Bayezyen servis içi arıza oranı modelleri

Predicting the number of appliance failures during service after sales is crucial for manufacturers to detect production errors and plan spare part inventories. We provide a two-phased Bayesian model that predicts the number of refrigerators that fail after sales. Thus the study focuses on both sales forecasting and failure detection. The two-phased Bayesian model is trained by the datasets provided by a leading durable home appliances company. The accuracy results show that one-level models are inferior to multi-level models when the data are sparse. We conclude that hierarchical Bayesian models are preferable since they can naturally capture the heterogeneity across all blends of attributes.