In Supply Chain Management (SCM), strengthening partnerships with suppliers is a significant factor for enhancing competitiveness. Hence, firms increasingly emphasize supplier evaluation processes. Supplier evaluation systems are basically developed in terms of criteria such as quality, cost, delivery, and flexibility. Because there are many variables to be analyzed, this process becomes hard to execute and needs expertise. On this account, this study aims to develop an expert system on supplier evaluation process by designing Artificial Neural Network (ANN) that is supported with Data Envelopment Analysis (DEA). The methods are applied on the data of 24 suppliers, which have longterm relationships with a medium sized company from German Iron and Steel Industry. The data of suppliers consists of variables such as material quality (MQ), discount of amount (DOA), discount of cash (DOC), payment term (PT), delivery time (DT) and annual revenue (AR). Meanwhile, the efficiency that is generated by using DEA is added to the supplier evaluation system in order to use them as system outputs.
[1] J. Wu, G. Liu and C. Yu, "The study on agile supply chain-based
supplier selection and evaluation," 2008 International Symposium on
Information Science and Engineering, pp. 280-285, 2008.
[2] J. G. Wacker, "A theoretical model of manufacturing lead times and
their relationship to a manufacturing goal hierarchy," Decision Sciences,
vol. 27, no. 3, pp. 483-517, 1996.
[3] M. Kumaraswamy, E. Palaneeswaran and P. Humphreys, "Selection
matters - in construction supply chain optimization," International
Journal of Physical Distribution & Logistics Management, vol. 30, no. 7,
2000.
[4] S. Zeng, A. C. Mitchell, J. S. Benjamin and J. Sairamesh, "A supplier
performance evaluation solution for proactive supplier quality
management," IEEE International Conference on E-Business
Engineering, 2008.
[5] S. Wei, J. Zhang and Z. Li, "A supplier-selecting system using a neural
network," IEEE International Conference on Intelligent Processing
Systems, Beijing, China, October 28-31, 1997.
[6] K. L. Choy and W. B. Lee, "A generic tool for the selection and
management of supplier relationships in an outsourced manufacturing
environment: the application of cased based reasoning," Logistics
Information Management, vol. 15, pp. 235-253, 2002.
[7] P. Tang, J. Lu and Z. Zhao, "Evaluation and selection of supplier based
on improved grey relation analysis," Wireless Communications,
Networking and Mobile Computing (WiCOM), 4th International
Conference. 2008.
[8] Q. Li, "A fuzzy neural network based multi-criteria decision making
approach for outsourcing supplier evaluation," Industrial Electronics and
Applications, 3rd IEEE Conference (ICIEA), 2008.
[9] K.L. Choy, W.B. Lee and C.W. Lau, "A knowledge-based supplier
intelligence retrieval system for outsource manufacturing," Knowledge-
Based Systems, vol. 18, pp. 1-17, 2005.
[10] B. H. Pang, "A method of suppliers evaluation and choice based on AHP
and fuzzy theory," Proceedings of the Seventh International Conference
on Machine Learning and Cybernetics, Kunming, 12-15 July 2008.
[11] E. Koskivaara, "Neural networks in analytical review procedures,"
Managerial Auditing Journal, vol. 19, no. 2, pp. 191-223, 2004.
[12] K.L. Choy, W.B. Lee and V. Lob, "Design of an intelligent supplier
relationship management system: a hybrid case based neural network
approach," Expert Systems with Applications, vol. 24, pp. 225-237,
2003.
[13] S. Kahraman, O. Gunaydin, M. Alber and M. Fener, "Evaluating the
strength and deformability properties of misis fault breccia using
artificial neural networks," Expert Systems with Applications, vol. 36,
pp. 6874-6878, 2009.
[14] P. Gupta and N. K. Sinha, "An improved approach for nonlinear system
identification using neural networks," Journal of the Franklin Institute,
vol. 336, no. 4, pp. 721-734. 1999.
[15] J. Johnson and P. Picton, "Mechatronics: Designing Intelligent
Machines: Concepts in Artificial Intelligence", Butterworth-Heinemann.
1995.
[16] T.C. Tang and L.C. Chi, "Neural networks analysis in business failure
prediction of Chinese importers: A between-countries approach" Expert
Systems with Applications, vol. 29, pp. 244-255, 2005.
[17] A. Costa and R. N. Markellos, "Evaluating public transport efficiency
with neural network models", Transpn Res. C., vol. 5, no 5, pp. 301-312,
1997
[18] M. Norman and B. Stoker, "Data envelopment analysis the analysis of
performance", John Wiley & Sons, Chichester, 1991
[19] S. C. Hu, Y. K. Chung and Y. S. Chen, "Using hopfield neural networks
to solve DEA problems" CIS 2008.
