Our client, Canada’s largest diversified resources company, was interested in creating a predictive maintenance system for their vehicle fleet. They had a scattered picture of how and when vehicles might break down, leading to unpredictable maintenance, downtime, and excess costs. As we began exploring the problem, our team discovered a secondary issue: inefficient employee shift management meant that drivers were spending a lot of time waiting in queue. We created an automation solution to solve both problems, saving the company over $1.6 million per month.
Standard Preventive Maintenance and Shift Scheduling Leading to Excess Costs and Slowdowns
The company’s manual approach to vehicle maintenance was costly and inefficient. They were struggling to accurately track the health of the hundreds of thousands of components across the fleet, making it challenging to estimate when a vehicle might need service. As a result, trucks and excavators were breaking down unpredictably, leading to excess costs and a slowdown in production. The client was looking for an automated tool that would track each vehicle and its parts in real time, so they could schedule service in advance, reduce costs and downtime, and run an optimized fleet.
During the Ideation phase, where we gain context of all elements of the problem, we discovered another issue the company had not considered. Because drivers started their shifts at the same time, they were all sitting in line in their trucks, waiting for them to be loaded by the excavators. Similarly, near the end of a shift, the excavators were all sitting idle waiting for the next wave of trucks to arrive. And in between, drivers were making best guesses about which excavator to head back to for reloading, resulting in more wait times.
We discovered that the company was spending $4000 for every minute a vehicle was not actively in use. And with each vehicle waiting 20 minutes a day on average, this added up fast.
Real-Time Vehicle Health and Shift Management Tool for Fleet Optimization
First, we created an automated predictive maintenance tool, using vehicle and repair data combined with machine learning and artificial intelligence. The system detects patterns and correlations that lead to equipment failure, and schedules maintenance proactively. The company now has a real-time view of the health of each piece of equipment, and is no longer over or undermaintaining the fleet.
In addition to automating the maintenance schedule, we also automated employees’ shift schedules so that trucks and excavators are running continuously instead of sitting idle. By staggering shift start times and routing trucks to optimal excavators throughout the shifts, we drastically reduced costly downtime.
$1,600,000 Monthly Cost Reduction
Our client now has an automated maintenance and shift scheduling tool that draws from multiple data sources, continuously learns and adapts, is customized to their needs and environment, frees up team members for other strategic work, provides precise maintenance recommendations, virtually eliminates downtime, and optimizes the entire fleet.
Thanks to this implementation, our client has achieved a cost reduction of $1.6 million each month.