Data was the problem for this mining company – their fleet of haul trucks was producing more than their current system could effectively process. With 250 sensors on each truck, both false alarms and sensor data were pouring in. Additionally, their maintenance cycle was slightly too short, leading to unnecessary costs and wasted resources.
To combat these problems, the company needed an automated operations scheduler to analyze maintenance data and predict possible failures in their hauling fleet. It would also give them intelligent guidance and prioritized action recommendations to keep trucks in good repair while weeding out false critical alerts.