[20] D. Wu, Z. Yang and L. Liang, "Using DEA-neural network approach to
evaluate branch efficiency of a large Canadian bank," Expert System
with Applications, pp.108-115, 2006.
[21] N. S. Chauhan, P. K. J. Mohapatra and K. P. Pandey, "Improving energy
productivity in paddy production through benchmarking-An application
of data envelopment analysis", Energy Conversion and Management, vol
47, pp. 1063-1085, 2006
[22] R. Madlener, C. H. Antunes and L. C. Dias, "Assessing the performance
of biogas plants with multi-criteria and data envelopment analysis,"
European Journal of Operations Research, vol 197, pp.1084-1094, 2009.
[23] S. Wang, "Adaptive non-parametric efficiency frontier analysis: a
neural-network-based model," Computers and Operations Research, vol
30, pp. 279-295, 2003.
[24] D. Santin, F. J. Delgado and A. Valino, "The measurement of technical
efficiency: a neural network approach," Applied Economics, vol. 36, pp.
627-635, 2004.
[25] H. Liao, B. Wang and T. Weyman-Jones, "Neural network based models
for efficiency frontier analysis: an application to east asian economies-
Growth Decomposition," Global Economic Review, vol. 36, no. 4, pp.
361-384, 2007.
[26] A. Emrouznejad and E. Shale, "A combined neural network and DEA
for measuring efficiency of large scale datasets", Computers & Industrial
Engineering, vol. 56, pp. 249-254, 2009.
[27] P. C. Pendharkar and J. A. Rodger, "Technical efficiency-based
selection of learning cases to improve forecasting accuracy of neural
networks under monotonicity assumption," Decision Support Systems,
vol. 36, pp. 117-136, 2003.
[28] C. Wu, X. Chen and Y. Yang, "Decision-making modeling method
based on artificial neural network and data envelopment analysis",
International Geoscience and Remote Sensing Symposium Proceedings:
Science for Society: Exploring and Managing a Changing Planet,
IGARSS, 2004.
[29] H. C. Liao, "A data envelopment analysis method for optimizing multiresponse
problem with censored data in the Taguchi method,"
Computers & Industrial Engineering, vol. 46, pp. 817-835, 2004.
[1] J. Wu, G. Liu and C. Yu, "The study on agile supply chain-based
supplier selection and evaluation," 2008 International Symposium on
Information Science and Engineering, pp. 280-285, 2008.
[2] J. G. Wacker, "A theoretical model of manufacturing lead times and
their relationship to a manufacturing goal hierarchy," Decision Sciences,
vol. 27, no. 3, pp. 483-517, 1996.
[3] M. Kumaraswamy, E. Palaneeswaran and P. Humphreys, "Selection
matters - in construction supply chain optimization," International
Journal of Physical Distribution & Logistics Management, vol. 30, no. 7,
2000.
[4] S. Zeng, A. C. Mitchell, J. S. Benjamin and J. Sairamesh, "A supplier
performance evaluation solution for proactive supplier quality
management," IEEE International Conference on E-Business
Engineering, 2008.
[5] S. Wei, J. Zhang and Z. Li, "A supplier-selecting system using a neural
network," IEEE International Conference on Intelligent Processing
Systems, Beijing, China, October 28-31, 1997.
[6] K. L. Choy and W. B. Lee, "A generic tool for the selection and
management of supplier relationships in an outsourced manufacturing
environment: the application of cased based reasoning," Logistics
Information Management, vol. 15, pp. 235-253, 2002.
[7] P. Tang, J. Lu and Z. Zhao, "Evaluation and selection of supplier based
on improved grey relation analysis," Wireless Communications,
Networking and Mobile Computing (WiCOM), 4th International
Conference. 2008.
[8] Q. Li, "A fuzzy neural network based multi-criteria decision making
approach for outsourcing supplier evaluation," Industrial Electronics and
Applications, 3rd IEEE Conference (ICIEA), 2008.
[9] K.L. Choy, W.B. Lee and C.W. Lau, "A knowledge-based supplier
intelligence retrieval system for outsource manufacturing," Knowledge-
Based Systems, vol. 18, pp. 1-17, 2005.
[10] B. H. Pang, "A method of suppliers evaluation and choice based on AHP
and fuzzy theory," Proceedings of the Seventh International Conference
on Machine Learning and Cybernetics, Kunming, 12-15 July 2008.
[11] E. Koskivaara, "Neural networks in analytical review procedures,"
Managerial Auditing Journal, vol. 19, no. 2, pp. 191-223, 2004.
[12] K.L. Choy, W.B. Lee and V. Lob, "Design of an intelligent supplier
relationship management system: a hybrid case based neural network
approach," Expert Systems with Applications, vol. 24, pp. 225-237,
2003.
