Vendor Selection Risk Management Framework in Automotive Industry
International Journal of Mechanical Engineering and Applications
Volume 3, Issue 3-1, June 2015, Pages: 57-66
Received: Oct. 11, 2016; Accepted: Oct. 13, 2016; Published: Nov. 7, 2016
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Authors
Kamran Mohtasham, Department of Mechanical and Manufacturing Engineering, Faculty of Engineering, Universiti Putra Malaysia, Malaysia
Faieza Abdul Aziz, Department of Mechanical and Manufacturing Engineering, Faculty of Engineering, Universiti Putra Malaysia, Malaysia
Mohd Khairol Anuar B. Mohd Ariffin, Department of Mechanical and Manufacturing Engineering, Faculty of Engineering, Universiti Putra Malaysia, Malaysia
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Abstract
Disruption of the supply chain can happen at any level of the process; therefore, investigation on the possible risks in the supply chain is inevitable in any SCM activity. Supplier failure is a major threat to the supply chain and to ensure proper vendor selection, this study aimed to establish a vendor selection procedure that can reduce the risk of supply chain disruption. Linear weighting method is used to analyze the risk factors and construct an empirically reliable model for supplier evaluation. The result of multi-criteria vendor evaluation model showed that supplier product quality had the highest degree of influence on vendor selection risk management. It was found that in a sequential order, product quality, human resources, financial power, governmental support, IT and R&D opportunities, and environmental vulnerability of the supplier are critical to supply chain management. The outcome of the current research is a vendor selection framework that utilizes the proposed supplier evaluation model to reduce the risk in vendor selection.
Keywords
Vendor Induced Risks, Supply Chain, Risk Management
To cite this article
Kamran Mohtasham, Faieza Abdul Aziz, Mohd Khairol Anuar B. Mohd Ariffin, Vendor Selection Risk Management Framework in Automotive Industry, International Journal of Mechanical Engineering and Applications. Special Issue: Transportation Engineering Technology — Part Ⅱ. Vol. 3, No. 3-1, 2015, pp. 57-66. doi: 10.11648/j.ijmea.s.2015030301.19
Copyright
Copyright © 2015 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
References
[1]
Croom, S. et al. Supply chain management: an analytical framework for critical literature review. Eur. J. Purch. Supply Manag, 2000. 6(2), 67–83.
[2]
Tang, C. S. Perspectives in supply chain risk management. Int. J. Prod. Econ, 2006. 103(4), 451–488.
[3]
Das, S.K. and Abdel-Malek, L. Modeling the flexibility of order quantities and lead-times in supply chains. Int. J. Prod. Econ, 2003. 85(3), 171–181.
[4]
Xia, D. and Chen, B. A comprehensive decision-making model for risk management of supply chain. Expert Syst. Appl, 2011. 38(4), 4957–4966.
[5]
McCormack, K. et al. Managing Risk in Your Organization with the SCOR Methodology. The Supply Chain Council Risk Research Team, 2008, 134–137.
[6]
Wu, D. et al. Supply chain outsourcing risk using an integrated stochastic-fuzzy optimization approach. Inf. Sci. (Ny), 2013. 235(6), 242–258.
[7]
Vanteddu, G. et al. Supply chain focus dependent supplier selection problem. Int. J. Prod. Econ, 2011. 129(6), 204–216.
[8]
Faez, F. et al. Vendor selection and order allocation using an integrated fuzzy case-based reasoning and mathematical programming model. Int. J. Prod. Econ, 2009. 121(3), 395–408.
[9]
Norrman, A. and Jansson, U. Ericsson’s proactive supply chain risk management approach after a serious sub-supplier accident. Int. J. Phys. Distrib. Logist. Manag, 2004. 34(3), 434–456.
[10]
Wagner, S. M. and Bode, C. An empirical investigation into supply chain vulnerability. J. Purch. Supply Manag, 2006. 12(1), 301–312.
[11]
Ferreira, L. and Borenstein, D. A fuzzy-Bayesian model for supplier selection. Expert Syst. Appl, 2012. 39(3), 7834–7844.
[12]
Bowersox, D. et al. Supply Chain Logistics Management, McGraw-Hill Education, 2002.45-49.
[13]
Sawik, T. Supplier selection in make-to-order environment with risks. Math. Comput. Model, 2011. 53(3), 1670–1679.
