Prediction of the development cost of commercial aviation aircraft

Abstract

Purpose The purpose of this paper is to develop a flexible design-oriented development cost method for commercial aviation aircraft based on small sample and poor information. Design/methodology/approach To predict the development cost of commercial aviation aircraft accurately, the methodology is based on the collected cost data and actual technical, and then the cost prediction relationships derived from an exhaustive statistical and filtered from regression analysis are incorporated. A series of regression equations with high regression coefficient are yielded after the cost driving factors of the development cost are fixed. Next, several sets of equations with high regression coefficient are selected for final integration. It is a flexible method that can be used efficiently to predict the cost of commercial aviation aircraft. Findings The development of commercial aviation aircraft has relatively a late start and no cost prediction model has been suitable for small sample, the proposed method is expected and is rather desirable. Practical implications By comparing the approach with the ordinary regression model and back propagation (BP) neural network, the scheme in this work is more efficient and convenient. Originality/value The results obtained in this paper show that the proposed method not only has a certain degree of versatility, but also can provide a preliminary prediction of the development cost of commercial aviation aircraft.

Prediction of the maintenance cost of general aviation aircraft based on engineering method

“…In general, when RA < 30%, the prediction results of the model are proved to be relatively accurate (Chen et al , 2019a, 2019b). Using the engineering method, the RA of the maintenance cost was found to be 13%–20%, which confirmed the validity of this study.…”

“…Compared with commercial aircraft, general aviation aircraft has attracted extensive attention because of its high degree of marketization, flexible flight mode, low price, convenient maintenance and other advantages (Chen et al, 2019a(Chen et al, , 2019bRasuo and Duknic, 2013;Wu et al, 2004). With the wide application of general aviation aircraft, its economy has been paid more and more attention by aircraft operators and manufacturers (Chen et al, 2019a(Chen et al, , 2019bHudda et al, 2022;Ma and Wang, 2022;Rogalski, 2008). For operators, choosing a reasonably priced aircraft with fewer late-stage failures is a great way to manage late-stage operations.…”

“…Since then, Boeing and Airbus proposed their own maintenance cost estimation methods in 1971 and 1988, respectively. The European Aeronautical Association also established AEA aircraft maintenance cost prediction model (Chen et al, 2019a(Chen et al, , 2019b in 1989. NASA, in conjunction with a number of American airlines, carried out in-depth research on aircraft and proposed the NASA series maintenance cost prediction methods (NASA, 1978) in 1995.…”

“…At present, most of the maintenance cost prediction research is carried out for military and civil aircraft (Castagne et al, 2008;Chen et al, 2019aChen et al, , 2019bJohnson, 1990;Ross and Crossley, 2000). The cost prediction method for aircraft mainly includes grey correlation analysis (Chen et al, 2021;Hu et al, 2015), multiple linear regression (Chen et al, 2019a(Chen et al, , 2019bScanlan et al, 2002;Zhang et al, 2014), BP neural network (Li et al, 2012;Sun and Li, 2012;Wu and Yuan, 2011;Zhou and Zheng, 2007), engineering estimation (Sun et al, 2018), support vector machine (Ben-Arieh, 2000;Wang et al, 2014) and genetic algorithm (Yang and Yang, 2012). Large international aviation enterprises usually use parameter method to predict the maintenance cost of civil and military aircraft and express the maintenance cost by the performance parameters of some aircraft and engines.…”