# Demand Prediction Using a Soft-Computing Approach: A Case Study of Automotive Industry
## 1 Introduction  
The definition of supply chain is used to include activities the flow of goods from the suppliers to the final consumer [1]. There are many factors affect supply chain performance [2]. Some of the most important factors that affect the companies performance are the decisions made based on the planning and the demand forecasting process, because they are processes in which various areas of the companies are based on [3].  
This document focuses on the conflicts that arise from suppliers to consumers when incorrect sales projections are generated. That is, to avoid a poor forecast, planners need to integrate activities related to transport, transformation and distribution of goods through good collaboration in the supply chain [4].  
One of the effects of poor planning is the well-known whip effect that distorts the flow of materials along the supply chain channel [5]. This is reflected in the excessive supplying and obsolescing therefore the costs of each of the participating organizations increase.  
Therefore, collaboration between the different areas of the company is an indispensable part for proper business administration, which results in effective decision making. However, a bias is generated when only one variable is considered in the preparation of the forecast, for example usingonly sales data. Because of this, an inadequate demand projection arises resulting in additional costs and operational inefficiencies [6].  
Today, demand forecasting is a planning activity that includes new analytical tools that add to the known traditional methods. Some of the traditional methods are moving averages, weighted average, smoothing and exponential regression and other novel approaches such as Walsh--Fourier techniques [7]. The methods mentioned above are added techniques derived from the principles of artificial intelligence such as artificial neural networks, fuzzy logic, big data, among other new mathematical techniques. [3,8]. On the other hand, there are also qualitative techniques that consist of the empirical interpretation of future sales. Finally, current managers make decisions taking into account more than one methodology. Concluding that by applying two or more tools for decision-making it would be possible to increase efficiency in the planning process [9].  
In addition, it is important to mention that the judgement issued by the experts discriminate against any type of mathematical method in the qualitative field, this allows generat projections that are adjusted according to intuition and evaluated in the same way [10]. This type of projection has the ability to generate high levels of certainty in the planning process because the expert requires better results than those obtained only with mathematical tools.  
In summary, traditional techniques combined with new techniques become increasingly important in modern decision-making processes. Therefore, the use of mathematical and statistical models combined with tools that allow the integration of historical sales data with other relevant information. With this, sales projections can be generated for future periods, reducing forecast errors and generating more efficient decision and operational processes [11].  
In this research, a fuzzy inference system is used to forecast the demand for light vehicles in the automotive industry. This model incorporates environmental factors that change the level of sales as input and demand as output. This paper is organized as follows: Section 2 presents a literature review of statistics and artificial intelligence techniques for forecasting in the supply chain. Section 3 explains the proposed methodology that integrates different techniques in the demand projection model based on the fuzzy inference system. Section 4 describes the application of the model in the industrial automotive industry. Finally, Section 5 presents the conclusion.