<div class="section abstract"><div class="htmlview paragraph">The automotive industry has been funding warranty repair work for many decades.
The most common vehicle warranty is 3 years or 36,000 miles [<span class="xref">1</span>]. Original equipment manufacturers (OEM)
in North America have dealers record all the work completed and submit claims
for the work that qualifies for warranty reimbursement [<span class="xref">2</span>]. The OEM reviews the request and pays dealers for the
work performed. In addition to payments, the database is also used to complete
quality analysis for the vehicles. Often the software being used by dealerships
is old and not designed for quality analysis. Reviewing all the warranty work
done can be an arduous task. OEMs can receive 100,000 or more claims each day.
To speed up the analysis process the OEMs will divide the repair work into
sections based on the segment of the vehicle requiring work. This categorization
allows the OEMs to spread the work across many experts in the company. But what
does the OEMs do when the problem cannot be located at the dealership? The
dealer still requires payment for the time they spent trying to find the issue.
This is often categorized as TNF (trouble not found). This type of work without
a resolution can account for a sizable percentage of warranty costs! It can be
as high as 38% depending on the manufacturer. It can also affect customer
satisfaction and customer loyalty. In fact, one of the most common complaints
submitted to NHTSA (National Highway Traffic Safety Administration) is “dealer
unable to locate problem” [<span class="xref">3</span>]. To make
matters worse, most customers end up returning to the dealer multiple times
before the issue is, if ever, located, and fixed [<span class="xref">4</span>]. So how can we find a better way to analyze these warranty claims
and improve the customer experience while decreasing the cost for the OEM? This
problem can be improved through machine learning and statistical pattern
analysis.</div></div>