The reliability of a product is not only important for customers to choose optimal products, but also necessary for manufacturers to design warranty strategies. While predicting the reliability of products accurately is always difficult. Several arithmetic was developed in the existed literature, such as Poisson models, Kalman filter etc. However, these methods hypotheses the distribution of the model, which limits the accuracy of prediction. In this paper, support vector regression (SVR) is used to evaluated the reliability of vehicles through the two-dimensional warranty data. Two aspects of predictions are discussed: prediction based on the same production data and different production data. The squared correlation coefficients of prediction in this paper are all higher than 0.95. In addition, the influences of training set volumes and kernel functions are analyzed. The relationship between the failure rate, failure age and mileage is also considered.
This paper considers a closed-loop supply chain (CLSC) in which two collectors provide used products to a manufacturer for remanufacturing. The collectors act as the channel leader, while the manufacturer is the follower and possesses private demand forecast information. We aim to investigate the manufacturer’s information sharing strategy and the effect of different information sharing strategies on the participants in the CLSC. We find that the manufacturer has an incentive to share its demand forecast information with the collectors. When the collectors’ investment cost-efficiency is high, the manufacturer prefers to share its information with only one collector. Under this scenario, the collector obtains the highest expected profit in all the information sharing cases. In addition, when the investment cost-efficiency is low, the manufacturer is willing to share its information with both collectors.
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