Civil Aircraft Spare Parts Prediction and Configuration Management Techniques: Review and Prospect

Abstract

Spare parts are treated as the basis to guarantee the safe and economic operation of civil aircraft, its scientific prediction and reasonable configuration play an important role in perfecting integrated logistics support (ILS) and achieving win-win situation of stakeholders (e.g. manufacturers, operators, maintenance providers). This paper studies the existing spare parts prediction and configuration methods of civil aircraft, and discusses future development trend of spare parts from the perspective of prediction and configuration, respectively. The current development status of civil aircraft spare parts prediction techniques are firstly introduced, according to demand characteristics of spare parts; the present research status of civil aircraft spare parts configuration methods are then elaborated, based on the traditional methods and machine learning methods; the development trend of civil aircraft spare parts prediction and configuration methods is finally analyzed, combined with the advantages and disadvantages of the existing methods of civil aircraft spare parts management methods. The efforts of this work provide a reference for the comprehensive management of civil aircraft spare parts and improve the perfection degree for the ILS of civil aircraft.

Spare parts management in industry 4.0 era: a literature review

“…The demand pattern of spare parts is generally not normally distributed due to its lumpy, erratic and slow-moving characteristics (Turrini and Meissner, 2019). The spare parts prediction uses time series, regression analysis, failure observation and machine learning methods (Feng et al., 2021). The engineering analysis with consumption pattern is an important factor for forecasting prediction of spare parts, hence forecasting strategy must integrate the decision making with non-smooth demand forecasting to achieve inventory cost benefits (Qian et al., 2017; Sahin et al., 2021).…”

“…Forecast accuracy of spare parts with fluctuating demands may be enhanced with data-driven AI-based Neural Network forecasting, whereas bootstrap forecasting method has given promising results in aviation (Baisariyev et al., 2021). Spare parts prediction may use: Time series method for demands with small changes in consumption, System affected by multiple factors may adopt Regression analysis method, new system is recommended for failure observation method and non-linear systems by machine learning methods (Feng et al., 2021). Table 3 shows the analysis of selected articles based on considered types of spares.…”