An ERP software selection process with using artificial neural network based on analytic network process approach

An enterprise resource planning (ERP) software selection is known to be multi attribute decision making (MADM) problem. This problem has been modeled according with analytic network process (ANP) method due to fact that it considers criteria and sub criteria relations and interrelations in selecting the software. Opinions of many experts are obtained while building ANP model for the selection ERP then opinions are reduced to one single value by methods like geometric means so as to get desired results. To use ANP model for the selection of ERP for a new organization, a new group of expert's opinions are needed. In this case the same problem will be in counter. In the proposed model, when ANP and ANN models are setup, an ERP software selection can be made easily by the opinions of one single expert. In that case calculation of geometric mean of answers that obtained from many experts will be unnecessary. Additionally the effect of subjective opinion of one single decision maker will be avoided. In terms of difficulty, ANP has some difficulties due to eigenvalue and their limit value calculation. An ANN model has been designed and trained with using ANP results in order to calculate ERP software priority. The artificial neural network (ANN) model is trained by results obtained from ANP. It seems that there is no any major difficulty in order to predict software priorities with trained ANN model. By this results ANN model has been come suitable for using in the selection of ERP for another new decision.

[1]  Angappa Gunasekaran,et al.  Erratum to “Implementation of enterprise resource planning in China” [Technovation 26 (2006) 1324–1336] , 2008 .

[2]  R. Shankar,et al.  On‐line trust building in e‐enabled supply chain , 2003 .

[3]  K Ishii,et al.  Evaluation of remedial countermeasures using the analytic network process. , 2006, Waste management.

[4]  Chin-Tsai Lin,et al.  Evaluating digital video recorder systems using analytic hierarchy and analytic network processes , 2007, Inf. Sci..

[5]  R. J. Kuo,et al.  A decision support system for selecting convenience store location through integration of fuzzy AHP and artificial neural network , 2002, Comput. Ind..

[6]  Volker Stix,et al.  Profile distance method - a multi-attribute decision making approach for information system investments , 2006, Decis. Support Syst..

[7]  T. L. Saaty,et al.  Decision making with dependence and feedback , 2001 .

[8]  T. Saaty,et al.  Procedures for Synthesizing Ratio Judgements , 1983 .

[9]  T. Saaty,et al.  Fundamentals of the analytic network process — Dependence and feedback in decision-making with a single network , 2004 .

[10]  Chao-Ton Su,et al.  Applying neural network approach to achieve robust design for dynamic quality characteristics , 1998 .

[11]  Masood A. Badri,et al.  A comprehensive 0-1 goal programming model for project selection , 2001 .

[12]  Ludmil Mikhailov,et al.  A fuzzy approach to deriving priorities from interval pairwise comparison judgements , 2004, Eur. J. Oper. Res..

[13]  Yuan Li,et al.  A model for selecting an ERP system based on linguistic information processing , 2007, Inf. Syst..

[14]  Qing Li,et al.  Enterprise information system project selection with regard to BOCR , 2008 .

[15]  Joseph Sarkis,et al.  Analyzing organizational project alternatives for agile manufacturing processes: An analytical network approach , 1999 .

[16]  E. Ertugrul Karsak,et al.  An integrated decision making approach for ERP system selection , 2009, Expert Syst. Appl..

[17]  Miroslaw J. Skibniewski,et al.  Neural Network Method of Estimating Construction Technology Acceptability , 1995 .

[18]  Zeki Ayağ,et al.  An intelligent approach to ERP software selection through fuzzy ANP , 2007 .

[19]  Jacques Verville,et al.  A SIX-STAGE MODEL OF THE BUYING PROCESS FOR ERP SOFTWARE , 2003 .

[20]  Manoj Kumar Tiwari,et al.  Analyzing alternatives in reverse logistics for end-of-life computers: ANP and balanced scorecard approach , 2005, Comput. Ind. Eng..

[21]  Hannu Kivijärvi,et al.  Supporting the Module Sequencing Decision in the ERP Implementation Process , 2009, Proceedings of the 39th Annual Hawaii International Conference on System Sciences (HICSS'06).

[22]  James L. McClelland,et al.  Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .

[23]  Chen-Fu Chien,et al.  An AHP-based approach to ERP system selection , 2005 .

[24]  T. Saaty,et al.  The Analytic Hierarchy Process , 1985 .

[25]  Yi-Chung Hu,et al.  Backpropagation multi-layer perceptron for incomplete pairwise comparison matrices in analytic hierarchy process , 2006, Appl. Math. Comput..

[26]  Mao-Jiun J. Wang,et al.  A comprehensive framework for selecting an ERP system , 2004 .

[27]  Satoshi Matsuda.  A Neural Network Model for the Decision-Making Process Based on ANP , 2006, The 2006 IEEE International Joint Conference on Neural Network Proceedings.

[28]  Thomas L. Saaty,et al.  Decision making with dependence and feedback : the analytic network process : the organization and prioritization of complexity , 1996 .

[29]  Soung Hie Kim,et al.  Using analytic network process and goal programming for interdependent information system project selection , 2000, Comput. Oper. Res..

[30]  Semra Boran,et al.  A Study On Election Of Personnel Based On Performance Measurement By Using Analytic Network Process (ANP) , 2008 .

[31]  Minghe Sun,et al.  Artificial neural network representations for hierarchical preference structures , 1996, Comput. Oper. Res..