Published Jun 24, 2019


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Jenifer Ramos-Ríos, MSc

Diego Fernando Manotas-Duque, PhD

Juan Carlos Osorio-Gómez, PhD



Objective: This study aims to propose a methodology that identifies and prioritizes the operational risk factors in a supply chain (SC) to provide a tool according to the process-based SC approach that is useful for risk assessment throughout the SC. Materials and methods: Risk identification was conducted by a scenario analysis, which linked the risk factors with the standard key performance indicators (KPIs) of the processes and logistics activities proposed by the supply chain operational reference model (SCORM o SCOR). These influence relationships were quantified using a proposed scale, and then, the risk factors were prioritized by the definition of their influence levels. This approach was applied to a real SC. Results and discussion: Twenty risk factors were clearly and effectively identified, analyzed and prioritized, and priority was given to those with the highest influence level, which can be understood as the risk factors that have a larger capacity to negatively affect SC performance. Conclusions: The methodology allows the identification of the most influential risk factors in a SC, and as it is based on a standard model, it fosters a collaborative analysis among its echelons. The main contributions of this paper are the risk identification by means of the KPIs of the SCOR model and the measurement of their influence levels, which is a new and useful feature for risk prioritization.


Supply chain risk, Operational risk, SCOR Model, Risk factor influence levelRiesgo en la cadena de suministro, Riesgo operacional, Modelo SCOR, Nivel de influencia de factor de riesgo

[1] T. Aven, “Risk assessment and risk management: Review of recent advances on their foundation,” Eur. J. Oper. Res., vol. 253, no. 1, pp. 1–13, Aug. 2016
[2] I. Heckmann, T. Comes, and S. Nickel, “A critical review on supply chain risk: Definition, measure and modeling,” Omega, vol. 52, pp. 119–132, Oct. 2015. doi: 10.1016/
[3] P. Singhal, G. Agarwal, and M. L. Mittal, “Supply chain risk management: Review, classification and future research directions,” Int. J. Bus. Sci. Appl. Manag., vol. 6, no. 3, pp. 15–42, 2011. Available:
[4] A. Mora Valencia, Riesgo operativo I: una revisión de la literatura (Borr. Admin., no. 46). Bogotá: CESA, 2011. Available:
[5] Y. Fan and M. Stevenson, “A review of supply chain risk management: Definition, theory, and research agenda,” Int. J. Phys. Distrib. Logist. Manag., vol. 48, no. 3, pp. 205–230, Jan. 2018. Available:
[6] S. Kumar, B. C. Boice, and M. J. Shepherd, “Risk Assessment and Operational Approaches to Manage Risk in Global Supply Chains,” Transp. J., vol. 52, no. 3, pp. 391–411, 2013.
[7] M. Han and J. Chen, “Managing operational risk in supply chain,” Int. Conf. Wireless Commun., Netw. Mobile Comput., WiCOM 2007, pp. 4919–4922.
[8] P. Boller, C. Grégorie, and T. Kawano, “Chapter 4. Operational risk,” in IAA Risk Book, 2016, pp. 1–19. Available:
[9] D. F. Manotas Duque, J. C. Osorio Gómez, and L. Rivera, “Operational risk management in third party logistics (3PL),” en Handbook of Research on Managerial Strategies for Achieving Optimal Performance in Industrial Processes, vol. I, USA: Business Science Reference, 2016, pp. 218– 239. doi: 10.4018/978-1-5225-0130-5.ch011 [10] M. Elmsalmi and W. Hachicha, “Risks prioritization in global supply networks using MICMAC method: A real case study,” in 2013 Int. Conf. Adv. Logist. Transp. ICALT 2013, pp. 394–399. doi: 10.1109/ICAdLT.2013.6568491
[11] J. Nan, J. Z. Huo, and H. H. Liu, “Supply chain purchasing risk evaluation of manufacturing enterprise based on Fuzzy-AHP method,” 2009 2nd Int. Conf. Intell. Comput. Technol. Autom. ICICTA 2009, vol. 3, no. 70772077, pp. 1001–1005. doi: 10.1109/ICICTA.2009.707
[12] P. K. Marhavilas, D. Koulouriotis, and V. Gemeni, “Risk analysis and assessment methodologies in the work sites: On a review, classification and comparative study of the scientific literature of the period 2000–2009,” J. Loss Prev. Process Ind., vol. 24, no. 5, pp. 477–523, Sep. 2011.
[13] A. Borghesi and B. Gaudenzi, Risk Management. How to Assess, Transfer and Communicate Critical Risks. Milan: Springer, 2013. doi: 10.1007/978-88-470-2531-8
[14] A. Mora Valencia, Una comparación de algunos métodos para cuantificar riesgo operativo (Borr. Admin., no. 39). Bogotá: CESA, 2010. Available:
[15] I. Kilubi, “Investigating current paradigms in supply chain risk management: A bibliometric study,” Bus. Process Manag. J., vol. 22, no. 4, pp. 662–692, 2016.
[16] Z. George A. and B. Ritchie, Supply Chain Risk. Springer Science + Business Media, 2009. doi: 10.1007/978-0-387-79934-6
[17] S. Nurmaya Musa, Supply Chain Risk Management: Identification, Evaluation and Mitigation Techniques (Linköping Stud. Sci. Technol. Diss., no. 1459). Swewden: Linköping University, 2012. Available:
[18] O. Tang and S. Nurmaya Musa, “Identifying risk issues and research advancements in supply chain risk management,” Int. J. Prod. Econ., vol. 133, no. 1, pp. 25–34, Sep. 2011.
[19] Supply Chain Council, Supply Chain Operations Reference Model Rev. 11.0. USA, 2012. Available:
[20] K. Rotaru, C. Wilkin, and A. Ceglowski, “Analysis of SCOR’s approach to supply chain risk management,” Int. J. Oper. Prod. Manag., vol. 34, no. 10, pp. 1246–1268, 2014.
[21] A. C. Cagliano, S. Grimaldi, and C. Rafele, “Enabling SCOR-Model Risk Management Process with a Theoretical Performance-Based Approach,” in Pioneering Solutions in Supply Chain Management: A Comprehensive Insight into Current Management Approaches, W. Kersten, T. Blecker, and C. Luthje, Eds. Berlin: Erich Schmidt Verlag, 2010, pp. 59–76. Available
[22] M. Abolghasemi, V. Khodakarami, and H. Tehranifard, “A new approach for supply chain risk management: Mapping SCOR into bayesian network,” J. Ind. Eng. Manag., vol. 8, no. 1, pp. 280–302, 2015.
How to Cite
Ramos-Ríos, J., Manotas-Duque, D. F., & Osorio-Gómez, J. C. (2019). Operational supply chain risk identification and prioritization from SCOR model. Ingenieria Y Universidad, 23(1).
Industrial and systems engineering

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