Warranty repairs are often viewed by manufacturing businesses as liability as they cost the manufacturer in cash to pay for warranty service and replacement parts. However, warranty repairs also provide information that a manufacturer can use to improve its understanding of its products, its finances, its suppliers and third-party OEMs, and its customers. The warranty data help the manufacturer to improve product quality. However, warranty data can also give insight into use – and misuse – of your products, increase product safety, improve repair procedures and reduce repair times, and improve warranty service conditionalities, help prepare product use guidelines, bring about change in manufacturing process.

ICURO experts guide global manufacturers and dealers after-sales service with help of warranty analytics and spares demand forecasting. In warranty analysis typically Gamma, Weibull, or lognormal distribution is observed for the failure of the product over the period. Each spare part is modeled, and its scoring function is stored in such a way that periodically the forecast of failures per item is generated. We enable manufacturers to develop, manage, and deploy accurate solutions for warranty analytics including root cause analysis, warranty service pack optimization, and warranty risk profile analysis which generate insights to help improve product and company reputations, new product designs, and find out warranty fraud before it becomes a serious concern.

The modus operandi we follow include historical data analysis, understanding of part failures, fraud claim processing, and client management. This  assists us in giving operational feedback, turnaround time, components performance, and estimating the warranty cost per product. Also the spare parts demand assessment is done by classifying them through ABC classification approach, lorenz curve, Pareto principle to find the demand frequency, regularity  and demand patterns. The spare part demand is strongly related to the number of primary products purchased, the age structure and the utilization intensity of primary products in use. Also, lifetime, exploitation and recycling composition of the primary product of standard parts, modules or specialized parts. In the scope of forecasting the known failure rate of the parts, the security stocks and the demand history additionally influence the future demand. For spare part demand forecasting techniques, ARIMA models, exponential smoothing and Croston’s method are used. Alternatively to statistical methods, recurrent neural networks for solving non-linear complex problems are employed.