AI-driven Inventory Management for Flexible, and Efficient NextGen Supply Chains (AIM-FLIX)
| Reference number | |
| Coordinator | Högskolan i Halmstad - Högskolan i Halmstad Akademin f informationsteknologi |
| Funding from Vinnova | SEK 8 000 000 |
| Project duration | November 2025 - December 2028 |
| Status | Ongoing |
| Venture | Transport and mobility solutions - FFI |
| Call | Transport and mobility services - FFI - autumn 2025 |
Purpose and goal
Global supply chains in the automotive aftermarket are increasingly complex, with intermittent demand, regional variations, and sudden spikes. Traditional inventory management approaches often fail to account for these dynamics, resulting in emergency shipments, excess stock, high returns, and increased environmental impact. AIM-FLIX enables adaptive, network-wide policies for inventory planning and rebalancing, dynamically coordinating service levels, costs, and sustainability considerations.
Expected effects and result
AIM-FLIX will explore solutions such as shared stocking (treating inventories across warehouses and dealers as a shared pool), and survival analysis (understanding the probability of adverse events, such as stock-outs, returns, or obsolescence, occurring over time)—all to improve responsiveness, reduce duplication, and optimise stock placement. Measurable targets include reducing rush orders by 20% and parts scraping by 15%.
Planned approach and implementation
The AIM-FLIX project builds on the results of the AIM-TRUE project, which developed novel AI/ML tools and demonstrated the feasibility of meta-learning for spare parts demand forecasting in realistic scenarios and at a global scale. The AIM-FLIX project takes the next step toward Reinforcement Learning (RL)–driven multi-echelon policy optimisation across the entire interconnected supply network.