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.


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.