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ClearForest - Quality & Warranty Solutions
       
A recent study by Boston-based AMR Research showed vehicle manufacturers face up to $14B in warranty costs annually. In fact, every day a quality issue goes unseen or unresolved costs an enterprise dearly. Manufacturers are under increased pressure to systematically improve product quality and protect brand value while complying with ever-tightening industry regulations such as Transportation Reliability Enhancement, Accountability & Documentation [TREAD].

Until recently, a vital component has been missing—the "voice" of the customer, service technician, and quality analyst-- all of whom have firsthand knowledge of root cause facts surrounding product failure. This valuable information, found in customer support comments or service technician notes, is not fully utilized because it exists within free-format text that previously defied structured data analysis. The alternative - manually sifting through content and reading - is slow, costly and inefficient.

ClearForest's Quality and Warranty Solution bridges the gap between situational context found in text and enterprise data analysis. Manufacturers can extract, structure and organize unstructured content from text-based warranty claims, satisfaction surveys, technician notes and customer comments for inclusion into quality analysis.

This powerful capability enables global enterprises to derive value from text. It positions them to understand the "why" rather than simply the "what" in early warning and root-cause diagnostics. Most importantly, it provides the context that an organization needs to spot trends and respond quickly.


  Benefits:
 
   
Early detection and analysis of failure events.
Faster determination of root causes.
More efficient and effective TREAD compliance.
Streamlined claims review and handling.
Improved engineering visibility into text-based market and service information.

 
 
  Resources

Quality & Warranty Solution Brochure
 
Issue Analytics Solution Brochure
 
IDC White Paper:
       Putting the Why in BI

 

 
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