Automotive business intelligence speeds ahead with predictive maintenance
When it comes to making high-functioning cars and trucks, automotive fleet managers and industry suppliers have traditionally relied on reactive reliability statistics to improve uptime. For haulage firms, unplanned standstills represent a slew of concerns: inconveniencing drivers, increasing repair costs, lost transportation revenue—and perhaps worst of all—potential damage to customer reputation. But certain companies are getting ahead by switching to a new model; they’re choosing to go with predictive analytics and are implementing new, sophisticated technology to get there.
For industrial vehicle manufacturers, actionable data represents opportunity: fast repair times, enhanced diagnostics and easy service processes. A manufacturer can now leverage knowledge gained from machine learning to schedule preventive maintenance based on hyper-specific, condition-based maintenance. As a result, repairs can be prepared for based on the fuel burn and utilization of the specific vehicle in question.
The proof is on the open road
In a recent case study conducted with the Volvo Group, an iconic manufacturer of trucks, buses, construction equipment and industrial engines, enhanced processes were achieved through integrating new, predictive maintenance strategies. To begin with, Volvo Group had plenty of data: data from pressure, vibration and temperature sensors as well as voltage and flow meters. But data does not a cohesive plan make; the organization needed actionable insights.
By integrating this data with the IBM SPSS Modeler, predictive modeling software that combines business intelligence and probable decision making outcomes, Volvo Group was able to identify patterns from the past and gain insights for the future. And all this knowledge could be used to influence decision making moving forward.
Preventive—and corrective—services are now quite achievable; reliability-centered maintenance is now possible. Organizations today can predict what’s coming next, in real time, and provide repair instructions and identify replacement parts—even before a truck arrives for service. For Volvo Group, this capability had a very real operational impact. It meant a reduction in diagnostic times by up to 70 percent and repair times of more than 20 percent.
The competitive advantage of predictive analytics
In short, predictive analytics—and maintenance—offer a key advantage in planning for repairs and sidestepping potential concerns. Volvo Group achieved a competitive advantage worthy of consideration by automakers everywhere. Be sure to take a look at a video featuring Volvo Group’s Peter Wallin, business intelligence solution architect, elaborate on the use of predictive analytics. And if you’re curious about what you’ve read in this posting, peruse additional background information showcasing several client stories.