Project Summary

This project will develop a sustainable, AI-powered multimodal logistics model to support small-scale food and drink producers across North East England, reducing carbon emissions and transportation costs while improving operational performance. Many rural producers, supported by Food and Drink North East , face challenges such as low vehicle utilisation, resulting in increased trips, higher costs and operational inefficiencies. By collecting real-world logistics data and using advanced optimisation and machine learning techniques at the University of Manchester, the project will identify opportunities for producers to collaborate on shared transport routes, co-delivering perishable and non-perishable products to regional markets. An innovative aspect of the project is leveraging under utilised space in rural passenger buses and trains, which often operate below capacity, turning idle space into revenue-generating cargo movement channels. This would enhance vehicle and network efficiency, reduce road congestion, and expand market access for rural businesses. AI models will determine delivery schedules, vehicle types and clustering strategies based on producer location and demand forecasts. Ultimately, the project will produce actionable recommendations to improve average vehicle utilisation, support phased electric vehicle adoption, and strengthen business to business coordination, enabling Food and Drink North East to foster a more efficient, greener and interconnected rural logistics ecosystem.