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SmartData Collective > Business Intelligence > CRM > Designing performance measurements to identify and reduce warranty waste
Business IntelligenceCRMData MiningPredictive Analytics

Designing performance measurements to identify and reduce warranty waste

JamesTaylor
Last updated: 2009/03/12 at 12:54 PM
JamesTaylor
5 Min Read
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Doug Maddox discussed how Chrysler has worked on warranty waste and performance measures to help prevent and eliminate it. In the past, Chrysler had relied on Expense per Unit Repaired to monitor dealers but this was easily manipulated (high cost repairs could be hidden) and it drove adverse behaviors because dealers would refuse to perform certain kinds of repairs that would trigger review.

To fix this, the first priority was to define “warranty waste”. The image is that this kind of waste is primarily fraud – fictitious claims or exaggerated repairs. This was actually a small percentage so they moved to monitor waste more holistically to include Repair Facility issues (training issues, poor diagnostics, no root cause analysis), Corporate issues (arbitrary guidelines for payment, incorrect diagnostic information and parts problems) and Market issues (customer expectations and power of the internet to spread rumors of problems). This waste has broad impact – not just the direct expense but the customer impact and the effort of corporate to manage problems that don’t exist.

Their solution was a system that used all the claims data and generates reports that allow repair facilities to…

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Doug Maddox discussed how Chrysler has worked on warranty waste and performance measures to help prevent and eliminate it. In the past, Chrysler had relied on Expense per Unit Repaired to monitor dealers but this was easily manipulated (high cost repairs could be hidden) and it drove adverse behaviors because dealers would refuse to perform certain kinds of repairs that would trigger review.

To fix this, the first priority was to define “warranty waste”. The image is that this kind of waste is primarily fraud – fictitious claims or exaggerated repairs. This was actually a small percentage so they moved to monitor waste more holistically to include Repair Facility issues (training issues, poor diagnostics, no root cause analysis), Corporate issues (arbitrary guidelines for payment, incorrect diagnostic information and parts problems) and Market issues (customer expectations and power of the internet to spread rumors of problems). This waste has broad impact – not just the direct expense but the customer impact and the effort of corporate to manage problems that don’t exist.

Their solution was a system that used all the claims data and generates reports that allow repair facilities to self-correct – not about corporate monitoring. It also uses an outlier strategy to identify repair facilities with significant differences from others to avoid chasing down problems that don’t really exist.

To make this work they had to normalize rates and parts markups and apply a product mix factor based on the kinds of vehicles being repaired. A minimum claim count is applied so that someone is not inspected until they have repeated behaviors (about 5 repairs). This comparison of dealers and these cut offs help ensure that they generally find problems when the system tells them to investigate. They identify dealers above this threshold and tracks repair group, actual labor, parts, frequency. Frequency is the biggest problem.

The dealers are involved in the system and have access to all the results. It is designed to be a very collaborative process not a central inspection tool. Feedback from the dealers has resulted in them adding climate, terrain, service mix and other aspects. Dealers get a quarterly report showing how they compare with other dealers and with the group of dealers of which they are part. They get a summary of their problems, a top 5 list and information about how their authorization level is being impacted by the problems. Ongoing monthly reports show them how they are doing against the problems identified and they get access to the original data through a what-if tool.

An interesting and very collaborative approach to monitoring.

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JamesTaylor March 12, 2009
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