Warranty reserve forecast for complex products

Sammanfattning: Warranty is a contract between a seller and a buyer. A warranty is also a signal for quality that can be utilized both by the seller and the customer. Even though the warranty is mainly something that is positive, a warranty also incurs risk to the warranty provider. The warranty cost can range from 2 - 15% of the net sales of a product which means that the warranty cost could potentially affect the company a lot. In order to handle such a risk, forecasting is a necessary tool. Forecasting is no exact science, and it’s impossible to forecast the exact future value due to uncertainty. Factors such as the quality of the product affect the cost of the claims, but unpredictable factors such as fraud, human factors and sales delay must also be considered. Forecasting error affects the warranty provider because the forecast sets the warranty reserve, which should cover the costs of the warranty claims. Overestimations and underestimations of the reserve have negative consequences for the company. The purpose of this study is to forecast a warranty reserve using a quantitative approach, in order to gain insight in to which model that could be best suited for complex products. The models that have been tested originate from causal and time-series methods, where the models tested consider different aspects of the data. The primary data used comes from archive data and to determine the forecast error, error measurements have been utilized. The time series method exponential smoothing Holt’s- Winter’s method was the one that performed best considering the error measurements. From the models tested, it has been shown that a more complex model does not necessarily mean a more accurate result. To be able to decrease the forecasting errors, a model considering unpredictable factors such as fraud could be the answer which makes it interesting to investigate.