Research On Warranty Claims Monitoring Based On Profile Monitoring Methodology

With the intensified market competition,the quality and reliability of durable goods are playing a more and more important role in marketing.Durable goods often have the characteristics of long service time and high prices.The manufacturers of durable goods are required to provide warranties for products sold.A warranty is a guarantee provided by the manufacturer to the customers on the reliability of the products.Under warranty,the manufacturer has obligation to repair or replace the failed products under normal using conditions in the warranty period.To ensure the quality and reliability of the products sold,manufacturers conduct strict reliability tests on the products during the design and manufacturing process.The reliability tests are often incomplete since this kind of test is time-consuming and very costly.The undetected design and manufacturing defects may break out in the field and bring huge economic and goodwill losses to the manufacturer.Warranty claims data contains useful information on product quality and reliability in the early use stage and therefore is an important data source for the early warning of quality and reliability problems.Statistical process control(SPC)is a widely used tool for detecting anomalies in processes.In this research,the SPC method is applied to monitor warranty claims for detecting design and manufacturing defects and providing early warning about quality and reliability risks.The warranty claims data to be monitored have the following characteristics: 1)the number of claims is affected by factors such as the service time of the product;2)the occurrence of claims is time-sequential;3)the number of claims is attribute data.Based on the above data characteristics,this research transforms the monitoring of warranty claims into the monitoring of profiles with attribute data and designs several monitoring schemes based on the profile monitoring methodology.Then,the proposed monitoring schemes are evaluated and verified through simulation experiments and real enterprise data.In view of the characteristics of warranty claims data,this research considers the monitoring in three scenarios,i.e.,warranty claims data with within-profile correlation,warranty claims data with between-profile correlation,and two-dimensional warranty claims data.Specifically,this research includes the following three aspects.Firstly,for warranty claims data with within-profile correlation,the modified score statistic for retrospective change-point detection and the empirical likelihood ratio(ELR)control chart for real-time monitoring are proposed respectively.The generalized estimating equation(GEE)method is used to model the within-profile correlation,which improves the accuracy of the change-point detection and the monitoring performance of the control chart.Simulation experiments and a real example of warranty claims show that the proposed change-point detection method and the ELR control chart outperform the existing methods in the presence of within-profile correlation.They can be applied not only to autocorrelated profiles but also to independent profiles.In addition,the ELR chart can detect changes in the mean and the correlation simultaneously.Secondly,for warranty claims data with between-profile correlation,the generalized quadratic polynomial model is used to model the profile relationship,and the learning-effect model is adopted to describe the between-profile correlation.Then,an EWMA chart with dynamic control limits is proposed for monitoring warranty claims data with between-profile correlation.Simulation results show that the proposed EWMA control chart has a satisfactory performance in both in-control and out-of-control states.A real example of automobile warranty claims is analyzed to demonstrate the effectiveness of the proposed EWMA control chart.Thirdly,for the two-dimensional warranty claims data,considering the effect of service time and mileage on the number of claims and the small number of claims for a single product during the warranty period,this research focuses on the semiparametric monitoring of the two-dimensional warranty claims data under small within-profile sample size conditions.The generalized semiparametric model is adopted to fit the twodimensional warranty claims data.Then two monitoring schemes with dynamic control limits,the WLR and WF charts,are proposed.The performances of the proposed monitoring schemes are evaluated and verified through simulation experiments and a real data example.This research is of great significance to the development and application of profile monitoring methods,provides new technical support for monitoring warranty claims,and lays a foundation for subsequent research on fault diagnosis,process capability improvement,and claim fraud detection.Moreover,the proposed profile monitoring methods can be applied not only to the monitoring of warranty claims but also to the monitoring of other similar processes or products with profile characteristics.