A Comparison of Three Strategies for Forecasting Warranty Claims

Abstract

The economic necessity for developing accurate forecasts of extended warranty costs on durable goods is established. Three strategies for forecasting warranty failures are outlined. The first strategy encompasses the use of linear, predictive models, wherein forecasts are based upon an extrapolation of a least squares fit of the data. These include regression and time series approaches. The risks associated with the use of these models when localized or highly nonlinear phenomena exists are illustrated. The second strategy is based upon the use of dynamic linear models wherein the model parameter estimates are updated sequentially by using the equations for the Kalman filter. The adaptive nature of these models leads to improved forecasts in the presence of localized phenomena. The third strategy encompasses the use of nonparametric modeling approaches, including the use of neural network models to provide a generalized, form-free modeling framework. With a constructed example, the comparative advantage in the use of a neural network representation is demonstrated through the use of a cross-validation strategy to identify and compare the best model in each class of models under consideration.

Additional information

Notes on contributors

Gary S. Wasserman

Gary S. Wasserman is an associate professor at the Department of Industrial and Manufacturing Engineering at Wayne State University. His research interests span the areas of quality and reliability assurance, with special interests in the development and implementation of leading edge quality technologies for industry. Dr Wasserman has worked closely with engineers and scientists at Ford Motor Co. over the past eight years, to provide technical consultation in areas of advanced statistical applications. He is a senior member of IIE, ASA, and ASQC, having served as past-president of the Detroit Senior Chapter and Faculty advisor of the Wayne State University student chapter.

Agus Sudjianto

Agus Sudjianto is a reliability methods engineer in the Powertrain Operations and Advanced Vehicle Technology organizations at Ford Motor Co. in Dearborn, MI. Dr Sudjianto received his Ph.D. in 1996 in Industrial and Manufacturing Engineering from Wayne State University. He has also associated with the Computation and Neural Networks Laboratory in the College of Engineering at Wayne State University. His research interests span the areas of computational intelligence and statistics for data-intensive computing and computer-aided engineering.