Contingency plan selection under interdependent risks

Managing supply chain risks (SCRs) has become an increasingly strategic key factor over the last decade, aimed at pursuing and maintaining business success. These types of risks clearly pose an important challenge to managers nowadays, and evaluating uncertainty affecting business scenarios is crucial. Indeed, COVID-19 has been dangerously affecting supply chains of global manufacturers, and is indicated as a main trigger cause of supply chain disruptions for a huge number of enterprises. Major effects derived from epidemic outbreaks on supply chains should be further adequately investigated since enterprises have been adopting poor risk management plans [1] to face them. Many companies, for instance, have been assuming a passive attitude towards the management of pandemic effects, simply waiting for the situation to come back to normality at hopefully short notice. On the other side, those companies that are more proactively reacting to the pandemic have been encountering countless difficulties in implementing risk management plans at operational levels [2]. Given these preliminaries, the present contribution is aimed at proposing a way for managing risks due to COVID-19. The main objectives of the present contribution can be formalised as follows: 1. analysing critical supply chain risks and related interdependence relationships to establish priorities on mitigation/prevention actions and most influential risks; 2. proposing a structured method capable to get the vector of risks’ weights and ease the selection of the most suitable contingency strategy on the basis of companies’ needs. These objectives are herein addressed by means of a Multi-Criteria Decision-Making (MCDM) approach based on the use of the Analytic Network Process (ANP), suggested to analyse and weight risks by taking into account relations of dependence existing among the same risks and effects. Results will be formalised in the field of automotive industry as offering a significant input for the process of contingency strategy selection while simultaneously considering uncertainty affecting evaluations on the basis of the specific business context features

A strategic approach to safeguard global supply chains against COVID-19 disruptions

The global system of supply chains has been dramatically disrupted over the last years due to the outbreak of the COVID-19 pandemic. In these current chal lenging times, this paper proposes a methodological approach for managing dependence and uncertainty in dynamic industrial scenarios. A detailed study of epidemic effects is carried out according to an operational management-based perspective. We proceed by analyzing connections among effects and risks potentially leading to significant supply chain disturbances through a multicri teria approach. Risks and effects are weighted by applying the Analytic Network Process (ANP). Weighted risks are then assumed as criteria for selecting the most suitable contingency strategy. To this aim, the Fuzzy Technique for Order of Pref erence by Similarity to Ideal Solution (FTOPSIS) is able to rank a set of strategies by addressing and quantifying uncertainty. A case study on the sector of the automotive industry is implemented to validate the proposed methodological approach

Designing supplier selection strategies under COVID-19 constraints for industrial environments

COVID-19 has been impacting worldwide supply chains causing interruption, closure of production and distribution. This impact has been drastic on the supplier side and, as a consequence of disruptions, strong reductions of production have been estimated. Such a circumstance forces companies to propose innovative best practices of supply chain risk management aimed at facing vulnerability generated by COVID-19 and pursuing industrial improvements in manufacturing and production environments. As a part of supply chain strategy, supplier selection criteria should be revised to include pandemic-related risks. This article aims to propose an answer to such a problem. In detail, a comprehensive tool designed as a hybrid combination of multi-criteria decision-making (MCDM) methods is suggested to manage important stages connected to the production development cycle and to provide companies with a structured way to rank risks and easily select their suppliers. The main criteria of analysis will be first identified from the existent literature. Risks related to COVID-19 will be then analysed in order to elaborate a comprehensive list of potential risks in the field of interest. The Best Worst Method (BWM) will be first used to calculate criteria weights. The Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (FTOPSIS) will be then applied to rank and prioritize risks affecting suppliers. The effectiveness of the approach will be tested through a case study in the sector of automotive industry. The applicability of the designed MCDM framework can be extended also to other industrial sectors of interest

Assessing Supply Chain Risks in the Automotive Industry through a Modified MCDM-Based FMECA

