Proposal for an Improvement Model to Reduce Aircraft Spare Parts Overstock Based on ABC Methodology and Neural Network Forecasting in an Air Service Company in Peru

Ramirez X.S.; Garate Solorio X.; Paredes M.P.; Torres Sifuentes C. · Congreso Internacional de Innovacion y Tendencias en Ingenieria, CONIITI (2024) · DOI: 10.1109/coniiti64189.2024.10854866

Resumen

The problem of overstock of aircraft spare parts is a critical challenge faced by the aviation industry in Peru. Inefficient management of spare parts demand can lead to unnecessary costs and a lack of vital parts, jeopardizing operational safety. The importance of addressing this issue lies in ensuring safety and economic efficiency in the sector. Despite previous efforts by other authors in this field, the solution proposed in this article stands out for its innovative approach. As students of Business Management Engineering, our personal motivation lies in seeking practical solutions for real business challenges like the one presented. This article provides an approach that combines neural networks to forecast spare parts demand with the ABC methodology to ensure effective inventory classification. The main result of this successful implementation is a substantial improvement in the accuracy of demand forecasts. Additionally, the application of this technology and methodology in spare parts management is a fundamental step in addressing the overstock problem, enhancing operational efficiency and safety in the aviation industry.

Instituciones: Universidad Peruana de Ciencias Aplicadas