Information Management and Business Review - 2024 : Artificial Intelligence-Powered Risk Assessment in Supply Chain Safety

Prof. Dr. Farha, i Abdol Ghapar (2024) Information Management and Business Review - 2024 : Artificial Intelligence-Powered Risk Assessment in Supply Chain Safety. Information Management and Business Review, 16 (3s). pp. 107-114. ISSN 2220-3796

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Abstract

The increasing complexity and globalization of supply chains necessitate robust risk management strategies to ensure safety and resilience. Traditional risk assessment methods often fall short in dynamically adapting to the rapidly changing conditions and voluminous data inherent in modern supply chains. This study explores the potential of Artificial Intelligence (AI)-powered risk assessment to address these limitations in the context of Malaysia's supply chain industry. By employing AI technologies such as machine learning, IoT, and predictive analytics, organizations can significantly enhance their risk management capabilities, improving predictive accuracy, real-time monitoring, and overall operational efficiency. Through a qualitative analysis involving in-depth interviews with supply chain managers, AI experts, and technology vendors, the study identifies the strategies employed for AI integration, the perceived effectiveness of these technologies, and the challenges faced in implementation. The findings highlight the importance of robust data governance, the development of explainable AI models, and continuous skill development to overcome barriers related to data quality, model interpretability, and high implementation costs. The study concludes with recommendations for fostering a safer and more resilient logistics environment in Malaysia, emphasizing the need for comprehensive AI adoption frameworks and scalable solutions for small and medium-sized enterprises.

Item Type:	Article
Uncontrolled Keywords:	Artificial Intelligence, Supply Chain Risk Management, Logistics Safety
Divisions:	Institute of Graduate Studies (IGS)
Depositing User:	LIBRARY1 UPTM
Date Deposited:	08 Jul 2025 02:38
Last Modified:	08 Jul 2025 02:38
URI:	http://eprints.kuptm.edu.my/id/eprint/4782

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