Abstract
Several techniques have been proposed in supply chain risk management to capture causality among risks in a network setting and prioritize risks regarding their network-wide propagation impact. However, these techniques might be unable to capture the Risk Network Value at Risk (RNVaR), the maximum risk exposure expected at a given confidence level for a given timeframe, associated with individual supply chain performance measures within a network setting. With an exclusive focus on using point estimates in existing techniques, there is a risk of overlooking tail distributions and ignoring critical risks. In this paper, we aim to address this research gap by introducing new risk metrics and a new process theoretically grounded in Bayesian Belief Network and Monte Carlo Simulation frameworks. Integrating these two techniques helps establish the RNVaR that is associated with different performance measures and the relative importance of individual risks for resource allocation. We demonstrate the application of the proposed process through a real case study in the telecommunications industry and compare the results of this study with existing approaches.
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References
Ackermann, F., Howick, S., Quigley, J., Walls, L., & Houghton, T. (2014). Systemic risk elicitation: Using causal maps to engage stakeholders and build a comprehensive view of risks. European Journal of Operational Research, 238(1), 290–299. https://doi.org/10.1016/j.ejor.2014.03.035
Adedipe, T., Shafiee, M., & Zio, E. (2020). Bayesian network modelling for the wind energy industry: An overview. Reliability Engineering & System Safety, 202. https://doi.org/10.1016/j.ress.2020.107053
Aloini, D., Dulmin, R., Mininno, V., & Ponticelli, S. (2012). Supply chain management: A review of implementation risks in the construction industry. Business Process Management Journal, 18(5), 735–761. https://doi.org/10.1108/14637151211270135
Aqlan, F., & Lam, S. S. (2015). A fuzzy-based integrated framework for supply chain risk assessment. International Journal of Production Economics, 161, 54–63. https://doi.org/10.1016/j.ijpe.2014.11.013
Aven, T. (2016a). Risk assessment and risk management: Review of recent advances on their foundation. European Journal of Operational Research, 253(1), 1–13. https://doi.org/10.1016/j.ejor.2015.12.023
Aven, T. (2016b). Supplementing quantitative risk assessments with a stage addressing the risk understanding of the decision maker. Reliability Engineering & System Safety, 152, 51–57. https://doi.org/10.1016/j.ress.2016.03.003
Aven, T. (2017). Improving risk characterisations in practical situations by highlighting knowledge aspects, with applications to risk matrices. Reliability Engineering & System Safety, 167, 42–48. https://doi.org/10.1016/j.ress.2017.05.006
Badurdeen, F., Shuaib, M., Wijekoon, K., Brown, A., Faulkner, W., Amundson, J., et al. (2014). Quantitative modeling and analysis of supply chain risks using Bayesian theory. Journal of Manufacturing Technology Management, 25(5), 631–654. https://doi.org/10.1108/JMTM-10-2012-0097
Berger, P. D., Gerstenfeld, A., & Zeng, A. Z. (2004). How many suppliers are best? A decision-analysis approach. Omega, 32(1), 9–15. https://doi.org/10.1016/j.omega.2003.09.001
Boutselis, P., & McNaught, K. (2019). Using Bayesian Networks to forecast spares demand from equipment failures in a changing service logistics context. International Journal of Production Economics, 209, 325–333. https://doi.org/10.1016/j.ijpe.2018.06.017
Cao, S., Bryceson, K., & Hine, D. (2019). An ontology-based Bayesian network modelling for supply chain risk propagation. Industrial Management and Data Systems, 119(8), 1691–1711. https://doi.org/10.1108/IMDS-01-2019-0032
Chaudhuri, A., Mohanty, B. K., & Singh, K. N. (2013). Supply chain risk assessment during new product development: A group decision making approach using numeric and linguistic data. International Journal of Production Research, 51(10), 2790–2804. https://doi.org/10.1080/00207543.2012.654922
