Forecasting spare part demand with installed base information: A review

Abstract

The classical spare part demand forecasting literature studies methods to forecast intermittent demand. The majority of these methods do not consider the underlying demand generating factors. Demand for spare parts originates from the part replacements of the installed base of machines, which are either done preventively or upon breakdown of the part. This information from service operations, which we refer to as installed base information, can be used to forecast future spare part demand. In this paper we review the literature on the use of such installed base information for spare part demand forecasting to asses (1) what type of installed base information can be useful; (2) how this information can be used to derive forecasts; (3) what is the value of using installed base information to improve forecasting; and (4) what are the limits of the currently existing methods. The latter serve as motivation for future research.

Forecasting vehicle's spare parts price and demand

“…El Hayek et al (2005) mention that the amount of SPs affects the life cycle maintenance cost of a complex end product. Syntetos et al (2009) claim that SPs’ management is an important challenging and complicated process, that is why categorizing SPs is crucial in order to increase attention to the most essential SPs, Van der Auweraer et al (2019) classify the SPs' characteristics: the first characteristic was demand patterns; intermittent demand, erratic and lumpy which makes it hard to present demand by the normal distribution, the second one is that SPs are different from end products which have independent demand, while SPs depend on the failure of parts or preventive maintenance procedures. The last one is that the production of SPs stays longer than the production of the end product itself.…”

“…The main issue that makes forecasting SPs demand a complex process is that SPs’ demand is intermittent, moreover, SPs’ demand is affected by many factors such as maintenance policy, the failure rate (FR) of a product, etc. Through the literature review of Van der auweraer et al . (2019), many authors propose forecasting SPs’ demand techniques relying on only historical data, they classify the information about SPs’ demand into three categories; SPs’ nature, maintenance guidelines and environmental factors.…”

“…Companies are improving after-sales services in order to increase the product life cycle and maintenance revenues, especially in the automotive industry (Lorentz et al , 2011). Van der auweraer et al . (2019) explains that the lack of SPs may affect the productivity of a product in terms of delay and high costs.…”

“…In addition, Syntetos et al (2009) claim that managing SPs is very important, challenging and complicated. Van der auweraer et al . (2019) categorize SPs according to demand patterns into intermittent demand, erratic and lumpy, while aftermarket parts are the parts manufactured by the original manufacturer and other companies, with different level of quality (Achetoui et al , 2019).…”

The Design of Forecasting System Used for Prediction of Electro-Motion Spare Parts Demands as an Improving Tool for an Enterprise Management

“…Need for collecting the preventive maintenance information in combination with the failure rate information to set up method which combines a broad set of demand drivers; 2. Combination of forecasting techniques can bring a possible research direction as well as further investigation of the combination of extrapolation techniques and causal methods [19].…”