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The data from warranty claims provides a direct view into a customer's experience while sharing insights about which products are causing problems. Warranty data helps you make more informed decisions about the supply chain, customer lifetime value, and product design.

Warranty data provides direct information about how and when your products fail. With this information, you can identify parts and systems that need improvement. Warranty data will even give you insight into what needs to be improved to reduce failure rates and improve customer satisfaction.

Warranty data are composed of claims data and supplementary data. Claims data are the data collected during the servicing of claims under warranty, and supplementary data are additional data (such production and marketing related, items with no claims, etc.)

A warranty analysis uses information about past warranty claims to predict the number and cost of warranty claims in the future. By fitting a distribution to your warranty data, you can estimate the number of expected failures in the next month, the next year, or other period of time.

Warranty data analysis relies on the estimation of a failure distribution based on data including the age and number of returns and the age and number of surviving units in the field.

A warranty analysis is mainly based on the data collected from those services, claims over a certain period. In the Warranty Analysis typically Gamma, Weibull, or lognormal Distribution is observed for the failure of the product over the period.

Warranty Failure Rate means the percentage representing the ratio of the number of products failing during the Warranty Period for such Product divided by the total number of Products shipped to customers during the corresponding period.