A New Approach for Supply Chain Risk Management: Mapping SCOR into Bayesian Network


Journal Name:


Publication Year:


Page Number-First: 
Page Number-Last: 

Publication Language:

Abstract (2. Language): 
Purpose: Increase of costs and complexities in organizations beside the increase of uncertainty and risks have led the managers to use the risk management in order to decrease risk taking and deviation from goals. SCRM has a close relationship with supply chain performance. During the years different methods have been used by researchers in order to manage supply chain risk but most of them are either qualitative or quantitative. Supply chain operation reference (SCOR) is a standard model for SCP evaluation which have uncertainty in its metrics. In This paper by combining qualitative and quantitative metrics of SCOR, supply chain performance will be measured by Bayesian Networks. Design/methodology/approach: First qualitative assessment will be done by recognizing uncertain metrics of SCOR model and then by quantifying them, supply chain performance will be measured by Bayesian Networks (BNs) and supply chain operations reference (SCOR) in which making decision on uncertain variables will be done by predictive and diagnostic capabilities. Findings: After applying the proposed method in one of the biggest automotive companies in Iran, we identified key factors of supply chain performance based on SCOR model through predictive and diagnostic capability of Bayesian Networks. After sensitivity analysis, we find out that ‘Total cost’ and its criteria that include costs of labors, warranty, transportation and inventory have the widest range and most effect on supply chain performance. So, managers should take their importance into account for decision making. We can make decisions simply by running model in different situations. Research limitations/implications: A more precise model consisted of numerous factors but it is difficult and sometimes impossible to solve big models, if we insert all of them in a Bayesian model. We have adopted real world characteristics with our software and method abilities. On the other hand, fewer data exist for some of the performance metrics. Practical implications: Mangers often use simple qualitative metrics for SCRM. However, combining qualitative and quantitative metrics will be more useful. Industries can recognize the important uncertain metrics by predicting supply chain performance and diagnosing possible happenings. Originality/value: This paper proposed a Bayesian method based on SCOR metrics which has the ability to manage supply chain risks and improve supply chain performance. This is the only presented case study for measuring supply chain performance by SCOR metrics.