[13] S. Kahraman, O. Gunaydin, M. Alber and M. Fener, "Evaluating the
strength and deformability properties of misis fault breccia using
artificial neural networks," Expert Systems with Applications, vol. 36,
pp. 6874-6878, 2009.
[14] P. Gupta and N. K. Sinha, "An improved approach for nonlinear system
identification using neural networks," Journal of the Franklin Institute,
vol. 336, no. 4, pp. 721-734. 1999.
[15] J. Johnson and P. Picton, "Mechatronics: Designing Intelligent
Machines: Concepts in Artificial Intelligence", Butterworth-Heinemann.
1995.
[16] T.C. Tang and L.C. Chi, "Neural networks analysis in business failure
prediction of Chinese importers: A between-countries approach" Expert
Systems with Applications, vol. 29, pp. 244-255, 2005.
[17] A. Costa and R. N. Markellos, "Evaluating public transport efficiency
with neural network models", Transpn Res. C., vol. 5, no 5, pp. 301-312,
1997
[18] M. Norman and B. Stoker, "Data envelopment analysis the analysis of
performance", John Wiley & Sons, Chichester, 1991
[19] S. C. Hu, Y. K. Chung and Y. S. Chen, "Using hopfield neural networks
to solve DEA problems" CIS 2008.
[20] D. Wu, Z. Yang and L. Liang, "Using DEA-neural network approach to
evaluate branch efficiency of a large Canadian bank," Expert System
with Applications, pp.108-115, 2006.
[21] N. S. Chauhan, P. K. J. Mohapatra and K. P. Pandey, "Improving energy
productivity in paddy production through benchmarking-An application
of data envelopment analysis", Energy Conversion and Management, vol
47, pp. 1063-1085, 2006
[22] R. Madlener, C. H. Antunes and L. C. Dias, "Assessing the performance
of biogas plants with multi-criteria and data envelopment analysis,"
European Journal of Operations Research, vol 197, pp.1084-1094, 2009.
[23] S. Wang, "Adaptive non-parametric efficiency frontier analysis: a
neural-network-based model," Computers and Operations Research, vol
30, pp. 279-295, 2003.
[24] D. Santin, F. J. Delgado and A. Valino, "The measurement of technical
efficiency: a neural network approach," Applied Economics, vol. 36, pp.
627-635, 2004.
[25] H. Liao, B. Wang and T. Weyman-Jones, "Neural network based models
for efficiency frontier analysis: an application to east asian economies-
Growth Decomposition," Global Economic Review, vol. 36, no. 4, pp.
361-384, 2007.
[26] A. Emrouznejad and E. Shale, "A combined neural network and DEA
for measuring efficiency of large scale datasets", Computers & Industrial
Engineering, vol. 56, pp. 249-254, 2009.
[27] P. C. Pendharkar and J. A. Rodger, "Technical efficiency-based
selection of learning cases to improve forecasting accuracy of neural
networks under monotonicity assumption," Decision Support Systems,
vol. 36, pp. 117-136, 2003.
[28] C. Wu, X. Chen and Y. Yang, "Decision-making modeling method
based on artificial neural network and data envelopment analysis",
International Geoscience and Remote Sensing Symposium Proceedings:
Science for Society: Exploring and Managing a Changing Planet,
IGARSS, 2004.
[29] H. C. Liao, "A data envelopment analysis method for optimizing multiresponse
problem with censored data in the Taguchi method,"
Computers & Industrial Engineering, vol. 46, pp. 817-835, 2004.
@article{"International Journal of Mechanical, Industrial and Aerospace Sciences:50011", author = "Dilek Özdemir and Gül Tekin Temur", title = "DEA ANN Approach in Supplier Evaluation System", abstract = "In Supply Chain Management (SCM), strengthening partnerships with suppliers is a significant factor for enhancing competitiveness. Hence, firms increasingly emphasize supplier evaluation processes. Supplier evaluation systems are basically developed in terms of criteria such as quality, cost, delivery, and flexibility. Because there are many variables to be analyzed, this process becomes hard to execute and needs expertise. On this account, this study aims to develop an expert system on supplier evaluation process by designing Artificial Neural Network (ANN) that is supported with Data Envelopment Analysis (DEA). The methods are applied on the data of 24 suppliers, which have longterm relationships with a medium sized company from German Iron and Steel Industry. The data of suppliers consists of variables such as material quality (MQ), discount of amount (DOA), discount of cash (DOC), payment term (PT), delivery time (DT) and annual revenue (AR). Meanwhile, the efficiency that is generated by using DEA is added to the supplier evaluation system in order to use them as system outputs.
", keywords = "Artificial Neural Network (ANN), DataEnvelopment Analysis (DEA), Supplier Evaluation System.", volume = "3", number = "6", pages = "678-6", }
