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 .” Clients’ pain-point
Client need
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.
Approach
Predictive maintenance data-driven approach was employed considering product lines/ manufacturing unit specifics.
Solution
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:
Benefits
Avoid unplanned downtime;
Better plan and optimize units’ maintenance schedule;
Inference on the effectiveness of maintenance providers;