Adhitya, A., Srinivasan, R., & Karimi, I.A. (2009). Supply chain risk identification using a
HAZOP‐based approach. AIChE journal, 55(6), 1447-1463. http://dx.doi.org/10.1002/aic.11764
Arzu Akyuz, G., & Erman Erkan, T. (2010). Supply chain performance measurement: A
literature review. International Journal of Production Research, 48(17), 5137-5155.
Cavinato, J.L. (2004). Supply chain logistics risks: from the back room to the board room.
International Journal of Physical Distribution & Logistics Management, 34(5), 383-387.
Chin, K.-S., Tang, D.-W., Yang, J.-B., Wong, S.Y., & Wang, H. (2009). Assessing new product
development project risk by Bayesian network with a systematic probability generation
methodology. Expert Systems with Applications, 36(6), 9879-9890.
Chopra, S., & Sodhi, M.S. (2012). Managing risk to avoid supply-chain breakdown. MIT Sloan
Management Review (Fall 2004).
Christopher, M., Mena, C., Khan, O., & Yurt, O. (2011). Approaches to managing global
sourcing risk. Supply Chain Management: An International Journal, 16(2), 67-81.
Christopher, M., & Peck, H. (2004). Building the resilient supply chain. The International
Journal of Logistics Management, 15(2), 1-14. http://dx.doi.org/10.1108/09574090410700275
Cirtita, H., & Glaser-Segura, D.A. (2012). Measuring downstream supply chain performance.
Journal of Manufacturing Technology Management, 23(3), 299-314.
Faisal, M., Banwet, D., & Shankar, R. (2007). Management of risk in supply chains: SCOR
approach and analytic network process. Paper presented at the Supply Chain Forum: An
International Journal.
Fox, M.S., Barbuceanu, M., & Teigen, R. (2001). Agent-oriented supply-chain management
Information-Based Manufacturing, 81-104, Springer.
Giunipero, L.C., & Eltantawy, R.A. (2004). Securing the upstream supply chain: a risk
management approach. International Journal of Physical Distribution & Logistics
Management, 34(9), 698-713. http://dx.doi.org/10.1108/09600030410567478
Gunasekaran, A., Patel, C., & McGaughey, R.E. (2004). A framework for supply chain
performance measurement. International journal of production economics, 87(3), 333-347.
Gunasekaran, A., Patel, C., & Tirtiroglu, E. (2001). Performance measures and metrics in a
supply chain environment. International journal of operations & production Management,
21(1/2), 71-87. http://dx.doi.org/10.1108/01443570110358468
Hallikas, J., Karvonen, I., Pulkkinen, U., Virolainen, V.M., & Tuominen, M. (2004). Risk
management processes in supplier networks. International Journal of Production Economics,
90(1), 47-58. http://dx.doi.org/10.1016/j.ijpe.2004.02.007
Heckerman, D., Mamdani, A., & Wellman, M.P. (1995). Real-world applications of Bayesian
networks. Communications of the ACM, 38(3), 24-26. http://dx.doi.org/10.1145/203330.203334
Hendricks, K.B., & Singhal, V.R. (2005). Association between supply chain glitches and
operating performance. Management science, 51(5), 695-711.
Hendricks, K.B., Singhal, V.R., & Zhang, R. (2009). The effect of operational slack,
diversification, and vertical relatedness on the stock market reaction to supply chain
disruptions. Journal of Operations Management, 27(3), 233-246.
Hunter, L.M., Kasouf, C.J., Celuch, K.G., & Curry, K.A. (2004). A classification of
business-to-business buying decisions: risk importance and probability as a framework for
e-business benefits. Industrial marketing management, 33(2), 145-154.
Hwang, Y.-D., Lin, Y.-C., & Lyu Jr, J. (2008). The performance evaluation of SCOR sourcing
process—The case study of Taiwan's TFT-LCD industry. International journal of production
economics, 115(2), 411-423. http://dx.doi.org/10.1016/j.ijpe.2007.09.014
Johnson, M.E., & Pyke, D.F. (2000). A framework for teaching supply chain management.
Production and Operations Management, 9(1), 2-18. http://dx.doi.org/10.1111/j.1937-5956.2000.tb00319.x
Jüttner, U., Peck, H., & Christopher, M. (2003). Supply chain risk management: outlining an
agenda for future research. International Journal of Logistics: Research and Applications,
6(4), 197-210. http://dx.doi.org/10.1080/13675560310001627016
Khodakarami, V., & Abdi, A. (2014). Project cost risk analysis: A Bayesian networks approach
for modeling dependencies between cost items. International Journal of Project Management
(0). http://dx.doi.org/10.1016/j.ijproman.2014.01.001
Lambert, D.M., & Pohlen, T.L. (2001). Supply chain metrics. The International Journal of
Logistics Management, 12(1), 1-19. http://dx.doi.org/10.1108/09574090110806190
Li, X., & Barnes, I. (2008). Proactive supply risk management methods for building a robust
supply selection process when sourcing from emerging markets. Strategic Outsourcing: An
International Journal, 1(3), 252-267. http://dx.doi.org/10.1108/17538290810915308
