Alankaya, Tolunay2022-08-092022-08-092022-082022-082022-08-04http://hdl.handle.net/11693/110397Cataloged from PDF version of article.Thesis (Master's): Bilkent University, Department of Industrial Engineering, İhsan Doğramacı Bilkent University, 2022.Includes bibliographical references (leaves 130-133).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.xvi, 133 leaves : color charts ; 30 cm.Englishinfo:eu-repo/semantics/openAccessHierarchical Bayesian modelsHamiltonian Monte CarloSales forecastingIn-service failuresThesisB161136