competitive edge through advanced technology. Grounded in dynamic capabilities theory (DCT), we developed a construct-based model that includes mediating, moderating, and direct relationships. An empirical analysis was conducted on a sample of 811 leading logistics firms, focusing on the implementation of AI in their operations. We employed partial least squares structural equation modeling (PLS-SEM) to test the proposed hypotheses among latent variables and constructs. The results demonstrate that both known and unknown uncertainties positively influence operational performance. Furthermore, AI provides stability and improvement in logistics operations, thereby enhancing competitive positioning. AI has significantly advanced the management of supply chain uncertainties, reducing operational errors and mitigating risks. The findings suggest that policymakers should consider adopting AI technologies in logistics operations to effectively address and navigate uncertainties.</p>

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Artificial intelligence-driven strategies for enhancing operational performance and managing uncertainties in the supply chain management

  • 期刊论文

  • 发表日期：

    2026年5月9日

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  • Zulkaif Ahmed Saqib,
  • Luo Qin,
  • Muhammad Ikram

摘要

In a dynamic business environment, firms face numerous uncertainties that significantly impact operational effectiveness. Recently, artificial intelligence (AI) has become a powerful toolkit for identifying and managing known and unknown uncertainties. This study examines how AI technology enhances operational performance amid uncertainty, a key concern for firms seeking to gain a competitive edge through advanced technology. Grounded in dynamic capabilities theory (DCT), we developed a construct-based model that includes mediating, moderating, and direct relationships. An empirical analysis was conducted on a sample of 811 leading logistics firms, focusing on the implementation of AI in their operations. We employed partial least squares structural equation modeling (PLS-SEM) to test the proposed hypotheses among latent variables and constructs. The results demonstrate that both known and unknown uncertainties positively influence operational performance. Furthermore, AI provides stability and improvement in logistics operations, thereby enhancing competitive positioning. AI has significantly advanced the management of supply chain uncertainties, reducing operational errors and mitigating risks. The findings suggest that policymakers should consider adopting AI technologies in logistics operations to effectively address and navigate uncertainties.