Powering Performance

Quality issues in the field need to be identified and resolved quickly, to contain costs and also to maintain end-consumer satisfaction. Data-driven techniques are the most effective and efficient means to identify such issues rapidly because they provide an inexpensive, centralized solution, without the need to ship physical parts or have analysts travel. Quality issues may be found and tracked semi-automatically by setting up and interpreting appropriate alerts. Early notifications involve inherently complex methods to avoid false alarms while not missing important real issues. With experience and experimentation, combined with the means to set alerts on diverse data sources, useful early quality alerts are feasible.

System-level problems are often indicated in repairs with replaced components, with failed parts tested showing No Trouble Found. Some aspects of this difficult and widespread problem are caused by system-level failures due to unexpected interactions among elements of the system (rather than component-level issues). We examine this NTF issue by the automated analysis or mining of repair and maintenance data generated by automotive service technicians.