AI-driven Automotive Service Market Logistics
| Title | AI-driven Automotive Service Market Logistics |
|---|---|
| Summary | This project, in collaboration with Volvo Logistics, focuses on using state-of-the-art methods based on meta-learning to improve demand forecasting, inventory management and spare parts availability at Volvo dealers and warehouses. |
| Keywords | |
| TimeFrame | Fall 2024 |
| References | |
| Prerequisites | Good knowledge of machine learning |
| Author | |
| Supervisor | Sławomir Nowaczyk, TBD |
| Level | Master |
| Status | Open |
This thesis topic is connected to our research project AIM-TRUE (AI-driven Automotive Service Market: Towards more Resource-Efficient and Sustainable Vehicle Maintenance). The project focuses on using state-of-the-art methods based on meta-learning to improve the services provided by the Service Market. In particular, more predictability enables the use of environmentally friendly transport channels and reduces the scrapping of parts due to obsolescence. AIM-TRUE will leverage ML to better understand the factors affecting parts availability and enable individualised inventory control policies. The project’s primary goal is to improve heavy-duty aftermarket resource efficiency and sustainability by reducing three aspects: urgent transport orders, back-and-forth haulage, and part scrapping. The new generation of predictive logistics provides opportunities for better system understanding, large-scale optimisation, quality monitoring, and new data-driven innovative services, all of which are prerequisites for the efficient use of resources – while providing the right parts at the right place and time.
