Implementing Advanced Analytics for Optimizing Food Supply Chain Logistics and Efficiency

Nsisong Louis Eyo-Udo, Anate Benoit Nicaise Abbey and Iyadunni Adewola Olaleye
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Nsisong Louis Eyo-Udo: Ulster University, United Kingdom
Anate Benoit Nicaise Abbey: Ulster University, United Kingdom
Iyadunni Adewola Olaleye: Ulster University, United Kingdom

International Journal of Research and Scientific Innovation, 2024, vol. 11, issue 12, 861-889

Abstract: The global food supply chain faces significant challenges in ensuring efficiency and minimizing disruptions, especially in the face of unexpected events, fluctuating demand, and logistical complexities. Implementing advanced analytics within food supply chains can transform operational dynamics, enabling precise prediction of potential disruptions and enhancing logistics efficiency. This study proposes a robust framework that leverages machine learning algorithms to forecast disruptions—such as delays in transport, shifts in consumer demand, or adverse weather conditions—and to optimize the logistics flow accordingly. By incorporating predictive modeling and real-time data analysis, this framework aims to enhance decision-making capabilities across various stages of the supply chain, from production and warehousing to transportation and delivery. The proposed framework utilizes a combination of supervised learning for demand forecasting and unsupervised learning for anomaly detection, enabling early identification of potential logistical challenges. Furthermore, integrating these analytics with real-time data sources, such as IoT sensors and satellite imagery, facilitates a comprehensive view of supply chain status, thereby increasing responsiveness and adaptability to unforeseen events. The potential future integration of this framework with autonomous delivery systems, such as drones or autonomous vehicles, could further streamline last-mile logistics, reducing both cost and delivery time while maintaining the quality and safety of perishable goods. This study not only demonstrates the immediate benefits of machine learning-driven predictive analytics but also discusses the scalability and flexibility of this approach for broader applications within global food systems. Future research can explore enhancements in algorithmic accuracy and integration with blockchain for traceability, supporting transparent, resilient, and highly efficient food supply chains. The integration of autonomous delivery systems will additionally be a key focus for ensuring seamless, reliable, and sustainable logistics operations in a rapidly evolving food industry landscape.

Date: 2024
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