[EN] Supply chains are complex networks that receive assiduous attention in the literature. Like any complex network, a supply chain is subject to a wide variety of risks that can result in significant economic losses and negative impacts in terms of image and prestige for companies. In circumstances of aggressive competition among companies, effective management of supply chain risks (SCRs) is crucial, and is currently a very active field of research. Failure Mode, Effects and Criticality Analysis (FMECA) has been recently extended to SCR identification and prioritization, aiming at reducing potential losses caused by lack of risk control. This article has a twofold objective. First, SCR assessment is investigated, and a comprehensive list of specific risks related to the automotive industry is compiled to extend the set of most commonly considered risks. Second, an alternative way of calculating the Risk Priority Number (RPN) is proposed within the FMECA framework by means of an integrated Multi-Criteria Decision-Making (MCDM) approach. We give a new calculation procedure by making use of the Analytic Hierarchy Process (AHP) to derive factors weights, and then the fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL) to evaluate the new factor of "dependence" among the risks. The developed joint analysis constitutes a risk analysis support tool for criticality in systems engineering. The approach also deals with uncertainty and vagueness associated with input data through the use of fuzzy numbers. The results obtained from a relevant case study in the automotive industry showcase the effectiveness of this approach, which brings important value to those companies: When planning interventions of prevention/mitigation, primary importance should be given to (1) supply chain disruptions due to natural disasters; (2) manufacturing facilities, human resources, policies and breakdown processes; and (3) inefficient transport.Mzougui, I.; Carpitella, S.; Certa, A.; El Felsoufi, Z.; Izquierdo Sebastián, J. (2020). Assessing Supply Chain Risks in the Automotive Industry through a Modified MCDM-Based FMECA. Processes. 8(5):1-22. https://doi.org/10.3390/pr8050579S12285Tian, Q., & Guo, W. (2019). Reconfiguration of manufacturing supply chains considering outsourcing decisions and supply chain risks. Journal of Manufacturing Systems, 52, 217-226. doi:10.1016/j.jmsy.2019.04.005Wu, Y., Jia, W., Li, L., Song, Z., Xu, C., & Liu, F. (2019). Risk assessment of electric vehicle supply chain based on fuzzy synthetic evaluation. Energy, 182, 397-411. doi:10.1016/j.energy.2019.06.007Garvey, M. D., & Carnovale, S. (2020). The rippled newsvendor: A new inventory framework for modeling supply chain risk severity in the presence of risk propagation. International Journal of Production Economics, 228, 107752. doi:10.1016/j.ijpe.2020.107752Kern, D., Moser, R., Hartmann, E., & Moder, M. (2012). Supply risk management: model development and empirical analysis. International Journal of Physical Distribution & Logistics Management, 42(1), 60-82. doi:10.1108/09600031211202472Wang, H., Gu, T., Jin, M., Zhao, R., & Wang, G. (2018). The complexity measurement and evolution analysis of supply chain network under disruption risks. Chaos, Solitons & Fractals, 116, 72-78. doi:10.1016/j.chaos.2018.09.018Ghoshal, S. (1987). Global strategy: An organizing framework. Strategic Management Journal, 8(5), 425-440. doi:10.1002/smj.4250080503Schoenherr, T., Rao Tummala, V. M., & Harrison, T. P. (2008). Assessing supply chain risks with the analytic hierarchy process: Providing decision support for the offshoring decision by a US manufacturing company. Journal of Purchasing and Supply Management, 14(2), 100-111. doi:10.1016/j.pursup.2008.01.008Xu, M., Cui, Y., Hu, M., Xu, X., Zhang, Z., Liang, S., & Qu, S. (2019). Supply chain sustainability risk and assessment. Journal of Cleaner Production, 225, 857-867. doi:10.1016/j.jclepro.2019.03.307Tang, C., & Tomlin, B. (2008). The power of flexibility for mitigating supply chain risks. International Journal of Production Economics, 116(1), 12-27. doi:10.1016/j.ijpe.2008.07.008Ghadge, A., Dani, S., & Kalawsky, R. (2012). Supply chain risk management: