Intelligent Logistics Network Design Using Machine Learning and Transportation Cost Analytics

Abstract

The looming globalization of trade and supply chain has given rise to the need to optimise the logistics network design (LND). Classical approaches to development of logistics networks are being substituted by smart approaches that use machine learning (ML) and analytics of transportation costs. The study is an examination of how machine learning can be used to create a smart logistics network and how it will influence lowering the cost of transportation. Machine learning methods, especially the supervised and unsupervised learning are used to model and predict the most efficient routing, modalities, and network structures. This research involves both historical logistics data and transportation costs measures which helps in finding patterns and maximizing the network design. The findings prove that ML-based models could significantly enhance efficiency of logistics networks, lower the operation costs, and efforts of decisionmaking. Also, this paper determines the difficulties encountered in the implementation of these solutions and recommends that more research is required to combine real-time information with machine learning algorithms to achieve dynamic optimization. The study ends with the recommendations on the future research and possible ways to enhance the logistics network design.