Bsquare works with a global technology leader in the design, manufacture and customer support of premium light-, medium- and heavy-duty trucks to maximize uptime
While this industry leader was collecting critical data from trucks via wireless technologies, they did not have a way of analyzing the data to understand how individual fault codes and operational parameters contributed to an overall complex event. Instead, they were evaluating each fault in isolation without understanding the interdependencies. For fleet owners, this information often led to conflicting recommendations for repair.
Additionally, repair technicians had little information about a fault before starting the diagnostic process. This is a lengthy process, which follows static repair steps based on the available symptoms. The repair manuals were often not kept up to date, which contributed to the inefficient process.
Bsquare proposed a solution that consisted of its own DataV software running on the AWS cloud.
DataV provides a method for applying analytics to the telematics data to understand the relationships between concurrent fault codes. This process combines the fault codes with current operating parameters and historical repair information to automate root cause analysis. The system provides an accurate recommendation to the severity of the problem, enabling fleet operators to manage the impact to their business.
Additionally, DataV empowers the repair technicians by automating much of the diagnostic process. This focuses their attention on the most probable root cause, reducing repair times and improving first time fix rates. The solution factors in actual repair data to improve the overall process, eliminating the need for static repair manuals.
AWS is providing the necessary scale and high-availability to meet the needs of this industry leader manufacturer through the use of a number of services, including Device Shawdows, SQS, EC2, S3, and EMR.
DataV on the AWS cloud provides the ability to run analytics to derive the logic needed to monitor for complex events in real-time. This is being used to combine multiple fault codes, improving fault remediation recommendations to their customer fleets.
The new approach may reduce unnecessary service expense by 33%. It is important to note that this outcome was achieved by applying just four sets of rules. Further analysis and rule generation has the potential to significantly improve this outcome. Through the use of DataV, these results are realized without the challenges of adding additional headcount and without compromising the number of monitored fault codes, providing more value to their customers in a cost-effective manner.
The ability to perform data-driven diagnostics at repair centers will not only increase the throughput of the repair centers, but improve the accuracy of the repair steps and contribute to a larger knowledge base over time.