Chiu, C.-H., & Choi, T.-M. (2016). Supply chain risk analysis with mean-variance models: A technical review. Annals of Operations Research, 240, 489–507. https://doi.org/10.1007/s10479-013-1386-4
Choi, T.-M. (2021). Risk analysis in logistics systems: A research agenda during and after the COVID-19 pandemic. Transportation Research Part e: Logistics and Transportation Review, 145, 102190. https://doi.org/10.1016/j.tre.2020.102190
Chopra, S., & Sodhi, M. S. (2004). Managing risk to avoid supply-chain breakdown. MIT Sloan Management Review, 46(1), 53–61. https://sloanreview.mit.edu/article/managing-risk-to-avoid-supplychain-breakdown/
Diabat, A., Govindan, K., & Panicker, V. V. (2012). Supply chain risk management and its mitigation in a food industry. International Journal of Production Research, 50(11), 3039–3050. https://doi.org/10.1080/00207543.2011.588619
Dogan, I., & Aydin, N. (2011). Combining Bayesian networks and total cost of ownership method for supplier selection analysis. Computers and Industrial Engineering, 61(4), 1072–1085. https://doi.org/10.1016/j.cie.2011.06.021
Dong, Q., & Cooper, O. (2016). An orders-of-magnitude AHP supply chain risk assessment framework. International Journal of Production Economics, 182, 144–156. https://doi.org/10.1016/j.ijpe.2016.08.021
Dubey, R., Gunasekaran, A., Childe, S. J., Papadopoulos, T., Blome, C., & Luo, Z. (2019). Antecedents of resilient supply chains: An empirical study. IEEE Transactions on Engineering Management, 66(1), 8–19. https://doi.org/10.1109/TEM.2017.2723042
Duijm, N. J. (2015). Recommendations on the use and design of risk matrices. Safety Science, 76, 21–31. https://doi.org/10.1016/j.ssci.2015.02.014
Fahimnia, B., Tang, C. S., Davarzani, H., & Sarkis, J. (2015). Quantitative models for managing supply chain risks: A review. European Journal of Operational Research, 247(1), 1–15. https://doi.org/10.1016/j.ejor.2015.04.034
Fan, H., Cheng, T. C. E., Li, G., & Lee, P. K. C. (2016). The effectiveness of supply chain risk information processing capability: An information processing perspective. IEEE Transactions on Engineering Management, 63(4), 414–425. https://doi.org/10.1109/TEM.2016.2598814
Fenton, N. E., Noguchi, T., & Neil, M. (2019). An extension to the noisy-or function to resolve the ‘Explaining Away’ deficiency for practical Bayesian network problems. IEEE Transactions on Knowledge and Data Engineering, 31(12), 2441–2445. https://doi.org/10.1109/TKDE.2019.2891680
Garvey, M. D., Carnovale, S., & Yeniyurt, S. (2015). An analytical framework for supply network risk propagation: A Bayesian network approach. European Journal of Operational Research, 243(2), 618–627. https://doi.org/10.1016/j.ejor.2014.10.034
Gaudenzi, B., & Borghesi, A. (2006). Managing risks in the supply chain using the AHP method. International Journal of Logistics Management, 17(1), 114–136. https://doi.org/10.1108/09574090610663464
Gaudenzi, B., & Qazi, A. (2021). Assessing project risks from a supply chain quality management (SCQM) perspective. International Journal of Quality & Reliability Management, 38(4), 908–931. https://doi.org/10.1108/IJQRM-01-2020-0011
GeNIe 2.0. The Decision Systems Laboratory, GeNIe and SMILE. Retrieved June 5, 2015, from http://genie.sis.pitt.edu/about.html
Ghadge, A., Dani, S., Chester, M., & Kalawsky, R. (2013). A systems approach for modelling supply chain risks. Supply Chain Management: An International Journal, 18(5), 523–538. https://doi.org/10.1108/scm-11-2012-0366
Heckmann, I., Comes, T., & Nickel, S. (2015). A critical review on supply chain risk—Definition, measure and modeling. Omega, 52, 119–132. https://doi.org/10.1016/j.omega.2014.10.004
Ho, W., Zheng, T., Yildiz, H., & Talluri, S. (2015). Supply chain risk management: A literature review. International Journal of Production Research, 53(16), 5031–5069. https://doi.org/10.1080/00207543.2015.1030467
Hossain, N. U. I., Amrani, S. E., Jaradat, R., Marufuzzaman, M., Buchanan, R., Rinaudo, C., et al. (2020). Modeling and assessing interdependencies between critical infrastructures using Bayesian network: A case study of inland waterway port and surrounding supply chain network. Reliability Engineering & System Safety, 198, 106898. https://doi.org/10.1016/j.ress.2020.106898
