Sathiya, Vadivelu (2025) Industry 4.0 technologies on supply chain resilience : insights from logistics and supply chain perspectives in Penang. Masters thesis, Universiti Utara Malaysia.
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Abstract
The adoption of Industry 4.0 technologies is transforming supply chain operations by enhancing efficiency, automation, and resilience. However, logistics and supply chain firms in Penang, Malaysia, face challenges in implementing key technologies such as Artificial Intelligence (AI), the Internet of Things (IoT), Big Data Analytics, Robotics, and Blockchain. Limited adoption, high costs, and cybersecurity risks hinder digital transformation, affecting supply chain resilience. This study examines the role of Industry 4.0 technologies in strengthening supply chain resilience, focusing on key enablers and challenges. Using a quantitative approach, data was collected from 254 firms in the logistics and supply chain sector. Findings show that IoT and AI improve visibility and predictive analytics, robotics enhances operational efficiency, Big Data supports strategic planning, and Blockchain strengthens security and transparency. However, adoption barriers remain a concern. The researcher highlights the need for strategic collaboration among businesses, policymakers, and technology providers to facilitate Industry 4.0 adoption. These insights provide a foundation for companies to enhance digital transformation strategies, ensuring a more resilient and competitive supply chain
| Item Type: | Thesis (Masters) |
|---|---|
| Supervisor : | Mohd Zahari, Fadhilah |
| Item ID: | 11847 |
| Uncontrolled Keywords: | Supply Chain Resilience, Internet of Things ( IoT), Artificial Intelligence, Robotic and Automation, Big Data Analytics, Blockchain |
| Subjects: | T Technology > TS Manufactures > TS155-194 Production management. Operations management |
| Divisions: | College of Business (COB) |
| Date Deposited: | 15 Oct 2025 07:20 |
| Last Modified: | 15 Oct 2025 07:20 |
| Department: | Collage of Business |
| Name: | Mohd Zahari, Fadhilah |
| URI: | https://etd.uum.edu.my/id/eprint/11847 |

