Optimizing Repair Efficiency with Predictive Spare Parts Forecasting
"We’ve seen a real shift since launching the solution: Faster repairs, fewer unnecessary parts, and better acceptance from the field teams. It’s a step forward for both performance and sustainability."
Service Operations Lead, Manufacturing Leader
About this project:
Our client, a global manufacturer of high-end domestic appliances, wanted to make repair operations more efficient — both to reduce emissions and increase circularity.
We developed a machine learning solution to predict the spare parts most likely needed during a first customer visit, based on multilingual service data and technician input.
The solution helped increase first-fix rates by over 90%, reduce internal returns, and cut unnecessary parts shipments. Built with scalability in mind, the platform integrates with existing tools and continuously improves via real-time feedback loops.