Hosseini, S., Al Khaled, A., & Sarder, M. D. (2016). A general framework for assessing system resilience using Bayesian networks: A case study of sulfuric acid manufacturer. Journal of Manufacturing Systems, 41, 211–227. https://doi.org/10.1016/j.jmsy.2016.09.006
Hosseini, S., & Barker, K. (2016). A Bayesian network model for resilience-based supplier selection. International Journal of Production Economics, 180, 68–87. https://doi.org/10.1016/j.ijpe.2016.07.007
Hosseini, S., & Ivanov, D. (2019). A new resilience measure for supply networks with the ripple effect considerations: A Bayesian network approach. Annals of Operations Research. https://doi.org/10.1007/s10479-019-03350-8
Hosseini, S., & Ivanov, D. (2020). Bayesian networks for supply chain risk, resilience and ripple effect analysis: A literature review. Expert Systems with Applications, 161, 113649. https://doi.org/10.1016/j.eswa.2020.113649
Hosseini, S., Ivanov, D., & Blackhurst, J. (2020a). Conceptualization and measurement of supply chain resilience in an open-system context. IEEE Transactions on Engineering Management. https://doi.org/10.1109/TEM.2020.3026465
Hosseini, S., Ivanov, D., & Dolgui, A. (2019a). Review of quantitative methods for supply chain resilience analysis. Transportation Research Part e: Logistics and Transportation Review, 125, 285–307. https://doi.org/10.1016/j.tre.2019.03.001
Hosseini, S., Ivanov, D., & Dolgui, A. (2020b). Ripple effect modelling of supplier disruption: Integrated Markov chain and dynamic Bayesian network approach. International Journal of Production Research, 58(11), 3284–3303. https://doi.org/10.1080/00207543.2019.1661538
Hosseini, S., Morshedlou, N., Ivanov, D., Sarder, M. D., Barker, K., & Khaled, A. A. (2019b). Resilient supplier selection and optimal order allocation under disruption risks. International Journal of Production Economics, 213, 124–137. https://doi.org/10.1016/j.ijpe.2019.03.018
Jensen, F. V., & Nielsen, T. D. (2007). Bayesian networks and decision graphs. Springer.
Jiang, M., & Lu, J. (2020). The analysis of maritime piracy occurred in Southeast Asia by using Bayesian network. Transportation Research Part e: Logistics and Transportation Review, 139, 101965. https://doi.org/10.1016/j.tre.2020.101965
Käki, A., Salo, A., & Talluri, S. (2015). Disruptions in supply networks: A probabilistic risk assessment approach. Journal of Business Logistics, 36(3), 273–287. https://doi.org/10.1111/jbl.12086
Kardes, I., Ozturk, A., Cavusgil, S. T., & Cavusgil, E. (2013). Managing global megaprojects: Complexity and risk management. International Business Review, 22(6), 905–917. https://doi.org/10.1016/j.ibusrev.2013.01.003
Kjaerulff, U. B., & Madsen, A. L. (2013). Bayesian networks and influence diagrams : A guide to construction and analysis. Springer.
Knemeyer, A. M., Zinn, W., & Eroglu, C. (2009). Proactive planning for catastrophic events in supply chains. Journal of Operations Management, 27(2), 141–153. https://doi.org/10.1016/j.jom.2008.06.002
Kull, T. J., & Talluri, S. (2008). A supply risk reduction model using integrated multicriteria decision making. IEEE Transactions on Engineering Management, 55(3), 409–419. https://doi.org/10.1109/TEM.2008.922627
Lee, C. K. M., Yeung, Y. C., & Hong, Z. (2012). An integrated framework for outsourcing risk management. Industrial Management & Data Systems, 112(4), 541–558. https://doi.org/10.1108/02635571211225477
Li, L., Wang, J., Leung, H., & Zhao, S. (2012). A Bayesian method to mine spatial data sets to evaluate the vulnerability of human beings to catastrophic risk. Risk Analysis, 32(6), 1072–1092. https://doi.org/10.1111/j.1539-6924.2012.01790.x
Lin, P., Neil, M., & Fenton, N. (2020). Improved high dimensional discrete Bayesian network inference using triplet region construction. Journal of Artificial Intelligence Research, 69, 231–295. https://doi.org/10.1613/jair.1.12198
Liu, Z., Lai, M., Zhou, T., & Zhou, Y. (2009). A supply chain risk assessment model based on multistage influence diagram. In 6th International conference on service systems and service management, 72–75. IEEE.
