AI-driven Automotive Service Market: Towards more Resource-Efficient and Sustainable Vehicle Maintenance

Purpose and goal:

The automotive industry must become more resource-efficient and sustainable. Volvo Group is proud to deliver complete transport solutions, from vehicles suited for any task to services that keep the vehicles running efficiently. It can only be achieved by anticipating where and when a spare part will be needed and delivering that part to the correct region before this need even arises, thus reducing costs and increasing service levels.

Efficient resource use in providing the right parts at the right place and time requires novel predictive logistics. Expected results and effects:

Sustainable transport requires new ML-based, flexible, green services that reduce resource utilisation and costs while increasing customer satisfaction and maintaining a competitive advantage. Higher predictability provides opportunities for better system understanding, large-scale optimisation, quality monitoring, data-driven decisions, and more environmentally friendly transport channels.

We will improve heavy-duty aftermarket sustainability by reducing three aspects: urgent transport orders, back-and-forth haulage, and part scrapping. Approach and implementation:

Project partners are Volvo Service Market Logistics, Rejmes Transportfordon and CAISR at Halmstad University. With more data available, AI and ML methods can help to reduce climate emissions and energy consumption through more efficient use of resources in vehicle aftermarket operations. The AIM-TRUE project focuses on using novel meta-learning tools to improve the services provided by Volvo Group.

We will leverage ML to understand better the factors affecting parts availability and enable individualised inventory control policies.