SPARE PARTS CONSUMPTION FORECASTING USING A HIERARCHICAL BAYESIAN MODEL

One of industrial companies’ challenges, especially for intensive-use plants and other assets, is the proper sizing of the stock of strategic spare parts, items that have a history of low consumption, but whose lack can cause delays in repair and maintenance services, at the extreme leading to operational shutdowns. Effects can be from small to large scales. While on one hand, having a large stock of strategic items can provide a greater guarantee of operational availability, on the other hand, it brings additional storage and preservation costs, in addition to fixed capital outlays. A compromise solution is needed. The use of traditional or simpler techniques to infer the ideal level of stock for each spare often suffers from lack of historical data, especially in installations in the initial phase of the operation and maintenance cycle. Another problem is the diversity of applications for some materials. The present work proposes a method based on reliability and Bayesian hierarchical models (HBMs) to overcome the problems of data scarcity, uncertainties and variability between applications of each spare part. The criticality of the equipment or assets in which the spare parts are applied is taken into account in the method. The hierarchical Bayesian model enables updating information as new consumption of strategic items is registered. The method is tested for a stationary offshore oil and gas unit.