Integration of Fuzzy Delphi, Fuzzy Clustering and Back-Propagation Neural Networks with Adaptive Learning Rate for Sales Forecasting in ERP Architecture


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Abstract


Sales forecasting, which has been investigated by various researchers, is a very complicated environment. Control and evaluation of future sales still seem concerned both researchers and policy makers and managers of companies. In recent years, there has been a strong tendency by companies to use centralized management systems like Enterprise resource planning (ERP). ERP systems offer a comprehensive and simplified process managements and extensive functional coverage. Sales management module is an important element business management of ERP. this research propose 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). An example based on an industrial company that manufactures packaging is used to evaluate the proposed intelligent system. Experimental results show that the proposed approach is superior then the traditional approaches
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Keywords


Fuzzy Delphi; Enterprise Resource Planning; Sales Forecasting; Fuzzy Clustering; Fuzzy System; Back Propagation Network; Hybrid Intelligence Approach

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References


Bezdek J. C. (1981) : "Pattern Recognition with Fuzzy Objective Function Algoritms", Plenum Press, New York.
http://dx.doi.org/10.1007/978-1-4757-0450-1_3

Casillas, J., Cordón, O., Herrera, F., & Villar, P. (2004). A hybrid learning process for the knowledge base of a fuzzy rule-based system. In Proceedings of the 2004 international conference on information processing and management of uncertainty in knowledge-based systems, Perugia, Italy (pp. 2189 2196).

Chang, P.-C, & Lai, C.-Y. (2005). A hybrid system combining self-organizing maps with case-based reasoning in wholesalerbs new-release book forecasting. Expert Systems with Applications, 29(1),183 192.
http://dx.doi.org/10.1016/j.eswa.2005.01.018

Chang, P.-C, & Liu, C.-H. (2008). A TSK type fuzzy rule based system for stock price prediction. Expert Systems with Applications, 34, 135 144.
http://dx.doi.org/10.1016/j.eswa.2006.08.020

Chang, P.-C, Liu, C.-H., & Fan, C. Y. (2009). Data clustering and fuzzy neural network for sales forecasting : A case study in printed circuit board industry. Knowledge Based Systems, 22(5), 344 355.
http://dx.doi.org/10.1016/j.knosys.2009.02.005

Chang, P.-C, Liu, C.-H., & Wang, Y.-W. (2006). A hybrid model by clustering and evolving fuzzy rules for sales decision supports in printed circuit board industry. Decision Support Systems, 42(3), 1254 1269.
http://dx.doi.org/10.1016/j.dss.2005.10.013

Chang, P.-C, & Wang, Y.-W. (2006). Fuzzy Delphi and back-propagation model for sales forecasting in PCB industry. Expert Systems with Applications, 30(4).
http://dx.doi.org/10.1016/j.eswa.2005.07.031

Chang, P.-C, Wang, Y.-W., & Liu, C.-H. (2007). The development of a weighted evolving fuzzy neural network for PCB sales forecasting. Expert Systems with Applications, 32(1),86 96.
http://dx.doi.org/10.1016/j.eswa.2005.11.021

Chang, P.-C, Wang, Y.-W., & Tsai, C.-Y. (2005). Evolving neural network for printed circuit board sales forecasting. Expert Systems with Applications, 29,83 92.
http://dx.doi.org/10.1016/j.eswa.2005.01.012

Chang, P.-C , Yen-Wen Wang, Chen-Hao Liu(2007). The development of a weighted evolving fuzzy neural network for PCB sales forecasting. Expert Systems with Applications, 32(1),86 96.
http://dx.doi.org/10.1016/j.eswa.2005.11.021

Chang PeiChann , Wang Di-di , Zhou Changle (2011). A novel model by evolving partially connected neural network for stock price trend forecasting. Expert Systems with Applications, Volume 39 Issue 1, January, 2012 .
http://dx.doi.org/10.1016/j.eswa.2011.07.051

Cordón, O., & Herrera, F. (1997). A three-stage evolutionary process for learning descriptive and approximate fuzzy logic controller knowledge bases from examples. International Journal of Approximate Reasoning, 17(4),369407.
http://dx.doi.org/10.1016/s0888-613x(96)00133-8

Cordón, O., Herrera, F., Hoffmann, F., & Magdalena, L. (2001). Genetic fuzzy systems : Evolutionary tuning and learning of fuzzy knowledge bases. Singapore : World Scientific.
http://dx.doi.org/10.1142/4177