Lockamy, A., & McCormack, K. (2010). Analysing risks in supply networks to facilitate outsourcing decisions. International Journal of Production Research, 48(2), 593–611. https://doi.org/10.1080/00207540903175152
Lockamy, A., & McCormack, K. (2012). Modeling supplier risks using Bayesian networks. Industrial Management & Data Systems, 112(2), 313–333. https://doi.org/10.1108/02635571211204317
Luo, L., Zhang, L., & Wu, G. (2020). Bayesian belief network-based project complexity measurement considering causal relationships. Journal of Civil Engineering and Management, 26(2), 200–215. https://doi.org/10.3846/jcem.2020.11930
Manuj, I., & Mentzer, J. T. (2008). Global supply chain risk management. Journal of Business Logistics, 29(1), 133–155. https://doi.org/10.1002/j.2158-1592.2008.tb00072.x
Mengshoel, O. J., Poll, S., & Kurtoglu, T. (2009). Developing large-scale Bayesian networks by composition: fault diagnosis of electrical power systems in aircraft and spacecraft. In the IJCAI-09 Workshop on Self-* and Autonomous Systems: reasoning and integration challenges (SAS-09), Pasadena, California, USA.
Mogre, R., Talluri, S., & Amico, F. D. (2016). A decision framework to mitigate supply chain risks: An application in the offshore-wind industry. IEEE Transactions on Engineering Management, 63(3), 316–325. https://doi.org/10.1109/TEM.2016.2567539
Nadkarni, S., & Shenoy, P. P. (2004). A causal mapping approach to constructing Bayesian networks. Decision Support Systems, 38(2), 259–281. https://doi.org/10.1016/S0167-9236(03)00095-2
Nagurney, A., & Matsypura, D. (2005). Global supply chain network dynamics with multicriteria decision-making under risk and uncertainty. Transportation Research Part e: Logistics and Transportation Review, 41(6), 585–612. https://doi.org/10.1016/j.tre.2005.07.002
Neil, M., Chen, X., & Fenton, N. (2012). Optimizing the calculation of conditional probability tables in hybrid Bayesian networks using binary factorization. IEEE Transactions on Knowledge and Data Engineering, 24(7), 1306–1312. https://doi.org/10.1109/TKDE.2011.87
Neil, M., Fenton, N., & Nielson, L. (2000). Building large-scale Bayesian networks. Knowledge Engineering Review, 15(3), 257–284. https://doi.org/10.1017/s0269888900003039
Nepal, B., & Yadav, O. P. (2015). Bayesian belief network-based framework for sourcing risk analysis during supplier selection. International Journal of Production Research, 53(20), 6114–6135. https://doi.org/10.1080/00207543.2015.1027011
Norrington, L., Quigley, J., Russell, A., & Van der Meer, R. (2008). Modelling the reliability of search and rescue operations with Bayesian belief networks. Reliability Engineering & System Safety, 93(7), 940–949. https://doi.org/10.1016/j.ress.2007.03.006
Ojha, R., Ghadge, A., Tiwari, M. K., & Bititci, U. S. (2018). Bayesian network modelling for supply chain risk propagation. International Journal of Production Research, 56(17), 5795–5819. https://doi.org/10.1080/00207543.2018.1467059
Pavlov, A., Ivanov, D., Dolgui, A., & Sokolov, B. (2018). Hybrid fuzzy-probabilistic approach to supply chain resilience assessment. IEEE Transactions on Engineering Management, 65(2), 303–315. https://doi.org/10.1109/TEM.2017.2773574
Pitchforth, J., & Mengersen, K. (2013). A proposed validation framework for expert elicited Bayesian networks. Expert Systems with Applications, 40(1), 162–167. https://doi.org/10.1016/j.eswa.2012.07.026
Qazi, A., & Akhtar, P. (2020). Risk matrix driven supply chain risk management: Adapting risk matrix based tools to modelling interdependent risks and risk appetite. Computers & Industrial Engineering, 139. https://doi.org/10.1016/j.cie.2018.08.002
