Modeling Spare Parts Demands Forecast under Two-Dimensional Preventive Maintenance Policy

Abstract

In maintenance practice, there is such a situation where the spare parts replacement should be carried out at the scheduling time of calendar or usage for whichever comes first. The issue of two-dimensional preventive maintenance usually was not addressed by traditional methods, and at present, few studies were focused on this very topic. Based on these considerations, this paper presented the two-dimensional preventive policy where replacements of spare parts are based on both calendar time and usage time. A novel model was developed to forecast spare parts demands under two-dimensional preventive maintenance policy, and a discrete algorithm was presented for solving the mathematical model. A case study was given to demonstrate its applicability and validity, and it was showed that the presented model can be used to forecast spare parts demands as well as to optimize spare parts and preventive maintenance jointly.

Forecasting spare part demand with installed base information: A review

“…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…”

“…Results / comparison Ritchie & Wilcox (1977) Case study Good graphical fit with real demand Fortuin (1984) Case study 25% stock reduction / current policy Ghodrati & Kumar (2005b) Case study Difference in forecasted demand of 20% / covariates not included Ghodrati & Kumar (2005a) Case study Difference in forecasted demand of 40% / covariates not included Deshpande et al (2006) Empirical study Average inventory cost reduction of 20% / exogenous lead time demand Ghodrati et al (2007) Case study Greater economical/production losses for noninclusion of covariates Hua et al (2007) Case study Lower error ratio, percentage error / SES, CR, bootstrapping Hong et al (2008) Graphical analysis Better fit with real demand / Ritchie & Wilcox (1977) Jin & Liao (2009 Numerical example Inventory control system is sensitive to time-tofailure distribution Ghodrati (2011) Sensitivity analysis Exp. time-to-failure distribution more affected by covariates than Weibull Jalil et al 2011Case study Cost improvement of 1-16% for small and 1-58% for large installed base / current policy Minner 2011Simulation Average inventory reduction of 50% / SES Wang & Syntetos 2011Simulation Reduced MAD / SBA Barabadi (2012) Case study Influence of time-dependence of covariates on spare part demand Ghodrati et al (2012) Implementation Less downtime and increased efficiency when considering covariates Jin & Tian (2012) Simulation Validation of the method Romeijnders et al (2012) Case study MSE and MAD reduced up to 20% / CR, SES, Moving Average (MA), TSB Hong & Meeker (2013) Simulation Reduced MSE when applying use-rate data Barabadi et al (2014) Case study hazard rate up to 1.8 times higher in different operating conditions Hellingrath & Cordes (2014) Case study Reduced forecast error / SBA Chou et al (2015) Case study MAD improves 16% / other regression models Hu et al (2015) Case study Influence of usage rates on spare part demand Kontrec et al (2015) Case study No validation/evaluation Lu & Wang (2015) Simulation No significant difference / simulated demand data Chou et al (2016) Case study MAD improvements of ...…”

“…Classification of the scientific work considering preventive maintenance the occurrence of planned maintenance actions Hu et al (2015). consider a two-dimensional preventive maintenance cycle, where preventive replacement scheduling is dependent on both calendar time and usage time (e.g., mileage, flight hours, etc.…”

Spare parts classification in industrial manufacturing using the dominance-based rough set approach

“…Spare parts are common inventory stock items that are required for timely maintenance of industrial plant systems. A recent study [51] shows that the operational and maintenance support costs in a typical industrial plant account for more than 60% of the overall cost, where the spare parts related costs alone account for about 25% to 30%. This clearly indicates that better operations management of spare parts is required and has an important role in the availability of the plant at an optimal cost.…”

Forecasting the spare part demands for mobile phones

“…With its help, we can achieve not only the on-time transportations but also lower warehousing costs. Hu [7] presented a two-dimensional preventive policy where replacements of objects were determined based on both calendar and usage times. Besides the common short-term prediction, Mei [8] proposed a method for long-term volatility predictions.…”