# Demand Prediction Using a Soft-Computing Approach: A Case Study of Automotive Industry
## 5 Discussion and Conclusions  
Managing the supply chain is a complex activity and maintains great relevance. The flow products control in organizations are complex because the demand for products is not predictable in its entirety. This generates conflict in the supply, production, inventories, and distribution levels [1]. The new technologies allow contributing in the administrative processes in the organizations, making it possible to analyze robust information sets through the soft-computing systems. There are many computer systems that support decision-making and these allow the development and demand planning with greater certainty as enterprise resource planning (ERP) and material requirements planning (MRP) and customer relationship management (CRM) [69; 70]. It is important to mention that these systems do not guarantee the bias elimination in demand forecast because they are people the administer them and the information systems not always information generated adequate. Forecast of demand is an activity it is supported in computer systems and this do not always adhere to the dynamic reality of the market and its sudden fluctuations [38]. Therefore people experience becomes relevant because they have a perception from another prospective. it can identify variations with suppliers or customers in advance or by implicit signals. These signals can be used in order to generate more efficient planning. Derived from the biases generated when making decisions based on numerical data are that it is decided to propose a methodology that integrates expert judgements, fuzzy logic, and artificial neural network of time series [71]. Some reasons why proposed this methodology:  
* This methodology is possible to identify variables that are not reflected in the planning and supply chain management. * Integrate expert judgements through tools that presently offer advantages to developing new planning and prediction strategies by analyzing variables. * Planning is one of the activities that help to control the products flow in an appropriate way, mitigating the error and the losses in order to minimize the risk. * The uncertainty on markets the requires new strategies to be efficient in the demand planning process through new artificial intelligence technologies. Planning process is one of the activities with greater relevance, through the proposed methodology is contemplated the integration the qualitative and quantitative variables contributing in decision making in the dynamic markets and it allows to have better reaction to changes and increase visibility in the supply chain. Some considerations that must be taken in the collection information. In other words, the validated data and the applied questionnaires must be evaluated. This is achieved through an adequate analysis by applying statistical measures. Also, in the process of fuzzy logic applied, fuzzy rules construction, operator type and membership function as an essential part of the study should be considered in principle. In other words, use the functions that recognize study properties. In addition, the artificial neural network must consider the percentages of training, test, validation. For future work, it is expected to apply the Sugeno method and the adaptive neuro fuzzy inference system (ANFIS) to assess performance by comparing the methods applied in different real scenarios. Conceptualization and methodology, T.E.S.-F. J.A.S.-M and J.M.V.-H; data curation and validation, J.A.S.-M and R.R.-A; writing--original draft preparation, T.E.S.-F., J.A.S.-M and J.M.V.-H.; writing--review and editing, J.A.S.-M., R.R.-A. and T.E.S.-F; All authors read and approved the submitted \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Performance models**} \\ \hline Modelo & MAD & MAPE & MSE \\ NARX & 17,661 & 8 & 561,742,971 \\ NAR & 16,446 & 8 & 584,297,989 \\ Holt & 23,359 & 12 & 996,544,938 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparative results. manuscript and agreed to be listed as authors.