Forecasting warranty claims for recently launched products

• Published in: Reliability engineering & system safety Vol. 106; pp. 160 - 164
• Main Authors: Wu, Shaomin, Akbarov, Artur
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01.10.2012; Elsevier
• Subjects: Applied sciences; Constrained maximum likelihood estimation; Exact sciences and technology; Firm modelling; Forecasting; Mathematics; Non-homogenous Poisson process (NHPP); Operational research and scientific management; Operational research. Management science; Parametric inference; Probability and statistics; Sciences and techniques of general use; Statistics; Warranty claims; Forecasting; Warranty claims; Non-homogenous Poisson process (NHPP); Constrained maximum likelihood estimation; Lower bound; Homogeneous process; Constrained maximum likelihood; Modeling; Poisson process; Marketing; estimation; Upper bound; Protest; Taxes; Guarantee; Maximum likelihood
• ISSN: 0951-8320; 1879-0836
• DOI: 10.1016/j.ress.2012.06.008

Forecasting warranty claims for recently launched products that have short histories of claim records is vitally important for manufacturers in preparing their fiscal plans. Since the amount of historical claim data for such products is not large enough, developing forecasting models with good performance has been a difficult problem. The objective of this paper is to develop an algorithm for forecasting the number of warranty claims of recently launched products. A two-phase modelling algorithm is developed: in Phase I, we estimate the upper and the lower bounds of the warranty claim rates of the reference products that have been in the market for a longer time; in Phase II, we build forecasting models for the recently launched products and assume that their future claim rates are subject to the bound constraints derived from Phase I. Based on this algorithm, we use the NHPP (non-homogeneous Poisson process) and the constrained maximum likelihood estimation to build forecasting models on artificially generated data as well as warranty claim data collected from an electronics manufacturer. The results show that the proposed algorithm outperforms commonly used NHPP models.