Method of Spare Parts Prediction Models Evaluation Based on Grey Comprehensive Correlation Degree and Association Rules Mining: A Case Study in Aviation

Abstract

Probability of spare parts sufficiency is crucial in the process of the normal operation of businesses, especially for the airline company. However, higher support sufficiency could inevitably lead to the increase of inventory cost of spare parts and restrict a company’s efficiency. Therefore, it is important for businesses to reduce material cost on the premise of normal operation in order to accurately predict spare parts requirements based on reasonable models. The purpose of this paper is to solve problems with the evaluation of spare parts prediction models and to improve efficiency of company. Firstly, this paper summarizes a series of prediction models of spare parts requirements and applies the grey comprehensive correlation degree to rank the models. Secondly, the method of association rules mining is used to discover the association relationships between the types of spare parts and the prediction models. Finally, a case study in aviation is given to demonstrate the feasibility of the methodology, and optimal prediction models are recommended for aircraft spare parts. In accordance with the association relationships, the applicable prediction model can be provided in terms of different types of spare parts. This model will greatly enhance the work efficiency of spare parts prediction and improve the prediction tasks for the aircraft companies.