present and future scope. The International Journal of Logistics Management, 23(3), 313-339. doi:10.1108/09574091211289200Ho, W. (2008). Integrated analytic hierarchy process and its applications – A literature review. European Journal of Operational Research, 186(1), 211-228. doi:10.1016/j.ejor.2007.01.004Lolli, F., Ishizaka, A., Gamberini, R., & Rimini, B. (2017). A multicriteria framework for inventory classification and control with application to intermittent demand. Journal of Multi-Criteria Decision Analysis, 24(5-6), 275-285. doi:10.1002/mcda.1620Żak, J., & Kruszyński, M. (2015). Application of AHP and ELECTRE III/IV Methods to Multiple Level, Multiple Criteria Evaluation of Urban Transportation Projects. Transportation Research Procedia, 10, 820-830. doi:10.1016/j.trpro.2015.09.035Zaidan, A. A., Zaidan, B. B., Al-Haiqi, A., Kiah, M. L. M., Hussain, M., & Abdulnabi, M. (2015). Evaluation and selection of open-source EMR software packages based on integrated AHP and TOPSIS. Journal of Biomedical Informatics, 53, 390-404. doi:10.1016/j.jbi.2014.11.012Chang, K.-H., Chang, Y.-C., & Lee, Y.-T. (2014). Integrating TOPSIS and DEMATEL Methods to Rank the Risk of Failure of FMEA. International Journal of Information Technology & Decision Making, 13(06), 1229-1257. doi:10.1142/s0219622014500758Nazeri, A., & Naderikia, R. (2017). A new fuzzy approach to identify the critical risk factors in maintenance management. The International Journal of Advanced Manufacturing Technology, 92(9-12), 3749-3783. doi:10.1007/s00170-017-0222-4Liu, H.-C., You, J.-X., Lin, Q.-L., & Li, H. (2014). Risk assessment in system FMEA combining fuzzy weighted average with fuzzy decision-making trial and evaluation laboratory. International Journal of Computer Integrated Manufacturing, 28(7), 701-714. doi:10.1080/0951192x.2014.900865Muhammad, M. N., & Cavus, N. (2017). Fuzzy DEMATEL method for identifying LMS evaluation criteria. Procedia Computer Science, 120, 742-749. doi:10.1016/j.procs.2017.11.304Chang, K.-H., & Cheng, C.-H. (2009). Evaluating the risk of failure using the fuzzy OWA and DEMATEL method. Journal of Intelligent Manufacturing, 22(2), 113-129. doi:10.1007/s10845-009-0266-xMarch, J. G., & Shapira, Z. (1987). Managerial Perspectives on Risk and Risk Taking. Management Science, 33(11), 1404-1418. doi:10.1287/mnsc.33.11.1404Blos, M. F., Quaddus, M., Wee, H. M., & Watanabe, K. (2009). Supply chain risk management (SCRM): a case study on the automotive and electronic industries in Brazil. Supply Chain Management: An International Journal, 14(4), 247-252. doi:10.1108/13598540910970072Hallikas, 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. doi:10.1016/j.ijpe.2004.02.007Jüttner, U. (2005). Supply chain risk management. The International Journal of Logistics Management, 16(1), 120-141. doi:10.1108/09574090510617385Gary Teng, S., Ho, S. M., Shumar, D., & Liu, P. C. (2006). Implementing FMEA in a collaborative supply chain environment. International Journal of Quality & Reliability Management, 23(2), 179-196. doi:10.1108/02656710610640943Jüttner, 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. doi:10.1080/13675560310001627016Sodhi, M. S., Son, B.-G., & Tang, C. S. (2011). Researchers’ Perspectives on Supply Chain Risk Management. Production and Operations Management, 21(1), 1-13. doi:10.1111/j.1937-5956.2011.01251.xWagner, S. M., & Bode, C. (2006). An empirical investigation into supply chain vulnerability. Journal of Purchasing and Supply Management, 12(6), 301-312. doi:10.1016/j.pursup.2007.01.004Manuj, I., & Mentzer, J. T. (2008). Global supply chain risk management strategies. International Journal of Physical Distribution & Logistics Management, 38(3), 192-223. doi:10.1108/09600030810866986Bevilacqua, M., Ciarapica, F. E., Marcucci, G., & Mazzuto, G. (2019). Fuzzy cognitive maps approach for analysing the domino effect of factors affecting supply chain resilience: a fashion industry case study. International Journal of Production Research, 58(20), 6370-6398. doi:10.1080/00207543.2019.1680893Bevilacqua, M., Ciarapica, F. E., Marcucci, G., & Mazzuto, G. (2018). Conceptual model for analysing domino effect among concepts affecting supply chain resilience. Supply