Dunn J. C. (1973) : "A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters", Journal of Cybernetics 3 : 32 57.
http://dx.doi.org/10.1080/01969727308546046

Eberhart, R. C., and Kennedy, J. (1995). A new optimizer using particle swarm theory. Proceedings of the Sixth International Symposium on Micro Machine and Human Science, Nagoya, Japan, 39 43. Piscataway, NJ : IEEE Service Center.
http://dx.doi.org/10.1109/mhs.1995.494215

Efendigil, T., Önü, S., & Kahraman, C. (2009). A decision support system for demand forecasting with artificial neural networks and neuro-fuzzy models : A comparative analysis. Expert Systems with Applications, 36(3),6697 6707.
http://dx.doi.org/10.1016/j.eswa.2008.08.058

Esmin, A (2007).Generating Fuzzy Rules from Examples Using the Particle Swarm Optimization Algorithm. Hybrid Intelligent Systems, 2007. HIS 2007. 7th International Conference IEEE.
http://dx.doi.org/10.1109/ichis.2007.4344075

Hadavandi Esmaeil,Shavandi Hassan, & Ghanbari Arash (2011),An improved sales forecasting approach by the integration of genetic fuzzy systems and data clustering : Case study of printed circuit board, Expert Systems with Applications 38 (2011) 9392 9399.
http://dx.doi.org/10.1016/j.eswa.2011.01.132

Goldberg, D. A. (1989). Genetic algorithms in search, optimization, and machine learning. Reading, MA : Addison-Wesley.

Kuo, R. J., & Chen, J. A. (2004). A decision support system for order selection in electronic commerce based on fuzzy neural network supported by realcoded genetic algorithm. Expert Systems with Applications, 26(2),141 154.
http://dx.doi.org/10.1016/s0957-4174(03)00115-5

Kuo .R.J & Xue. K.C (1999).Fuzzy neural networks with application to sales forecasting.Fuzzy Sets and Systems, 108,123 143.
http://dx.doi.org/10.1016/s0165-0114(97)00326-6

Kyong Joo Oh, Ingoo Han (2001),An intelligent clustering forecasting system based on change-point detection and artificial neural networks :application to financial economics.System Sciences, 2001. Proceedings of the 34th Annual Hawaii
http://dx.doi.org/10.1109/hicss.2001.926304

Martínez-López, F., & Casillas, J. (2009). Marketing intelligent systems for consumer behaviour modelling by a descriptive induction approach based on genetic fuzzy. Industrial Marketing Management.
http://dx.doi.org/10.1016/j.indmarman.2008.02.003

Orriols-Puig, A., Casillas, J., & Martínez-López, F. (2009). Unsupervised learning of fuzzy association rules for consumer behavior modeling. Mathware and Soft Computing, 16(1),29 43.

Saeid Iranmanesh, M. Amin Mahdavi (2009). A Differential Adaptive Learning Rate Method for Back-Propagation Neural Networks.World Academy of Science, Engineering and Technology 50 2009.

Sivanandam.S. N, Visalakshi P : Dynamic task scheduling with load balancing using parallel orthogonal particle swarm optimisation. IJBIC 1(4) : 276 286 (2009)
http://dx.doi.org/10.1504/ijbic.2009.024726

Toly Chen (2003).A fuzzy back propagation network for output time prediction in a wafer fab.Applied Soft Computing 2/3F (2003),211 222.
http://dx.doi.org/10.1016/s1568-4946(02)00066-2

Zhang, Wang & Chang (2009) . A Model on Forecasting Safety Stock of ERP Based on BP Neural Network.Proceedings of the 2008 IEEE ICMIT Zhang, Haifeng and Huang(2010).Sales Forecasting Based on ERP System through BP Neural Networks.ISICA’10 Proceedings of the 5th international conference on Advances in computation and intelligence
http://dx.doi.org/10.1007/978-3-642-16493-4_30

Chang, I-C., Hwang, H-G., Hung, M-C., Chen, S-L., Yen, D., “A Neural Network Evaluation Model for ERP Performance from SCM Perspective to Enhance Enterprise Competitive Advantage, .” Expert Systems with Applications 2007.08:102
http://dx.doi.org/10.1016/j.eswa.2007.08.102

Hicham, A., Mohamed, B., Abdellah, E.F., An improved approach based on fuzzy clustering and Back-propagation Neural networks with adaptive learning rate for sales forecasting, (2012) International Review on Computers and Software (IRECOS), 7 (3), pp. 1053-1061.


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