Warranty claims function as primary source of characterizing field failures across industries, wherein appropriate classification of these claims is critical for further analysis. The classification of warranty claims is a highly laborious effort, involving significant man-hours of warranty analysts. This can be highly optimized and made efficient using direct interpretation of the claim data on 3D model using unity game engine. Additionally, the color perception technique using immersive technology (AR/VR) can help to identify the vital few & drive prioritization of the field failures leading to faster problem resolution.
The capabilities of UI/UX & advanced visualization are integrated to develop novel methods to classify the warranty claims & interpret it on a 3D model using immersive technology which is novel and one of its kind in industry. Unique characteristics of this tool is it focuses on the warranty claim classification by claim cost & count of claims and presents the heat map (Red-High, Yellow- Moderate, Green- Low, Gray – No claims) which helps in faster visualization of the claims on actual product.
We have also integrated the respective corrective action associated for a failure mode which can help us to understand the status of actions plan to be deployed / closed and track for its future effectiveness.
The NLP algorithm has been trained through a multi-representative training dataset which covers all the failure categories based on experiential knowledge. Cross-validation technique is used for optimizing the machine learning algorithm parameters. The test datasets consisting of different mix of warranty claims were then fed into the model wherein the prediction accuracy of more than 90% achieved over multiple runs consistently.