Author

  • N. Sureshkumar PP Narayanan

    • University of East London, London, England
    • United Kingdom

  • Farha Ghapar

    • Universiti Poly-Tech Malaysia, Kuala Lumpur, Malaysia
    • Malaysia

  • Li Lian Chew

    • Binary University, Binary Business School, Selangor, Malaysia
    • Malaysia

  • Veera Pandiyan Kaliani Sundram

    • RIG–Sustainable Supply Chain Logistics / Faculty of Business and Management, Universiti Teknologi MARA Selangor, Malaysia
    • Malaysia

  • Babudass M.Naidu

    • Bumi Sendayan Sdn. Bhd., Petaling Jaya, Selangor, Malaysia
    • Malaysia

  • Mohd Hafiz Zulfakar

    • RIG–Sustainable Supply Chain Logistics / Faculty of Business and Management, Universiti Teknologi MARA Selangor, Malaysia
    • Malaysia

  • Azimah Daud

    • RIG–Sustainable Supply Chain Logistics / Faculty of Business and Management, Universiti Teknologi MARA Selangor, Malaysia
    • Malaysia

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.

Keywords

References

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How to Cite

Narayanan, N. S. P., Ghapar, F., Chew, L. L., Sundram, V. P. K., M.Naidu, B., Zulfakar, M. H., & Daud, A. (2024). Artificial Intelligence-Powered Risk Assessment in Supply Chain Safety. Information Management and Business Review, 16(3S(I)a), 107-114. https://doi.org/10.22610/imbr.v16i3S(I)a.4124

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Copyright (c) 2024 N. Sureshkumar PP Narayanan, Farha Ghapar, Li Lian Chew, Veera Pandiyan Kaliani Sundram, Babudass M.Naidu, Mohd Hafiz Zulfakar, Azimah Daud (Author)

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Research Paper

Published in

Vol. 16 No. 3S(I)a (2024) Supply Chain Resilience, Islamic Finance & Micro-Influence View issue contents

Published

2024-10-27

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