Title: BAYESIAN IN-SERVICE FAILURE RATE MODELS by Tolunay Alankaya , MS Industrial Engineering
Advisor: Prof. Dr. Savaş Dayanık
Date: Aug 02, 2022 10:00 AM Istanbul
Meeting ID: 654 774 6234
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.