Lockamy, A., & McCormack, K. (2004). Linking SCOR planning practices to supply chain
performance: An exploratory study. International Journal of Operations & Production
Management, 24(12), 1192-1218. http://dx.doi.org/10.1108/01443570410569010
Manuj, I., & Mentzer, J.T. (2008). Global supply chain risk management strategies.
International Journal of Physical Distribution & Logistics Management, 38(3), 192-223.
McCormack, K., Wilkerson, T., Marrow, D., Davey, M., Shah, M., & Yee, D. (2008). Managing
risk in your organization with the SCOR methodology. The Supply Chain Council Risk
Research Team.
MCCREA, B. (2006). Metrics take center stage. Logistics Management, 37(1).
Milgate, M. (2001). Supply chain complexity and delivery performance: an international
exploratory study. Supply Chain Management: An International Journal, 6(3), 106-118.
Min, H., & Zhou, G. (2002). Supply chain modeling: past, present and future. Computers &
Industrial Engineering, 43(1), 231-249. http://dx.doi.org/10.1016/S0360-8352(02)00066-9
Norrman, A., & Jansson, U. (2004). Ericsson's proactive supply chain risk management
approach after a serious sub-supplier accident. International Journal of Physical Distribution
& Logistics Management, 34(5), 434-456. http://dx.doi.org/10.1108/09600030410545463
Pai, R.R., Kallepalli, V.R., Caudill, R.J., & Zhou, M. (2003). Methods toward supply chain risk
analysis. Paper presented at the Systems, Man and Cybernetics, 2003. IEEE International
Conference on. http://dx.doi.org/10.1109/ICSMC.2003.1245702
Patterson, R.E., Eng, C., Horowitz, S.F., Gorlin, R., & Goldstein, S.R. (1984). Bayesian
comparison of cost-effectiveness of different clinical approaches to diagnose coronary artery
disease. Journal of the American College of Cardiology, 4(2), 278-289.
Pettit, T.J., Fiksel, J., & Croxton, K.L. (2010). Ensuring supply chain resilience: Development of
a conceptual framework. Journal of Business Logistics, 31(1), 1-21.
Pitchforth, J., & Mengersen, K. (2013). A proposed validation framework for expert elicited
Bayesian Networks. Expert Systems with Applications, 40(1), 162-167.
Pollino, C.A., Woodberry, O., Nicholson, A., Korb, K., & Hart, B.T. (2007). Parameterisation and
evaluation of a Bayesian network for use in an ecological risk assessment. Environmental
Modelling & Software, 22(8), 1140-1152. http://dx.doi.org/10.1016/j.envsoft.2006.03.006
Rabelo, L., Eskandari, H., Shaalan, T., & Helal, M. (2007). Value chain analysis using hybrid
simulation and AHP. International Journal of Production Economics, 105(2), 536-547.
Rowbottom, U. (2004). Managing risk in global supply chains. Supply Chain Practice, 6(2),
SCOR (2006). Available at: www.scor.com
Shi, D. (2004). A review of enterprise supply chain risk management. Journal of systems
science and systems engineering, 13(2), 219-244. http://dx.doi.org/10.1007/s11518-006-0162-2
Svensson, G. (2000). A conceptual framework for the analysis of vulnerability in supply chains.
International Journal of Physical Distribution & Logistics Management, 30(9), 731-750.
Tang, C., & Tomlin, B. (2008). The power of flexibility for mitigating supply chain risks.
International Journal of Production Economics, 116(1), 12-27.
Tang, C. S. (2006). Perspectives in supply chain risk management. International journal of
production economics, 103(2), 451-488. http://dx.doi.org/10.1016/j.ijpe.2005.12.006
Tuncel, G., & Alpan, G. (2010). Risk assessment and management for supply chain networks:
A case study. Computers in industry, 61(3), 250-259.
Van Hoek, R. (2003). Are you ready? Risk readiness tactics for the supply chain. Logistics
Research Network.
Wagner, S.M., & Bode, C. (2006). An empirical investigation into supply chain vulnerability.
Journal of purchasing and supply management, 12(6), 301-312.
Wagner, S.M., & Neshat, N. (2010). Assessing the vulnerability of supply chains using graph
theory. International journal of production economics, 126(1), 121-129.
Wu, D., & Olson, D.L. (2008). Supply chain risk, simulation, and vendor selection.
International Journal of Production Economics, 114(2), 646-655.
Wu, T., Blackhurst, J., & Chidambaram, V. (2006). A model for inbound supply risk analysis.
Computers in industry, 57(4), 350-365. http://dx.doi.org/10.1016/j.compind.2005.11.001
Yan, H., Xu, B., & Wang, C. (2008). Study on the Optimization Measures of Reducing Supply
Chain Cooperation Risks. Paper presented at the Information Processing (ISIP), 2008
International Symposiums on. http://dx.doi.org/10.1109/ISIP.2008.16
Yongsheng, L., & Kun, Z. (2009). Study on Evaluation Index System for Supply Chain Risk.
Proceeding of the 2009 First International Conference on Information Science and
Engineering, pp. 4510-4513.

Thank you for copying data from http://www.arastirmax.com