Research Article

Demand Forecasting Method Proposal for a Packaging Industry at the Manaus Industrial Hub

Dan Ferreira Machado
Federal University of Amazonas, Brazil
Marcelo Albuquerque de Oliveira
Federal University of Amazonas, Brazil
* Corresponding author
Gabriela de Mattos Veroneze
Federal University of Amazonas, Brazil
DOI icon 10.24018/ejbmr.2021.6.6.1101

Read Counter
579

Downloads
699

Citations

Share

Article Sidebar

Submitted 2021-09-11
Published 2021-12-02

Read counter = 579 times

Table of Contents

Article Main Content

Every year, many companies hold meetings with their experts to prospect sales for the following year. Projections are often based on the team's experience and are subject to bias and errors. This article seeks to propose a statistical method of forecasting demand for a packaging company located in the Industrial Pole of Manaus, comparing performance metrics from different analyses. The results suggested that in general, the predictions had a good performance, but in certain cases, the need for a more human vision was also felt in order to ponder certain points. Therefore, the combination of quantitative and qualitative demand forecasting methods is essential.

Keywords: Demand forecast Packaging industry Time series

References

  1. Box, G. E. P., Jenkins, G. M., Reinsel, G. C., and Ljung, G. M. (2016). Times Series Analysis – Forecasting and Control. 5 Ed. New Jersey: Wiley, ISBN: 978-1-118-67502-1, 712 p.
     Google Scholar
  2. Bueno, R. L. S. (2011). Econometria de séries temporais. 2 Ed. São Paulo: Cengage Learning, ISBN-13: 978-8522111572, 360 p.
     Google Scholar
  3. Cecatto, C., and Belfiore, P. (2015). O uso de métodos de previsão de demanda nas indústrias alimentícias brasileiras. Gestão & Produção, 22(2):404-418.
     Google Scholar
  4. Dias, M. A. P. (1993). Administração de Materiais: Uma Abordagem Logística. 4 Ed. São Paulo: Atlas, ISBN: 9788522463909.
     Google Scholar
  5. Ferro, W. A., Lima, J. D., and Trentin, M. G. (2019). Combinações de Métodos Quantitativos na Previsão de Demanda de Vendas de Eletrodomésticos. GEPROS. Gestão da Produção, Operações e Sistemas, 14(5): 67-88.
     Google Scholar
  6. Gonçalves, P. S. (2020). Administração de Materiais. Editora‎ GEN Atlas; 6ª edição, ISBN-13: 978-8595151123, 504 p.
     Google Scholar
  7. Jain, C. L., and Malehorn, J. (2006). Benchmarking forecasting practices: a guide to improving forecasting performance. New York: Graceway Pub Co; 3rd edition, ISBN-13: 978-0932126870, 126 p.
     Google Scholar
  8. Kerkkänen, A., Korpela, J., and Huiskonen, J. (2009). Demand forecasting errors in industrial context: Measurement and impacts. International Journal of Production Economics, 118:43-48. https://doi.org/10.1016/j.ijpe.2008.08.008.
     Google Scholar
  9. Machado, W. C., Fonseca Júnior, E. S. (2013). Redes neurais artificiais aplicadas na previsão do VTEC no Brasil. Bol. Ciênc. Geod., Curitiba, 19(2):227-246.
     Google Scholar
  10. Makridakis, S., Wheelwright, S. C., and Hyndman, R. J. (1998). Forecasting - methods and applications. 3° Ed., John Wiley, New York, ISBN: 978-0-471-53233-0, 656 p.
     Google Scholar
  11. Mancuso, A. C. B. (2013). Uma investigação do desempenho de métodos de Combinação de Previsões: simulado e aplicado. Dissertação de Mestrado, UFRGS/PGEP, Porto Alegre.
     Google Scholar
  12. Morettin, P. A., Toloi, C. M. de C. (2004). Análise de séries temporais. Edgard Blucher, ISBN-13: 978-6555060041, 284 p.
     Google Scholar
  13. Pellegrini, F. R., and Fogliatto, F. S. (2001). Passos para implantação de sistemas de previsão de demanda: técnicas e estudo de caso. Produção, 11(1):43-64.
     Google Scholar
  14. Samohyl, R. W., Souza, G. P., and Miranda, R. G. (2008). Métodos simplificados de previsão empresarial. Rio de Janeiro: Ciencia Moderna; 1ª edição, ISBN-13: 978-8573936667, 192 p.
     Google Scholar
  15. Walter, O. M. F. C, Henning, E., Moro, G., and Samohy, R. W. (2013). Aplicação de um modelo SARIMA na previsão de vendas de motocicletas. Exacta – EP, 11(1):77-88.
     Google Scholar
  16. Wang, Fu-Kwun., and Chang, Ku-Kuang. (2010). Adaptive neuro-fuzzy inference system for combined forecasts in a panel manufacturer. Expert Systems with Applications, 37:8119-8126.
     Google Scholar