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<p>Flood-induced disruptions pose significant challenges to supply chain recovery, particularly in fast-moving consumer goods (FMCG) sectors in developing economies. However, limited empirical evidence exists on how distinct artificial intelligence (AI) capabilities function as decision-support mechanisms for recovery resilience. This study examines how predictive modelling and forecasting, real-time response decision-making, and automated recovery optimisation are associated with recovery performance, where higher values indicate faster and more efficient recovery, with logistics process flexibility as a mediator. Using cross-sectional data from 268 managers and engineers in AI-enabled manufacturing firms in Nigeria, analysed via PLS-SEM, the findings show that predictive modelling and automated recovery optimisation are associated with logistics flexibility and recovery performance. In contrast, real-time response decision-making shows a weaker association and no significant link with logistics flexibility. The study positions AI as an embedded decision-support infrastructure and shows that supply chain resilience is more strongly associated with anticipatory and execution-oriented capabilities than with purely reactive responses, offering practical insights for designing effective AI-enabled recovery systems in disruption-prone environments.</p>

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Artificial intelligence deployment capabilities for resilient supply chains under flood-induced disruptions

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月21日

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  • Nsikan John,
  • Benjamin Eneojo Ameh,
  • Victor A. Umoh,
  • Ine Briggs

摘要

Flood-induced disruptions pose significant challenges to supply chain recovery, particularly in fast-moving consumer goods (FMCG) sectors in developing economies. However, limited empirical evidence exists on how distinct artificial intelligence (AI) capabilities function as decision-support mechanisms for recovery resilience. This study examines how predictive modelling and forecasting, real-time response decision-making, and automated recovery optimisation are associated with recovery performance, where higher values indicate faster and more efficient recovery, with logistics process flexibility as a mediator. Using cross-sectional data from 268 managers and engineers in AI-enabled manufacturing firms in Nigeria, analysed via PLS-SEM, the findings show that predictive modelling and automated recovery optimisation are associated with logistics flexibility and recovery performance. In contrast, real-time response decision-making shows a weaker association and no significant link with logistics flexibility. The study positions AI as an embedded decision-support infrastructure and shows that supply chain resilience is more strongly associated with anticipatory and execution-oriented capabilities than with purely reactive responses, offering practical insights for designing effective AI-enabled recovery systems in disruption-prone environments.