Manufacturers spend a great deal of money on warranty costs, anywhere between 2-5% of sales. Therefore, proactively streamlining claim processes, detecting emerging defects and fraud helps improving not only the bottom line but also customer satisfaction.
While manufacturers want to implement Warranty analytics, they face multiple problems with data residing in disparate systems due to the complexity of supply chains, with multiple service providers and call centers handling claims. Telematics or real-stream data gathered from assets is also something manufacturers need to explore for added advantage in claims processing.
With BIRD warranty analytics, through its big data Architecture and ability to easily process and model massive amounts of disparate data types, and with an impressive combination of
Advanced ML analytics such as regression, classification, text mining
Can alert users in real-time on KPIs such as Cost per Asset, Failure Rate and detect product failures early on. In addition, it can, identify fraudulent claims, recover warranty costs from suppliers, and more.
Following are some of the advanced ML models BIRD developed to address multiple aspects of Warranty Analytics
Forecasting Number of Claims
Automated Claim Processing
Advanced statistics such as Weibull Distribution, ML models such as Deep Neural Nets, XgBoost and Text Analysis are used to address the above use-cases