Support vector regression for warranty claim forecasting
Forecasting the number of warranty claims is vitally important for manufacturers/warranty providers in preparing fiscal plans. In existing literature, a number of techniques such as log-linear Poisson models, Kalman filter, time series models, and artificial neural network models have been developed. Nevertheless, one might find two weaknesses existing in these approaches: (1) they do not consider the fact that warranty claims reported in the recent months might be more important in forecasting future warranty claims than those reported in the earlier months, and (2) they are developed based on repair rates (i.e., the total number of claims divided by the total number of products in service), which can cause information loss through such an arithmetic-mean operation. To overcome the above two weaknesses, this paper introduces two different approaches to forecasting warranty claims: the first is a weighted support vector regression (SVR) model and the second is a weighted SVR-based time series model. These two approaches can be applied to two scenarios: when only claim rate data are available and when original claim data are available. Two case studies are conducted to validate the two modelling approaches. On the basis of model evaluation over six months ahead forecasting, the results show that the proposed models exhibit superior performance compared to that of multilayer perceptrons, radial basis function networks and ordinary support vector regression models.
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- Yeo, Wee Meng & Yuan, Xue-Ming, 2009. "Optimal warranty policies for systems with imperfect repair," European Journal of Operational Research, Elsevier, vol. 199(1), pages 187-197, November.
- Kim, Bowon & Park, Sangsun, 2008. "Optimal pricing, EOL (end of life) warranty, and spare parts manufacturing strategy amid product transition," European Journal of Operational Research, Elsevier, vol. 188(3), pages 723-745, August.
- Bai, Jun & Pham, Hoang, 2006. "Cost analysis on renewable full-service warranties for multi-component systems," European Journal of Operational Research, Elsevier, vol. 168(2), pages 492-508, January.
- Wu, Shaomin & Li, Huiqing, 2007. "Warranty cost analysis for products with a dormant state," European Journal of Operational Research, Elsevier, vol. 182(3), pages 1285-1293, November.
- Carbonneau, Real & Laframboise, Kevin & Vahidov, Rustam, 2008. "Application of machine learning techniques for supply chain demand forecasting," European Journal of Operational Research, Elsevier, vol. 184(3), pages 1140-1154, February.
- Lingras, P. & Butz, C.J., 2010. "Rough support vector regression," European Journal of Operational Research, Elsevier, vol. 206(2), pages 445-455, October.
- David Stephens & Martin Crowder, 2004. "Bayesian analysis of discrete time warranty data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 53(1), pages 195-217.
- Murthy, D. N. P. & Djamaludin, I., 2002. "New product warranty: A literature review," International Journal of Production Economics, Elsevier, vol. 79(3), pages 231-260, October.
- Trafalis, Theodore B. & Gilbert, Robin C., 2006. "Robust classification and regression using support vector machines," European Journal of Operational Research, Elsevier, vol. 173(3), pages 893-909, September.
- Nesreen Ahmed & Amir Atiya & Neamat El Gayar & Hisham El-Shishiny, 2010. "An Empirical Comparison of Machine Learning Models for Time Series Forecasting," Econometric Reviews, Taylor & Francis Journals, vol. 29(5-6), pages 594-621.
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