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.

Project Achievements

AI-powered Digital Application for Rural to Urban Supply Chain Logistics – Secured research funding of around Euro 300k from Horizon Europe for the Supply Chain Logistics domain – Progressing our commercialisation discussion with companies such as Hubl Logistics

Conclusions

It was a tremendous experience of working on the TRIG programme, interacting with different organizations, Department for Transport.

Next Steps

We plan to further develop the digital twin on top of our AI-powered Digital Application for the Supply Chain Logistics problem. We also plan to develop different use-cases while collaborating with the interested organizations and show how our application works for different businesses.