Qazi, A., Dickson, A., Quigley, J., & Gaudenzi, B. (2018). Supply chain risk network management: A Bayesian belief network and expected utility based approach for managing supply chain risks. International Journal of Production Economics, 196, 24–42. https://doi.org/10.1016/j.ijpe.2017.11.008
Qazi, A., Quigley, J., Dickson, A., & Ekici, ŞÖ. (2017). Exploring dependency based probabilistic supply chain risk measures for prioritising interdependent risks and strategies. European Journal of Operational Research, 259(1), 189–204. https://doi.org/10.1016/j.ejor.2016.10.023
Qazi, A., Quigley, J., Dickson, A., & Kirytopoulos, K. (2016). Project Complexity and Risk Management (ProCRiM): Towards modelling project complexity driven risk paths in construction projects. International Journal of Project Management, 34(7), 1183–1198. https://doi.org/10.1016/j.ijproman.2016.05.008
Qazi, A., & Simsekler, M. C. E. (2022). Worst Expected Best method for assessment of probabilistic network expected value at risk: Application in supply chain risk management. International Journal of Quality & Reliability Management, 39(1), 155–175. https://doi.org/10.1108/IJQRM-07-2020-0238
Rodgers, M., & Oppenheim, R. (2019). Ishikawa diagrams and Bayesian belief networks for continuous improvement applications. The TQM Journal, 31(3), 294–318. https://doi.org/10.1108/TQM-11-2018-0184
Rodgers, M., & Singham, D. (2020). A framework for assessing disruptions in a clinical supply chain using Bayesian belief networks. Journal of Pharmaceutical Innovation, 15, 467–481. https://doi.org/10.1007/s12247-019-09396-2
Ruiz-Benítez, R., López, C., & Real, J. C. (2018). The lean and resilient management of the supply chain and its impact on performance. International Journal of Production Economics, 203, 190–202. https://doi.org/10.1016/j.ijpe.2018.06.009
Sarker, S., Engwall, M., Trucco, P., & Feldmann, A. (2016). Internal visibility of external supplier risks and the dynamics of risk management silos. IEEE Transactions on Engineering Management, 63(4), 451–461. https://doi.org/10.1109/TEM.2016.2596144
Segal, S. (2011). Corporate value of enterprise risk management: The next step in business management. Wiley.
Sener, A., Barut, M., Dag, A., & Yildirim, M. B. (2021). Impact of commitment, information sharing, and information usage on supplier performance: A Bayesian belief network approach. Annals of Operations Research, 303, 125–158. https://doi.org/10.1007/s10479-019-03504-8
Shad, M. K., Lai, F.-W., Fatt, C. L., Klemeš, J. J., & Bokhari, A. (2019). Integrating sustainability reporting into enterprise risk management and its relationship with business performance: A conceptual framework. Journal of Cleaner Production, 208, 415–425. https://doi.org/10.1016/j.jclepro.2018.10.120
Sharma, S., & Routroy, S. (2016). Modeling information risk in supply chain using Bayesian networks. Journal of Enterprise Information Management, 29(2), 238–254. https://doi.org/10.1108/JEIM-03-2014-0031
Sharma, S. K., & Sharma, S. (2015). Developing a Bayesian network model for supply chain risk assessment. Supply Chain Forum: An International Journal, 16(4), 50–72. https://doi.org/10.1080/16258312.2015.11728693
Simsekler, M. C. E., & Qazi, A. (2020). Adoption of a data-driven Bayesian belief network investigating organizational factors that influence patient safety. Risk Analysis. https://doi.org/10.1111/risa.13610
Sinha, P. R., Whitman, L. E., & Malzahn, D. (2004). Methodology to mitigate supplier risk in an aerospace supply chain. Supply Chain Management: An International Journal, 9(2), 154–168. https://doi.org/10.1108/13598540410527051
SRA. (2015). Society for risk analysis glossary. https://www.sra.org/risk-analysis-introduction/risk-analysis-glossary/.
