Enhancing Seamless Supply Chain Connectivity and Achieving Operational Excellence - Iacoban, Dorin Cosmin (Digital Repository of Theses, 2025)
In today's increasingly digital and data-intensive environment, supply chain systems are challenged to process vast amounts of real-time data generated by the Internet of Things (IoT) devices. Traditional cloud-based architectures often struggle with high latency and bandwidth constraints, hindering timely decision-making in critical supply chain applications. The research aims to develop and evaluate a dynamic, priority-based task allocation framework that leverages Edge/Fog computing to address these limitations. The core framework is a Priority Equation that integrates principles from Queuing Theory (Willig, 1999) and dynamic thresholding, enabling the efficient distribution of tasks processing load across Edge/Fog and Cloud layers. The research adopts a quantitative approach to analyze the impact of the Priority Equation on latency, resource allocation, and task prioritization in a simulated IoT supply chain environment. Key metrics include task processing time and system scalability under varying data loads by dynamically adjusting task priorities according to real-time demands. The Equation was designed to ensure that critical tasks are processed with minimal delay while optimizing the distribution of non-critical tasks. The results demonstrate that the priority-based framework reduces latency and improves operational efficiency compared to traditional cloud-centric models. Additionally, Edge/Fog computing integration shows improved system scalability, maintaining performance as data volume and task arrival rates increase. Research contributes to Supply Chain Management by introducing a scalable, adaptive framework that enhances real-time responsiveness and resilience in complex, data-driven environments. Priority Equation offers a practical solution for industries where timely high-frequency data processing is essential, such as logistics, healthcare, and manufacturing. This study lays the groundwork for a more efficient, agile, and customercentric supply chain model by optimizing task allocation and leveraging decentralized computing, positioning organizations to navigate the digital age's demands better.