challenge
Our customer is the global market leader in refractories. The steel industry is facing increasing price pressure due to stricter environmental regulations and weakened supply chains. In this context, major savings potential can be realized, especially through efficient maintenance of the refractory material of steel pans and containers. The aim of the project was to use predictive maintenance forecasts to predict maintenance intervals and deliveries of refractory bricks more precisely in order to avoid unplanned downtime in production and save maintenance costs.
solution
- Preparation of production data from all locations worldwide
- Development of machine learning models to predict the remaining life of production plants
- Integration of forecast data into maintenance and procurement planning systems
- Comprehensive validation and optimization of the model up to productive use
Business benefits
- Reducing downtime of investment-intensive plants through preventive maintenance measures
- Savings in material procurement and maintenance costs through improved planning