Chain Forum: An International Journal, 19(4), 282-299. doi:10.1080/16258312.2018.1537504Hsieh, C. Y., Wee, H. M., & Chen, A. (2016). Resilient logistics to mitigate supply chain uncertainty: A case study of an automotive company. Scientia Iranica, 23(5), 2287-2296. doi:10.24200/sci.2016.3957Lotfi, M., & Saghiri, S. (2018). Disentangling resilience, agility and leanness. Journal of Manufacturing Technology Management, 29(1), 168-197. doi:10.1108/jmtm-01-2017-0014Marasova, D., Andrejiova, M., & Grincova, A. (2017). Applying the Heuristic to the Risk Assessment within the Automotive Industry Supply Chain. Open Engineering, 7(1), 43-49. doi:10.1515/eng-2017-0007Pandey, A. K., & Sharma, R. K. (2017). FMEA-based interpretive structural modelling approach to model automotive supply chain risk. International Journal of Logistics Systems and Management, 27(4), 395. doi:10.1504/ijlsm.2017.085221Vujović, A., Đorđević, A., Gojković, R., & Borota, M. (2017). ABC Classification of Risk Factors in Production Supply Chains with Uncertain Data. Mathematical Problems in Engineering, 2017, 1-11. doi:10.1155/2017/4931797Aven, T. (2012). The risk concept—historical and recent development trends. Reliability Engineering & System Safety, 99, 33-44. doi:10.1016/j.ress.2011.11.006Chang, D., & Paul Sun, K. (2009). Applying DEA to enhance assessment capability of FMEA. International Journal of Quality & Reliability Management, 26(6), 629-643. doi:10.1108/02656710910966165Chin, K.-S., Wang, Y.-M., Ka Kwai Poon, G., & Yang, J.-B. (2009). Failure mode and effects analysis using a group-based evidential reasoning approach. Computers & Operations Research, 36(6), 1768-1779. doi:10.1016/j.cor.2008.05.002Chang, B., Chang, C.-W., & Wu, C.-H. (2011). Fuzzy DEMATEL method for developing supplier selection criteria. Expert Systems with Applications, 38(3), 1850-1858. doi:10.1016/j.eswa.2010.07.114Zhang, Z., & Chu, X. (2011). Risk prioritization in failure mode and effects analysis under uncertainty. Expert Systems with Applications, 38(1), 206-214. doi:10.1016/j.eswa.2010.06.046Zhang, Y. F., Zhou, R. B., Yang, J. M., & Zhang, Z. (2014). Application of FTA-FMEA Method in Fault Diagnosis of Tracked Vehicle. Advanced Materials Research, 940, 112-115. doi:10.4028/www.scientific.net/amr.940.112Liu, H.-C., Liu, L., Bian, Q.-H., Lin, Q.-L., Dong, N., & Xu, P.-C. (2011). Failure mode and effects analysis using fuzzy evidential reasoning approach and grey theory. Expert Systems with Applications, 38(4), 4403-4415. doi:10.1016/j.eswa.2010.09.110Liu, H.-C., Liu, L., Liu, N., & Mao, L.-X. (2012). Risk evaluation in failure mode and effects analysis with extended VIKOR method under fuzzy environment. Expert Systems with Applications, 39(17), 12926-12934. doi:10.1016/j.eswa.2012.05.031Liu, Y., Fan, Z.-P., Yuan, Y., & Li, H. (2014). A FTA-based method for risk decision-making in emergency response. Computers & Operations Research, 42, 49-57. doi:10.1016/j.cor.2012.08.015Kutlu, A. C., & Ekmekçioğlu, M. (2012). Fuzzy failure modes and effects analysis by using fuzzy TOPSIS-based fuzzy AHP. Expert Systems with Applications, 39(1), 61-67. doi:10.1016/j.eswa.2011.06.044Chang, C., Liu, P., & Wei, C. (2001). Failure mode and effects analysis using grey theory. Integrated Manufacturing Systems, 12(3), 211-216. doi:10.1108/09576060110391174Bevilacqua, M., Braglia, M., & Gabbrielli, R. (2000). Monte Carlo simulation approach for a modified FMECA in a power plant. Quality and Reliability Engineering International, 16(4), 313-324. doi:10.1002/1099-1638(200007/08)16:43.0.co;2-uLai, G., Debo, L. G., & Sycara, K. (2009). Sharing inventory risk in supply chain: The implication of financial constraint. Omega, 37(4), 811-825. doi:10.1016/j.omega.2008.06.003Carpitella, S., Certa, A., Izquierdo, J., & La Fata, C. M. (2018). A combined multi-criteria approach to support FMECA analyses: A real-world case. Reliability Engineering & System Safety, 169, 394-402. doi:10.1016/j.ress.2017.09.017Mahmoudi, S., Jalali, A., Ahmadi, M., Abasi, P., & Salari, N. (2019). Identifying critical success factors in Heart Failure Self-Care using fuzzy DEMATEL method. Applied Soft Computing, 84, 105729. doi:10.1016/j.asoc.2019.10572

