Use Case: Shipping Raw Materials Worldwide
A bulk shipping company that ships raw materials internationally, supplied by rail from the interior of the continent.
Raw materials are supplied to manufacturing in Asia.
The company wanted to improve ROI through better-planned maintenance schedules and leverage the data generated by their equipment. If the data could be used to predict when loading conveyor systems were to fail, they could plan to get equipment into maintenance before the predicted failure. Unexpected downtime cost the company millions of dollars in lost productivity.
Using AI to monitor the data generated by their equipment, we were able to save the company millions in lost productivity by avoiding unplanned equipment failure.
Dygital9 formed a team of data engineers and analytics specialists that managed to capture hundreds of data points in real-time from the equipment. The data was stored in data lakes at AWS. After capturing data over time, we were able to predict the chances of equipment failing. After capturing enough data over time, we were then able to predict failures more accurately.
The company increased efficiency by being able to plan when its equipment needed maintenance. They were also able to reduce unplanned maintenance failures that could have cost the company millions of dollars due to loss of productivity/downtime and having to dispatch mobile maintenance technicians to the field for reactive repairs.