TY  - CHAP
AB  - Abstract A Bayesian approach to demand forecasting to optimize spare parts inventory that requires periodic replenishment is examined relative to a non-Bayesian approach when the demand rate is unknown. That is, optimal inventory levels are decided using these two approaches at consecutive time intervals. Simulations were conducted to compare the total inventory cost using a Bayesian approach and a non-Bayesian approach to a theoretical minimum cost over a variety of demand rate conditions including the challenging slow moving or intermittent type of spare parts. Although Bayesian approaches are often recommended, this study’s results reveal that under conditions of large variability across the demand rates of spare parts, the inventory cost using the Bayes model was not superior to that using the non-Bayesian approach. For spare parts with homogeneous demand rates, the inventory cost using the Bayes model for forecasting was generally lower than that of the non-Bayesian model. Practitioners may still opt to use the non-Bayesian model since a prior distribution for the demand does not need to be identified.
VL  - 10
SN  - 978-1-78441-209-8/1477-4070
DO  - 10.1108/S1477-407020140000010018
UR  - https://doi.org/10.1108/S1477-407020140000010018
AU  - Lindsey Matthew
AU  - Pavur Robert
PY  - 2014
Y1  - 2014/01/01
TI  - Evaluating a Bayesian Approach to Forecasting Stocking Spare Parts that Require Periodic Replenishment
T2  - Advances in Business and Management Forecasting
T3  - Advances in Business and Management Forecasting
PB  - Emerald Group Publishing Limited
SP  - 111
EP  - 128
Y2  - 2024/04/25
ER  -