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

Authors: Antônio Augusto Rodrigues de Camargo; Mauri Aparecido de Oliveira

Addresses: Department of Management and Decision Support, Aeronautics Institute of Technology–ITA, Praça Marechal Eduardo Gomes 50, Vila das Acácias 12228-900, São José dos Campos, São Paulo, Brazil; Federal University of São Paulo – UNIFESP, Cesare Monsueto Giulio Lattes 1201, Eugênio de Melo 12247-014, São José dos Campos, São Paulo, Brazil ' Institute of Science and Technology, Aeronautics Institute of Technology–ITA, Praça Marechal Eduardo Gomes 50, Vila das Acácias 12228-900, São José dos Campos, São Paulo, Brazil

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.

DOI: 10.1504/IJBFMI.2024.139347

International Journal of Business Forecasting and Marketing Intelligence, 2024 Vol.9 No.3, pp.300 - 317

Received: 24 May 2023
Accepted: 29 May 2023
Published online: 01 Jul 2024 *

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