Özet :

Demand forecasting represents an important part of production planning because it can estimate the future demand of products and services and the amount of resources that needs to be allocated in order to accomplish that demand. As the demands can vary as the times passes, the production plan must be able to face those variations. Demand estimation methods are classified under two main headings: quantitative and qualitative. The quantitative estimation method is a method of estimating the basis of knowledge of people's own experiences. The qualitative method is the method of estimating the numerical data based on the results obtained by supporting the mathematical modeling. Artificial neural network model is among quantitative estimation methods. Therefore, it may be appropriate to use methods and algorithms such as machine learning methods, especially support vector machine, nearest n-neighbor, regression and artificial neural networks and Bayesian networks. In this paper, we focus on the mining of the time series formed by all the past results using an artificial neural network-based simulation system that is able to identify an appropriate production forecast. The results of the production simulations are used as historical data in order to forecast the future demands and the amount of time needed to satisfy them. The time series forecast results show that data mining can be used in this domain in order to extract patterns that can be used to optimize the production process.

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