[14]
Ruiz-Torres, A. J. et al. Supplier selection model with contingency planning for supplier failures. Comput. Ind. Eng, 2013. 66(5), 374–382.
[15]
Chen, Y.-J. Structured methodology for supplier selection and evaluation in a supply chain. Inf. Sci. (Ny), 2011. 181(8), 1651–1670.
[16]
Aksoy, A. and Öztürk, N. Supplier selection and performance evaluation in just-in-time production environments. Expert Syst. Appl, 2011. 38(1), 6351–6359.
[17]
Huang, G.Q. et al. A new model of the customer–supplier partnership in new product development. J. Mater. Process. Technol, 2003. 138(4), 301–305.
[18]
Huang, S. H. and Keskar, H. Comprehensive and configurable metrics for supplier selection. Int. J. Prod. Econ, 2007. 105(11), 510–523.
[19]
Zolghadri, M. et al. Power-based supplier selection in product development projects. Comput. Ind, 2011. 62(3), 487–500.
[20]
Weber, C. A. et al. Vendor selection criteria and methods. Eur. J. Oper. Res, 1991. 50(5), 2–18.
[21]
Wilson, E. J. The Relative Importance of Supplier Selection Criteria: A Review and Update. Int. J. Purch. Mater. Manag, 1994. 30(3), 34–41.
[22]
Verma, R. and Pullman, M. E. An analysis of the supplier selection process. Omega, 1998. 26(2), 739–750.
[23]
Lam, K.-C. et al. A material supplier selection model for property developers using Fuzzy Principal Component Analysis. Autom. Constr, 2010. 19(2), 608–618.
[24]
Kilic, H. S. An integrated approach for supplier selection in multi-item/multi-supplier environment. Appl. Math. Model, 2013. 37(4), 7752–7763.
[25]
Azaron, a. et al. A multi-objective stochastic programming approach for supply chain design considering risk. Int. J. Prod. Econ, 2008.116(5), 129–138.
[26]
Omurca, S. I. An intelligent supplier evaluation, selection and development system. Appl. Soft Comput, 2013.13(20), 690–697.
[27]
Ávila, P. et al. Supplier’s Selection Model based on an Empirical Study. Procedia Technol, 2012. 5(1), 625–634.
[28]
Ng, W.L. An efficient and simple model for multiple criteria supplier selection problem. Eur. J. Oper. Res, 2008. 186(5), 1059–1067.
[29]
Kusaba, K. et al. Low-cost country sourcing competence: a conceptual framework and empirical analysis. J. Supply Chain Manag, 2011. 47(3), 73–93.
[30]
Goh, M. et al. A stochastic model for risk management in global supply chain networks. Eur. J. Oper. Res, 2007. 182(6), 164–173.
[31]
Esposito, J. L. A Framework Relating Questionnaire Design-and Evaluation Processes to Sources of Measurement Error., in International Conference on Questionnaire Development, Evaluation, and Testing Methods, 2002. 32–37.
[32]
Kocabasoglu, C. et al. Linking forward and reverse supply chain investments: The role of business uncertainty. J. Oper. Manag, 2007. 25(4), 1141–1160.
[33]
Chen, I.J. and Paulraj, A. Towards a theory of supply chain management: the constructs and measurements. J. Oper. Manag, 2004. 22(2), 119–150.
[34]
De Boer, L. et al. A review of methods supporting supplier selection. Eur. J. Purch. Supply Manag, 2001. 7(2), 75–89.
[35]
Mousavi, M. et al. Virtual Reality Framework Development in Malaysian Automotive Manufacturing Industry. Aust. J. Basic Appl. Sci, 2013.7(1), 582–589.
[36]
Deros, B. M. et al. A benchmarking implementation framework for automotive manufacturing SMEs. Benchmarking An Int. J, 2006. 13(2), 396–430.
[37]
Thun, J.-H. and Hoenig, D. An empirical analysis of supply chain risk management in the German automotive industry. Int. J. Prod. Econ, 2011. 131(3), 242–249.
[38]
Shariat, A. et al. The Adverse Health Effects of Shift Work in Relation to Risk of Illness/Disease: A Review. Acta Medica Bulgarica, 2015. 42(1), 63–72.
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