Analysis of the application of different forecasting methods for time series in the context of the aeronautical industry

Antônio Augusto Rodrigues de Camargo and Mauri Aparecido de Oliveira

International Journal of Business Forecasting and Marketing Intelligence, 2024, vol. 9, issue 3, 300-317

Abstract: The aeronautical sector is a vital part of the Brazilian industrial landscape, contributing to the development of new technologies and production techniques with potential applications in other industries. However, there are limited studies on implementing improvements in its systems, highlighting the need for attention in specific subareas of companies in this sector. One such area is the production-planning department, especially the forecasting techniques applied in the supply chain. The objective is to compare the effectiveness of various time series forecasting methods, including classical statistical methods and neural networks (NN) using three different evaluation metrics. The study employs a real-time series that depicts the consumption of a specific material extensively used in the production line of a major Brazilian aircraft manufacturer. The purpose is to emphasise the significance of optimising strategic planning within the aeronautical sector and the potential savings that can be achieved by selecting the best forecast.

Keywords: forecasting; time series analysis; aeronautical industry; supply chain; statistical methods; neural networks; NN; operations research; Kanban; simple exponential smoothing; forecast accuracy. (search for similar items in EconPapers)
Date: 2024
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