relation: http://repositorio.unini.edu.mx/id/eprint/6976/ canonical: http://repositorio.unini.edu.mx/id/eprint/6976/ title: Spare Parts Forecasting and Lumpiness Classification Using Neural Network Model and Its Impact on Aviation Safety creator: Shafi, Imran creator: Sohail, Amir creator: Ahmad, Jamil creator: Martínez Espinosa, Julio César creator: Dzul Lopez, Luis Alonso creator: Bautista Thompson, Ernesto creator: Ashraf, Imran subject: Ingeniería description: 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. date: 2023 type: Artículo type: PeerReviewed format: text language: en rights: cc_by_4 identifier: http://repositorio.unini.edu.mx/id/eprint/6976/1/applsci-13-05475-v2.pdf identifier: Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica Fundación Universitaria Internacional de Colombia > Investigación > Producción Científica Universidad Internacional Iberoamericana México > Investigación > Producción Científica Universidad Internacional Iberoamericana Puerto Rico > Investigación > Producción Científica Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés 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. metadata Shafi, Imran; Sohail, Amir; Ahmad, Jamil; Martínez Espinosa, Julio César; Dzul Lopez, Luis Alonso; Bautista Thompson, Ernesto y Ashraf, Imran mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, luis.dzul@unini.edu.mx, ernesto.bautista@unini.edu.mx, SIN ESPECIFICAR (2023) Spare Parts Forecasting and Lumpiness Classification Using Neural Network Model and Its Impact on Aviation Safety. Applied Sciences, 13 (9). p. 5475. ISSN 2076-3417 relation: http://doi.org/10.3390/app13095475 relation: doi:10.3390/app13095475 language: en