Abstract
Warranty claim data analysis is a useful tool for the manufacturer because it contains many useful informations regarding reliability of the product in the real-world environments. Because of the nature of uncertainty and the incompleteness of data, some bias patterns are observed on warranty claim rate known as 'spikes'. Two types of spikes are considered. One is due to manufacturing-related failures. The other is caused by customer's behavior. This paper proposes a model by considering two types of spikes. Warranty claim data is analyzed with the proposed model. To represent spikes observed on the early warranty period, we classify failures into manufacturing-related failures and usage-related failures. Uniform distribution is assumed for the time delayed to diagnose and report by customers. By reducing maximum value of the delayed time by customers, the proposed model characterizes customer's rush in the vicinity of the warranty expiration limit. Experimental results by using the real warranty claim data show that the proposed model is better than the existing one in respect to MSE(Mean Squared Error). Moreover it is expected to estimate the failure rate more realistically with proposed model because it considers the delayed time to diagnose and report by customers.