Sweeting, P. (2017). Financial enterprise risk management. Cambridge University Press.
Talluri, S., Kull, T. J., Yildiz, H., & Yoon, J. (2013). Assessing the efficiency of risk mitigation strategies in supply chains. Journal of Business Logistics, 34(4), 253–269. https://doi.org/10.1111/jbl.12025
Tang, C. S., Zimmerman, J. D., & Nelson, J. I. (2009). Managing new product development and supply chain risks: The Boeing 787 case. Supply Chain Forum: An International Journal, 10(2), 74–86. https://doi.org/10.1080/16258312.2009.11517219
Tokdemir, O. B., Erol, H., & Dikmen, I. (2019). Delay risk assessment of repetitive construction projects using line-of-balance scheduling and Monte Carlo simulation. Journal of Construction Engineering and Management, 145(2), 04018132. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001595
Tong, R., Cheng, M., Zhang, L., Liu, M., Yang, X., Li, X., et al. (2018). The construction dust-induced occupational health risk using Monte-Carlo simulation. Journal of Cleaner Production, 184, 598–608. https://doi.org/10.1016/j.jclepro.2018.02.286
Trkman, P., & McCormack, K. (2009). Supply chain risk in turbulent environments—A conceptual model for managing supply chain network risk. International Journal of Production Economics, 119(2), 247–258. https://doi.org/10.1016/j.ijpe.2009.03.002
Tse, Y. K., Zhang, M., Tan, K. H., Pawar, K., & Fernandes, K. (2019). Managing quality risk in supply chain to drive firm’s performance: The roles of control mechanisms. Journal of Business Research, 97, 291–303. https://doi.org/10.1016/j.jbusres.2018.01.029
Tummala, R., & Schoenherr, T. (2011). Assessing and managing risks using the Supply Chain Risk Management Process (SCRMP). Supply Chain Management: An International Journal, 16(6), 474–483. https://doi.org/10.1108/13598541111171165
Wan, C., Yan, X., Zhang, D., Qu, Z., & Yang, Z. (2019). An advanced fuzzy Bayesian-based FMEA approach for assessing maritime supply chain risks. Transportation Research Part e: Logistics and Transportation Review, 125, 222–240. https://doi.org/10.1016/j.tre.2019.03.011
Werner, C., Bedford, T., Cooke, R. M., Hanea, A. M., & Morales-Nápoles, O. (2017). Expert judgement for dependence in probabilistic modelling: A systematic literature review and future research directions. European Journal of Operational Research, 258(3), 801–819. https://doi.org/10.1016/j.ejor.2016.10.018
Wilson, M. C. (2007). The impact of transportation disruptions on supply chain performance. Transportation Research Part e: Logistics and Transportation Review, 43(4), 295–320. https://doi.org/10.1016/j.tre.2005.09.008
Wu, T., Huang, S., Blackhurst, J., Zhang, X., & Wang, S. (2013). Supply chain risk management: An agent-based simulation to study the impact of retail stockouts. IEEE Transactions on Engineering Management, 60(4), 676–686. https://doi.org/10.1109/TEM.2012.2190986
Zhang, G., Thai, V. V., Law, A.W.-K., Yuen, K. F., Loh, H. S., & Zhou, Q. (2020). Quantitative risk assessment of seafarers’ nonfatal injuries due to occupational accidents based on bayesian network modeling. Risk Analysis, 40(1), 8–23. https://doi.org/10.1111/risa.13374
Zhang, Y. (2016). Selecting risk response strategies considering project risk interdependence. International Journal of Project Management, 34(5), 819–830. https://doi.org/10.1016/j.ijproman.2016.03.001
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Qazi, A., Simsekler, M.C.E. & Formaneck, S. Supply chain risk network value at risk assessment using Bayesian belief networks and Monte Carlo simulation. Ann Oper Res 322, 241–272 (2023). https://doi.org/10.1007/s10479-022-04598-3
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DOI: https://doi.org/10.1007/s10479-022-04598-3