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- Title
Spare Parts Forecasting and Lumpiness Classification Using Neural Network Model and Its Impact on Aviation Safety.
- Authors
Shafi, Imran; Sohail, Amir; Ahmad, Jamil; Espinosa, Julio César Martínez; López, Luis Alonso Dzul; Thompson, Ernesto Bautista; Ashraf, Imran
- Abstract
Safety critical spare parts hold special importance for aviation organizations. However, accurate forecasting of such parts becomes challenging when the data are lumpy or intermittent. This research paper proposes an artificial neural network (ANN) model that is able to observe the recent trends of error surface and responds efficiently to the local gradient for precise spare prediction results marked by lumpiness. Introduction of the momentum term allows the proposed ANN model to ignore small variations in the error surface and to behave like a low-pass filter and thus to avoid local minima. Using the whole collection of aviation spare parts having the highest demand activity, an ANN model is built to predict the failure of aircraft installed parts. The proposed model is first optimized for its topology and is later trained and validated with known historical demand datasets. The testing phase includes introducing input vector comprising influential factors that dictate sporadic demand. The proposed approach is found to provide superior results due to its simple architecture and fast converging training algorithm once evaluated against some other state-of-the-art models from the literature using related benchmark performance criteria. The experimental results demonstrate the effectiveness of the proposed approach. The accurate prediction of the cost-heavy and critical spare parts is expected to result in huge cost savings, reduce downtime, and improve the operational readiness of drones, fixed wing aircraft and helicopters. This also resolves the dead inventory issue as a result of wrong demands of fast moving spares due to human error.
- Subjects
SPARE parts; AIRPLANES; AERONAUTICAL safety measures; BUILDING failures; FORECASTING; HUMAN error; TRANSSHIPMENT
- Publication
Applied Sciences (2076-3417), 2023, Vol 13, Issue 9, p5475
- ISSN
2076-3417
- Publication type
Article
- DOI
10.3390/app13095475