Akbarov, A. and Wu, S. (2012) Forecasting Warranty Claims Considering Dynamic Over-Dispersion. International Journal of Production Economics, 139 (2). pp. 615-622. ISSN 09255273 .
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Forecasting warranty claims are vitally important for manufacturers in preparing their fiscal plans as well as in managing their inventory. One of the widely used forecasting models is the non-homogeneous Poisson process (NHPP), which assumes that the mean and the variance of the numbers of warranty claims at any given time interval are equal. However, this is not always the case. Warranty claim data often exhibit a phenomenon known as over-dispersion, which implies that the variance to mean ratio is larger than one. Furthermore, this ratio might change over time and can have a trend or a clearly discernible functional form, which has not yet been considered in the existing literature on warranty claims forecasting. This paper presents a warranty claim forecasting approach that tackles the problem of the dynamic over-dispersion exhibited in warranty claims data. It considers the application of both mixed NHPP and Cox process models to warranty claims and assumes that the intensity of the mixed NHPP follows a gamma distribution and the intensity of the Cox process follows a gamma process. Warranty claim data collected from an electronics product manufacturer are used validate the models, which show that these models outperform conventional NHPP models. Â© 2012 Elsevier B.V. All rights reserved.
|Additional information:||Unmapped bibliographic data: PY - 2012/// [EPrints field already has value set] AD - School of Applied Sciences, Cranfield University, Bedfordshire MK43 0AL, United Kingdom [Field not mapped to EPrints] JA - Int J Prod Econ [Field not mapped to EPrints]|
|Uncontrolled keywords:||Cox process, Mixed Poisson process, Poisson process, Warranty data, Warranty forecasting, Warranty prediction, Cox process, Electronics products, Forecasting models, Functional forms, Gamma distribution, Gamma process, Nonhomogeneous Poisson process, Poisson process, Time interval, Variance-to-mean ratio, Warranty claims data, Warranty data, Warranty prediction, Dispersions, Manufacture, Poisson distribution, Forecasting|
|Subjects:||H Social Sciences
H Social Sciences > HA Statistics > HA33 Management Science
|Divisions:||Faculties > Social Sciences > Kent Business School
Faculties > Social Sciences > Kent Business School > Management Science
|Depositing User:||Cathy Norman|
|Date Deposited:||26 Sep 2012 14:24|
|Last Modified:||16 Oct 2012 15:36|
|Resource URI:||http://kar.kent.ac.uk/id/eprint/31002 (The current URI for this page, for reference purposes)|
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