# Forecasting Steel Production in the World--Assessments Based on Shallow and Deep Neural Networks
## 6 Conclusions  
Production management is the process or activity by which resources flow within a defined system, and are grouped and transformed in a controlled manner to add value, according to the policies defined by a company.  
As presented in the literature review, steel plays an active role in human activities. Figure 7 shows the amount of steel produced annually around the world. Because steel is a finite and important natural resource, monitoring it is a key strategic issue for companies and others; hence the importance of forecasting so that decisions can be made in advance.  
A reliable forecasting method can be a key asset to anticipate good and bad periods, giving the management opportunity to react and take measures in time and possibly avoid losses.  
The present paper demonstrates the behavior of different predictive models, particularly the combination of convolutional layers for the MLP, LSTM, and GRU. The results show that each model presents a particularity regarding the delay windows and the advance windows.  
Table 4 shows that for the delay window equal to 4 and 12 the MLP model shows better accuracy. For the delay window equal to 6 and 8, it is the CNNMLP model that shows better accuracy. For the delay window equal to 10, it is the LSTM model that shows better accuracy, and for the delay window equal to 14, it is the CNNLSTM model that shows better accuracy.  
The results of the encoder-decoder model presented the best result, although it turned out that these results had a great influence on the input variables of the model. The results also show that it is possible to improve the model if information is added that correlates in some way with the variable being predicted. It should also be noted that much of this information is included in the variables' derivatives, so differencing can greatly reduce prediction errors.  
Future work includes experiments to combine other dependent variables, such as GDP growth, to improve predictions, as well as apply the same models to predict the production of other commodities.  
Conceptualization, J.T.F., A.J.M.C. and M.M.; methodology, J.T.F. and M.M.; software, B.C.M. and M.M.; validation, B.C.M. and M.M.; formal analysis, J.T.F. and M.M.; investigation, B.C.M. and M.M.; resources, J.T.F. and A.J.M.C.; writing--original draft preparation, B.C.M.; writing--review and editing, J.T.F., M.M., R.A. and L.M.d.C.; project administration, J.T.F. and A.J.M.C.; funding acquisition, J.T.F. and A.J.M.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the COFAC/EIGeS.  
The data that support the findings of this study are openly available in [World Steel website] at [[[https://www.worldsteel.org/steel-by-topic/statistics/steel-statistical-yearbook.html](https://www.worldsteel.org/steel-by-topic/statistics/steel-statistical-yearbook.html)]([https://www.worldsteel.org/steel-by-topic/statistics/steel-statistical-yearbook.html](https://www.worldsteel.org/steel-by-topic/statistics/steel-statistical-yearbook.html))].  
The authors thanks to COFAC by the funding and all support to the development of this research.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
AI Artificial Intelligence ANN Artificial Neural Networks CNN Convolution Neural Networks GRU Gated Recurrent Units