Using artificial neural networks and analytic hierarchy process for the supplier selection problem

Pattern classification and its applications are classic domains of study in the area of artificial intelligence. In this study, we integrate neural networks and analytic hierarchy process for the purpose of pattern classification, and showcase the application for the supplier selection problem in the domain of procurement management. Pattern classification has been used for the purpose of supplier base rationalization. In this study, the criteria for decision making have been modeled using fuzzy logic, which further has been modeled as a multi-objective decision making process, by combining the two complementary approaches. The integrated approach drastically reduces the data points required for training and thus extends the applicability of the classifier in the supplier selection domain where the data points is not so high, as is required by classifiers. The proposed integrated approach has been further studied through a case study conducted on a multi-national firm and the results of the analysis have been discussed.

[1]  Shigeo Abe DrEng Pattern Classification , 2001, Springer London.

[2]  J. Buckley,et al.  Fuzzy hierarchical analysis , 1999, FUZZ-IEEE'99. 1999 IEEE International Fuzzy Systems. Conference Proceedings (Cat. No.99CH36315).

[3]  Pi-Fang Hsu,et al.  Developing and Implementing a Selection Model for Bedding Chain Retail Store Franchisee Using Delphi and Fuzzy AHP , 2007 .

[4]  Huseyin Basligil,et al.  Pre-selection of suppliers through an integrated fuzzy analytic hierarchy process and max-min methodology , 2010 .

[5]  Srinivas Talluri,et al.  A Supply Risk Reduction Model Using Integrated Multicriteria Decision Making , 2008, IEEE Transactions on Engineering Management.

[6]  Yoshua Bengio,et al.  Pattern Recognition and Neural Networks , 1995 .

[7]  Eleonora Bottani,et al.  A fuzzy multi-attribute framework for supplier selection in an e-procurement environment , 2005 .

[8]  Ezgi Aktar Demirtaş,et al.  An integrated multiobjective decision making process for supplier selection and order allocation , 2008 .

[9]  Peter L. Bartlett,et al.  The Sample Complexity of Pattern Classification with Neural Networks: The Size of the Weights is More Important than the Size of the Network , 1998, IEEE Trans. Inf. Theory.

[10]  L. V. D. Wegen,et al.  Outranking methods in support of supplier selection , 1998 .

[11]  David G. Stork,et al.  Pattern Classification , 1973 .

[12]  Sushil Kumar,et al.  Analytic hierarchy process: An overview of applications , 2006, Eur. J. Oper. Res..

[13]  Zeger Degraeve,et al.  An evaluation of vendor selection models from a total cost of ownership perspective , 2000, Eur. J. Oper. Res..

[14]  Emre Cevikcan,et al.  Intelligence decision systems in enterprise information management , 2011, J. Enterp. Inf. Manag..

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

[16]  José María Moreno-Jiménez,et al.  The geometric consistency index: Approximated thresholds , 2003, Eur. J. Oper. Res..

[17]  Rina Azoulay-Schwartz,et al.  Exploitation vs. exploration: choosing a supplier in an environment of incomplete information , 2004, Decis. Support Syst..

[18]  Xiaowei Xu,et al.  Multi-criteria decision making approaches for supplier evaluation and selection: A literature review , 2010, Eur. J. Oper. Res..

[19]  S. H. Ghodsypour,et al.  The total cost of logistics in supplier selection, under conditions of multiple sourcing, multiple criteria and capacity constraint , 2001 .

[20]  M. Sevkli,et al.  Fuzzy Analytic Hierarchy Based Approach for Supplier Selection , 2003 .

[21]  R. Hill,et al.  Using the Analytic Hierarchy Process to Structure the Supplier Selection Procedure , 1992 .

[22]  Victor B. Kreng,et al.  The application of Fuzzy Delphi Method and Fuzzy AHP in lubricant regenerative technology selection , 2010, Expert Syst. Appl..

[23]  Tiesong Hu,et al.  A fuzzy neural network approach for contractor prequalification , 2001 .

[24]  G. Crawford,et al.  A note on the analysis of subjective judgment matrices , 1985 .

[25]  R.P. Lippmann,et al.  Pattern classification using neural networks , 1989, IEEE Communications Magazine.

[26]  R. Ramanathan Supplier selection problem: integrating DEA with the approaches of total cost of ownership and AHP , 2007 .

[27]  V. M. Rao Tummala,et al.  An application of the AHP in vendor selection of a telecommunications system , 2001 .

[28]  Arpan Kumar Kar,et al.  A Soft Classification Model for Vendor Selection , 2011 .

[29]  A. Rangone,et al.  A contingent approach to the design of vendor selection systems for different types of co‐operative customer/supplier relationships , 2000 .

[30]  Barruquer Moner IX. References , 1971 .

[31]  F. Çebi,et al.  An integrated approach for supplier selection , 2003 .

[32]  Mohamed A. Youssef,et al.  Supplier selection in an advanced manufacturing technology environment: an optimization model , 1996 .

[33]  A. Kar Modeling of Supplier Selection in e-Procurement as a Multi-Criteria Decision Making Problem , 2009 .

[34]  Ramayya Krishnan,et al.  A hybrid approach to supplier selection for the maintenance of a competitive supply chain , 2008, Expert Syst. Appl..

[35]  P. K. Simpson,et al.  Fuzzy min-max neural networks , 1991, [Proceedings] 1991 IEEE International Joint Conference on Neural Networks.

[36]  S. Deshmukh,et al.  Vendor Selection Using Interpretive Structural Modelling (ISM) , 1994 .

[37]  Arpan Kumar Kar,et al.  A Study to Compare Relative Importance of Criteria for Supplier Evaluation in e-Procurement , 2011, 2011 44th Hawaii International Conference on System Sciences.

[38]  J. Current,et al.  An optimization approach to determining the number of vendors to employ , 2000 .

[39]  Margaret H. Dunham,et al.  Data Mining: Introductory and Advanced Topics , 2002 .

[40]  Fatemeh Zahedi,et al.  The Analytic Hierarchy Process—A Survey of the Method and its Applications , 1986 .

[41]  Patrick K. Simpson,et al.  Fuzzy min-max neural networks. I. Classification , 1992, IEEE Trans. Neural Networks.