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.
Highlights
Spare parts are one of the material foundations for integrated logistics support (ILS), the scientific prediction and reasonable configuration of spare parts is essential for guaranteeing the safe and economic operation of civil aircraft
The aim of this paper is to investigate spare parts prediction and configuration methods, analyze the advantages and disadvantages of existing theories and methods in the comprehensive management of spare parts and summarize its development trends, with a view to provide support for the improvement of the competitiveness of civil aviation industry
Babajanivalashed et al.[78] proposed a methodology to select the best prediction method based on binary classifier machine learning, the results indicated that neural network is the best method for 98% of demand compared with the performance of random forest
Summary
Spare parts are one of the material foundations for integrated logistics support (ILS), the scientific prediction and reasonable configuration of spare parts is essential for guaranteeing the safe and economic operation of civil aircraft. The utilization rate and turnover rate of most civil aircraft spare parts are extremely depressed, only 25% are used, and there is a problem of excessive backlog.[4,5] And if the prediction and configuration strategy is unreasonable, it will lead to lack and untimely support, and cause flight delays or aircraft on ground (AOG). To solve the existing problems and ensure a win-win situation for stakeholders, it is currently the focus and hotspot of the integrated management of civil aircraft spare parts
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