From Data to Decisions: Leveraging Digital Twins, AI, and IoT for Smarter Supply Chain Management

This research explores how Digital Twins and the Internet of Things (IoT) can be integrated to transform supply chain management at scale. Modern supply chains often face persistent challenges such as limited real-time visibility, low forecasting accuracy, rising operational costs, and heightened exposure to both local and global disruptions. Traditional systems frequently provide delayed or incomplete insights, leading to bottlenecks, inefficiencies, and reduced competitiveness. Advances in technology now make it possible to overcome these limitations and reshape the way supply chains are designed and managed. The study has three primary objectives: (a) to conduct a systematic review of the literature on the combined use of IoT and Digital Twins in supply chains; (b) to propose a conceptual framework that unifies these technologies into a coherent model; and (c) to design and implement a simulation prototype that demonstrates how this integration can enhance responsiveness, efficiency, and resilience. In this context, IoT devices will be assessed for their role in transmitting continuous, sensor-based data streams, while Digital Twins will be examined as real-time virtual models capable of simulating, monitoring, and optimizing supply chain activities. By addressing current gaps in research, this study seeks to contribute both theoretical insights and practical applications, supporting the development of supply chain systems that are more adaptable, sustainable, and resistant to disruption.

Keywords

Digital Twins, Internet of Thing, Supply chain management, Artificial Intelligent

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