Recomotor Prediction, Comparison and Suggestion System (RPCSS)
We develop a system capable of predicting prices, comparing data and generating automatic recommendations, integrating information from junkyards and marketplaces. With this technology, we optimise inventory management, improve profitability and promote sustainability in the automotive sector.
Technology, efficiency and circular economy, serving the future of spare parts.
Project details
- Duration: 05/2023 to 09/2024
- Total investment: €253,522
- European Union aid: €215,494
- Project execution location: Lleida, Spain
Europe Is Felt
Main Objectives
- Predict automotive part prices using advanced machine learning models.
- Integrate internal and external data into a unified platform.
- Automate recommendations and quotes to streamline commercial and operational processes.
- Drive sustainability by optimising the use of recovered parts and promoting the circular economy.
Results
The project has consolidated a robust and scalable platform, integrating internal and external data in real time, with operational predictive models and automated processes, achieving technical and economic efficiency, and laying the foundations for future innovations in artificial intelligence.
| Area / Activity | Planned Scope | Actual Scope |
|---|---|---|
| External data integration | Integrate multiple sources (OEM, junkyards, marketplaces) into the system. | Integration completed, overcoming heterogeneity challenges and achieving real-time updates. |
| Predictive models | Develop models for price, demand and turnover prediction. | Operational models with promising results; in use for analysis and decisions. |
| Technical infrastructure | Scalable and secure cloud platform. | Infrastructure deployed on AWS and Kubernetes, ready to grow and adapt. |
| Process automation | Automate quoting processes and part recommendations. | Automation implemented and integrated with business systems (ecommerce, CRM). |
