Cookies help us display personalized product recommendations and ensure you have great shopping experience.

By using this site, you agree to the Privacy Policy and Terms of Use.
Accept
SmartData CollectiveSmartData Collective
  • Analytics
    AnalyticsShow More
    predictive analytics risk management
    How Predictive Analytics Is Redefining Risk Management Across Industries
    7 Min Read
    data analytics and gold trading
    Data Analytics and the New Era of Gold Trading
    9 Min Read
    composable analytics
    How Composable Analytics Unlocks Modular Agility for Data Teams
    9 Min Read
    data mining to find the right poly bag makers
    Using Data Analytics to Choose the Best Poly Mailer Bags
    12 Min Read
    data analytics for pharmacy trends
    How Data Analytics Is Tracking Trends in the Pharmacy Industry
    5 Min Read
  • Big Data
  • BI
  • Exclusive
  • IT
  • Marketing
  • Software
Search
© 2008-25 SmartData Collective. All Rights Reserved.
Reading: Designing performance measurements to identify and reduce warranty waste
Share
Notification
Font ResizerAa
SmartData CollectiveSmartData Collective
Font ResizerAa
Search
  • About
  • Help
  • Privacy
Follow US
© 2008-23 SmartData Collective. All Rights Reserved.
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
JamesTaylor
5 Min Read
SHARE

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…

More Read

Enter Nanosolar, a San Jose-based start-up that manufactures…
From MIT/Sloan Analytics: The New Path To Value
The Miracle of Combining Forecasts
An Interesting Find in a days work
3 Incredible Ways Small Businesses Can Grow Revenue With the Help of AI Tools


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.

Previous

 
Link to original post

Share This Article
Facebook Pinterest LinkedIn
Share

Follow us on Facebook

Latest News

street address database
Why Data-Driven Companies Rely on Accurate Street Address Databases
Big Data Exclusive
predictive analytics risk management
How Predictive Analytics Is Redefining Risk Management Across Industries
Analytics Exclusive Predictive Analytics
data analytics and gold trading
Data Analytics and the New Era of Gold Trading
Analytics Big Data Exclusive
student learning AI
Advanced Degrees Still Matter in an AI-Driven Job Market
Artificial Intelligence Exclusive

Stay Connected

1.2kFollowersLike
33.7kFollowersFollow
222FollowersPin

You Might also Like

BI like Google

6 Min Read

Determine version number in BusinessObjects

2 Min Read

Teradata’s “Multiple paths to social media”

2 Min Read

Customer Churn and Retention

4 Min Read

SmartData Collective is one of the largest & trusted community covering technical content about Big Data, BI, Cloud, Analytics, Artificial Intelligence, IoT & more.

ai is improving the safety of cars
From Bolts to Bots: How AI Is Fortifying the Automotive Industry
Artificial Intelligence
AI and chatbots
Chatbots and SEO: How Can Chatbots Improve Your SEO Ranking?
Artificial Intelligence Chatbots Exclusive

Quick Link

  • About
  • Contact
  • Privacy
Follow US
© 2008-25 SmartData Collective. All Rights Reserved.
Go to mobile version
Welcome Back!

Sign in to your account

Username or Email Address
Password

Lost your password?