Spare Parts Forecasting Based on Reliability

Abstract

Stochastic models for spare parts forecasting have not been widely researched in scientific literature from the aspect of their reliability. In this chapter, the authors present models which analyze standard reliability parameters of technical systems' parts/components. By analyzing system reliability and failure rate, we estimate the required number of spare parts in the moment of expected failure or when reliability falls below the predefined level. Two different approaches based on data availability are presented herewith.

Forecasting spare part demand with installed base information: A review

“…The author determines the probability distribution, instead of a point forecast, for the one-period-ahead demand by means of recursion. Kontrec et al (2015) and Kontrec & Stefan (2017) propose a reliability model where they observe the total unit time, i.e., the average life span of a part, and use it to estimate the number of parts to be kept in stock. Stormi et al (2018) propose a regression model which only considers the size of the installed base as the driver for demand, and ignore the failure behavior of the parts.…”

“…x x x Deshpande et al (2006) x x x Hua et al (2007) (H) Hong et al (2008) x 2012x x Lanza et al (2009) x x x x Jalil et al 2011x x (H) x Minner 2011x x x Wang & Syntetos (2011) x x x Barabadi (2012) x x Romeijnders et al 2012(H) Hong & Meeker (2013) x x x Barabadi et al (2014) x x x x Hellingrath & Cordes (2014) x x x Chou et al (2015) x x x x x Hu et al (2015) x x x Kontrec et al (2015) x x x Lu & Wang (2015) x x x x Gharahasanlou et al (2016) x x x x Chou et al (2016) x Kim et al (2017) x x x x Kontrec & Stefan (2017) x x x Qarahasanlou et al (2017) x x x x Si et al (2017) x Stormi et al (2018) x x x (H) Fortuin (1984) Linear growth function Yamashina (1989) Time dependent sales rate / Lump production Jin & Liao (2009) Homogeneous Poisson Process Minner 2011Logistic growth function Jin & Tian (2012) Homogeneous Poisson Process Liu & Tang (2016) Deterministic Table 7: Assumptions on the distribution of the new sales…”

“…Kim et al (2017) (1) Long estimation period during production phase necessary, (2) preferably short EOL phase, (3) part deterioration depend on product age. Kontrec & Stefan (2017) No validation/evaluation of the method performance. Qarahasanlou et al (2017) Definition and estimation of covariates necessary.…”

“…Minner (2011);Perminova-Harikoski et al (2015);Kontrec & Stefan (2017);Si et al (2017) Paper in peerreviewed journalShaunty & Hare Jr (1960);Ritchie & Wilcox (1977);Fortuin (1984); Yamashina (1989);Petrovic & Petrovic (1992); Auramo & Ala-risku (2005); Ghodrati & Kumar (2005b,a); Deshpande et al (2006); Ghodrati et al (2007); Hua et al (2007); Cavalieri et al (2008); Hong et al (2008); Wagner & Lindemann (2008); Jin & Liao (2009); Lanza et al (2009); Jalil et al (2011); Wang & Syntetos (2011); Barabadi (2012); Ghodrati et al (2012); Jin & Tian (2012); Romeijnders et al (2012); Dekker et al (2013); Hong & Meeker (2013); Barabadi et al (2014); Hellingrath & Cordes (2014); Chou et al (2015); Hu et al (2015); Kontrec et al (2015); Lu & Wang (2015); Chou et al (2016); Gharahasanlou et al (2016); Liu & Tang (2016); Lu & Hjelle (2016); Hu et al (2017); Kim et al (2017); Qarahasanlou et al (2017); Andersson & Jonsson (2018); Stormi et al (2018)…”

Application of Statistical Distribution Models to Predict Health Index for Condition-Based Management of Transformers

“…Weibull distribution has been widely used for industrial applications, especially in the field of reliability engineering. It is normally used in the reliability assessment, such as the derivation of reliability indices mean time to failure (MTTF) [25], equipment's failure rate [26][27][28], remaining useful life prediction [29,30], spare parts replacement, and maintenance/replacement strategies [31,32]. It is a versatile distribution with two critical parameters, which can also describe the characteristics of other types of distributions.…”