Proactively address rundown time source and hence costs
“ Malfunctions and equipment failure lead to downtime. Downtime leads to loss. We need a functional early warning system to address this .”
Our Client needs enhanced attitude towards the process of predicting when failure may occur by monitoring equipment performance . This will help determine the optimal moment for maintenance to be performed and thus reduce downtime, hence loss.
Predictive maintenance data-driven approach was employed considering product lines/ manufacturing unit specifics.
The solution comes from the Machine Learning domain and is multistage.
Different sensors’ data are aggregate leveraging statistical learning where the outcome is the manufacturing unit traffic-light health factor:
- Avoid unplanned downtime;
- Better plan and optimize units’ maintenance schedule;
- Inference on the effectiveness of maintenance providers;
- Manage maintenance costs.
- Minimize spares inventory;
- Optimum equipment life.