Analysis of the application of different forecasting methods for time series in the context of the aeronautical industry
by Antônio Augusto Rodrigues de Camargo; Mauri Aparecido de Oliveira
International Journal of Business Forecasting and Marketing Intelligence (IJBFMI), Vol. 9, No. 3, 2024

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.

Online publication date: Mon, 01-Jul-2024

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Business Forecasting and Marketing Intelligence (IJBFMI):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com