Spare part demand forecasting with bayesian model

Abstract

Continuous change in technology and differentiation in product models in industrial market have an indispensable impact on forecasting demand for spare parts. Inventory managers periodically update their predictions of future demand rates for products. A Bayesian model, using a prior probability distribution for the demand rate, was used to obtain optimal inventory levels over several periods assuming a known cost for surplus and shortage. However, its performance has not been examined under various demand rates such as intermittent demand. Study examines such conditions with a research question.