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
    unusual trading activity
    Signal Or Noise? A Decision Tree For Evaluating Unusual Trading Activity
    3 Min Read
    software developer using ai
    How Data Analytics Helps Developers Deliver Better Tech Services
    8 Min Read
    ai for stock trading
    Can Data Analytics Help Investors Outperform Warren Buffett
    9 Min Read
    media monitoring
    Signals In The Noise: Using Media Monitoring To Manage Negative Publicity
    5 Min Read
    data analytics
    How Data Analytics Can Help You Construct A Financial Weather Map
    4 Min Read
  • Big Data
  • BI
  • Exclusive
  • IT
  • Marketing
  • Software
Search
© 2008-25 SmartData Collective. All Rights Reserved.
Reading: Measuring and improving an effective and efficient warranty process
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 > Measuring and improving an effective and efficient warranty process
Business IntelligenceCRMData MiningPredictive Analytics

Measuring and improving an effective and efficient warranty process

JamesTaylor
JamesTaylor
5 Min Read
SHARE

Copyright © 2009 James Taylor. Visit the original article at Measuring and improving an effective and efficient warranty process.John Hagen of Trane presented on measuring and improving an effective and efficient warranty process. Trane produces Commercial HVAC systems of every size. Warranty is tricky because they make everything so specific to customers.
Trane started a new […]


Copyright © 2009 James Taylor. Visit the original article at Measuring and improving an effective and efficient warranty process.

John Hagen of Trane presented on measuring and improving an effective and efficient warranty process. Trane produces Commercial HVAC systems of every size. Warranty is tricky because they make everything so specific to customers.

Trane started a new quality initiative in 2004 because they felt that there were some low hanging fruit. They had warranty reserves that were set up in the 80s and no-one knew how they had been calculated. Found that they were under or over-reserved in various areas. They also focused on some major, systemic issues (coils, compressors etc) and as they dug into their claims it was clear they were still paying claims even though they had been focused on solving some of these problems for years. They found that a lot of their problems were misstated and the claims were wrong – for instance, because terms for paying for repairs were fixed and unfair, repairers were mis-reporting to get fair prices for the repair. Outdated policies were driving bad behavior and warranty/concession data was misleading.

More Read

The STEM Profession that Women Dominate
Dealing with Online Profiles
Why Budgeting Is “Mission Critical” in Higher Education
5 Ways Big Data is Changing Marketing Forever
Micro vs. Macro Information Retrieval

In 2003 there was plenty of data but no information. Manufacturing spent a lot of time doing analysis, the supply chain lacked supplier failure data, engineering were missing key field failure information and although there was a focus on customers there were too few analytical measures. And IT systems were disjointed. to fix this they:

  • Identified 49 quality process elements across customer, sourcing, manufacturing and engineering.
  • Defined quality metrics for each of these and this allowed the various groups to specify what they needed to know from the warranty process.
  • Organized around regional quality leaders as well as functional quality leaders.

To manage demands from across the company, Trane has to balance the efficiency and effectiveness of the warranty process. Efficiency involves cost per transaction, early warning (cost per nugget of insight), parts return (cost per root cause). Effectiveness involves validity of claims, missed/over-started problems or incorrect root causes. While they want to replace the warranty management system, for now they have added analytics onto their old system.

Of the 16 steps they found in the warranty process, they focused on process planning, concession claims analysis, reserve and expense planning, returns process, returns policy, customer feedback, field service information capture and product support capture and analysis. Working on these areas has helped them drive down warranty and concession costs from 3.3% to 2.2% (with 2.0% targeted for this year and perhaps 1.5% next year).

Compared with 2003 they have made a lot of progress. Quality and early warning integrated into the customer process, clear reliability and quality goals for engineering, manufacturing is managing time to detect and correct and the supply chain is focused on routine recovery and supplier quality. From an IT perspective they are adding text analytics and automated claims.

Their experience is that warranty and warranty claims are a key part of improving quality. And with a 60% turnover in models in the next 12 months (resulting from new refigerant rules) quality and early detection are going to be more critical than ever. Lessons Learned:

  • Use claims to identify quality issues
  • Start with vendor recovery quickly
  • Define management metrics by functiona and track them
  • Improve communication in both directions and keep the customer in mind
Previous Next


Link to original post

Share This Article
Facebook Pinterest LinkedIn
Share

Follow us on Facebook

Latest News

0622cae5 f7d7 4f74 84b5 eabd1a823dca
How Data-Driven Grocery Recommendations Help Shoppers Eat Better With Less Effort
Big Data Exclusive
business recovering from data loss
How Data-Driven Businesses Protect MySQL Databases from Shutdown
Big Data Exclusive
ai driven task management
Reducing “Work About Work” with AI Task Managers
Artificial Intelligence Exclusive
data center uptime
Why Rodent-Resistant Conduits Are Critical for Data Center Uptime
Big Data Data Management Exclusive Risk Management

Stay Connected

1.2KFollowersLike
33.7KFollowersFollow
222FollowersPin

You Might also Like

Image
AnalyticsBig DataBusiness IntelligenceCloud ComputingData QualityData VisualizationData WarehousingHadoopITMapReduceOpen SourceSocial DataSoftwareSQLUnstructured DataWorkforce Data

8 Features of a True Enterprise-Grade Platform for Hadoop and NoSQL

5 Min Read
Image
Big DataBusiness IntelligenceData ManagementData MiningData QualityData WarehousingITModeling

A Better Way to Model Data

5 Min Read
Image
CRM

Customizing a CRM

5 Min Read
Business Intelligence
Business IntelligenceBusiness Rules

Business Transformations caused by Business Intelligence

7 Min Read

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

data-driven web design
5 Great Tips for Using Data Analytics for Website UX
Big Data
ai in ecommerce
Artificial Intelligence for eCommerce: A Closer Look
Artificial Intelligence

Quick Link

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

Sign in to your account

Username or Email Address
Password

Lost your password?