# Demand Prediction Using a Soft-Computing Approach: A Case Study of Automotive Industry
## 4 Application
### Fuzzy Analysis  
The [PERSON] method application contemplate the implementation AND and OR operator, therefore the antecedent's relation both in whole is evaluated. These are also known as independent variables.  
Fuzzy logic aims to establish dependency relationships between variables, this tool can implement an indefinite variable set. Also, the attributes of each set, the coherence and the behavior. In addition to this activity, They are done through Matlab 2015 in its Fuzzy Logic Designer Toolbox application. it is chosen for development and implementation activities.  
An important part of the fuzzy process is the construction of rules. These should reflect the expert's opinions, through this process it is possible to develop a decision analysis of demand, as it's shown in the following fuzzy rules [66].  
* Interest-rate is A and GDP is B and inflation is C and USA/MXN is D then is Z.
* Interest-rate is A or GDP is B or inflation is C or USA/MXN is D then is Z.  
The fuzzy mechanism construction is used in the fuzzy logic designer toolbox application using the Matlab software. This includes four variables such as antecedents and demand consequently. This study has the capacity to add four or more antecedents that affect the demand, as shown in the Figure 2. It is also important to mention that the purpose of the rules is evaluating four antecedents and generate one consequent.  
In addition, fuzzy mechanism elaboration contemplated the rules construction that integrate each one of the selected variables, the membership function is chosen that reflects the demand behavior and reflects its decision boundaries and the triangular membership function is chosen.  
The membership functions construction contemplates the discussion universe values, in other words, maximum and minimum levels, besides this may be integrated by a membership functions set and may have specific characteristics, as show in the Figure 3.  
The decision behavior can be represented in a clear graphic way. The three coordinates choices represent the demand level, it evaluates the demand projections without the need to carry out the  
Figure 3: Fuzzy construction.  
Figure 2: Fuzzy mechanism.  
Delphi methodology once that the fuzzy mechanism itself is carried out, which already contains the expert's perception.  
Once the study of weighted variables has been done, numerical values can be obtained through an established convergence of all experts. Through this tool, the creation of projections from the opinion is simplified. Therefore, people who work in a planning area can integrate more information more easily. The system allows to evaluate fuzzy relationships when the behavior of the variables increases or decreases, as shown in the Figure 4, and this allows to forecast demand values from the fuzzy values, as shown in the Table 1.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Input** & & & & & **Output** \\ \hline
**Date** & **Interest rate** & **GDP** & **Inflation** & **USA/MXN** & **Demand** \\ \hline
2018/01 & 13.15 & 1.2 & 5.55 & 18.60 & 205,000 \\
2018/02 & 13.15 & 1.2 & 5.34 & 18.84 & 202,000 \\
2018/03 & 13.  
This research integrates more than a qualitative data variable, in other words, they are represented as factors that influence the demand for behavior. Through the Matlab 2015 Toolbox application and the use of neural net time series 2.2.21, both types of information can be integrated; expert judgements and historical data. The sales projection for a future period is generated by integrating the market behavior perceived by the experts.