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The present paper proposes a new Hybrid clustering Process based on Fuzzy Genetic System. The proposed Approach consists of two steps: (1) Using a method called Fuzzy clustering, all data elements will be clustered into N groups; (2) utilizing a Fuzzy Genetic System, for every level the fuzzy rule of adhesion will be generated. If we compare our research to others that use the hard clustering, we will conclude that by using the fuzzy clustering we are able to raise the ingredient of each cluster and upgrade the accuracy of the offer target system and we will win in terms of complexity because the system is based on hybrid intelligent method and then we will not need to generate a new cluster every time we add a new data point. Experimental results on estimation models using clustering methods on synthetic data show that the proposed algorithm outperforms few commonly used clustering algorithms.

To enhance the competitive advantage in a constantly changing environment, the manager of a company must make the right decision at the right time based on the information at hand. The Enterprise Resource Planning System (ERP) integrates the management of internal and external information across the organization (finance / accounting, manufacturing, sales and service, customer relationship management, etc.). How to use the information resources of the ERP and how to exercise effective information resources are currently pressing issues. This research proposes an intelligent hybrid sales forecasting system based on Fuzzy Delphi Method, fuzzy clustering and Back-propagation (BP) Neural Networks with adaptive learning rate in ERP architecture (Delphi-FCBPN-ERP). We utilize SPC (Scientific Private Cloud) was to reduce the time computation of the proposed model. This cloud computing platform will allow improved the execution time of parallel neural networks proposed in our model. Experimental results show that the proposed approach is superior then the traditional approaches.

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