PJMS

Evaluating AI’s Impact on Supply Chain Resilience and Disruption Recovery: The Case of Ghana Ports and Harbours Authority, Takoradi

Authors

DOI:

https://doi.org/10.4314/pmsj.v8i4.3

Keywords:

Artificial Intelligence (AI), Supply Chain Resilience, Disruption Recovery, Predictive Analytics, Real-Time Monitoring, Automation

Abstract

The increasing frequency of global supply chain disruptions, driven by geopolitical tensions, pandemics, and climate-related shocks, has heightened the need for resilient and adaptive logistics systems. Artificial Intelligence (AI) is emerging as a transformative tool for enhancing supply chain resilience and recovery capabilities. This study evaluates the impact of AI on supply chain resilience and disruption recovery within the Ghana Ports and Harbours Authority (GPHA), Takoradi.

Specifically, it examines how AI-driven technologies—such as predictive analytics, real-time monitoring, automation, and decision-support systems—contribute to mitigating risks, improving operational efficiency, and accelerating recovery from disruptions. Using a mixed-methods approach, data will be collected through structured questionnaires, interviews with key port officials, and analysis of operational records.

The study seeks to establish the extent to which AI adoption enhances the port’s ability to anticipate disruptions, optimize resource allocation, and maintain continuity of port services. Findings are expected to provide empirical insights into the role of AI in strengthening supply chain resilience in emerging economies, offering policy and managerial implications for GPHA and similar port authorities in Sub-Saharan Africa. Ultimately, the research contributes to the growing body of knowledge on digital transformation in supply chain management and its strategic role in ensuring sustainable port operations.

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Published

2025-10-22

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Section

Articles

How to Cite

Evaluating AI’s Impact on Supply Chain Resilience and Disruption Recovery: The Case of Ghana Ports and Harbours Authority, Takoradi. (2025). Project Management Scientific Journal , 8(4), 23-46. https://doi.org/10.4314/pmsj.v8i4.3

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