Analysis of the Application of Different Forecasting Methods for Time Series in the Context of the Aeronautical Industry †

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, due to its restricted nature, 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, which play a crucial role in the operations of any company and are a determining factor in decision making. The objective of this research is to compare the effectiveness of various time-series forecasting methods, including classical statistical methods and neural networks. 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 proposed forecasting methods are applied, and the results are compared using three different evaluation metrics. The objective is to emphasize the significance of optimizing strategic planning within the industry and the potential savings that can be achieved by selecting the best forecast. In conclusion, the findings of this study can be used to enhance the efficiency of the supply chain and operations of companies in the aeronautical sector.