Assessing supply chain risks in the automotive industry through a modified MCDM-based FMECA

Supply chains are complex networks that receive assiduous attention in the literature. Like any complex network, a supply chain is subject to a wide variety of risks that can result in significant economic losses and negative impacts in terms of image and prestige for companies. In circumstances of aggressive competition among companies, effective management of supply chain risks (SCR) is crucial, and is currently a very active field of research. Failure Mode Effects and Criticality Analysis (FMECA) has been recently extended to SCR identification and prioritization, aiming at reducing potential losses caused by lack of risk control. This article has a twofold objective. First, SCR assessment is investigated, and a comprehensive list of specific risks related to the automotive industry is compiled to extend the set of most commonly considered risks. Second, an alternative way of calculating the risk priority number (RPN) is proposed within the FMECA framework by means of an integrated multi-criteria decision-making (MCDM) approach. We give a new calculation procedure by making use of the Analytic Hierarchy Process (AHP) to derive factors weights, and then the fuzzy DEcision-MAking Trial and Evaluation Laboratory (DEMATEL) to evaluate the new factor of “dependence” among risks. The developed joint analysis constitutes a risk analysis support tool for criticality in systems engineering. The approach also deals with uncertainty and vagueness associated to input data through the use of fuzzy numbers. The results obtained from a relevant case study in the automotive industry showcase the effectiveness of this approach, which brings important value to those companies: when planning interventions of prevention/mitigation, primary importance should be given to 1) supply chain disruptions due to natural disasters, 2) manufacturing facilities, human resources, policies and breakdown processes, and 3) inefficient transport

Assessing Supply Chain Risks in the Automotive Industry through a Modified MCDM-Based FMECA

Supply chains are complex networks that receive assiduous attention in the literature. Like any complex network, a supply chain is subject to a wide variety of risks that can result in significant economic losses and negative impacts in terms of image and prestige for companies. In circumstances of aggressive competition among companies, effective management of supply chain risks (SCRs) is crucial, and is currently a very active field of research. Failure Mode, Effects and Criticality Analysis (FMECA) has been recently extended to SCR identification and prioritization, aiming at reducing potential losses caused by lack of risk control. This article has a twofold objective. First, SCR assessment is investigated, and a comprehensive list of specific risks related to the automotive industry is compiled to extend the set of most commonly considered risks. Second, an alternative way of calculating the Risk Priority Number (RPN) is proposed within the FMECA framework by means of an integrated Multi-Criteria Decision-Making (MCDM) approach. We give a new calculation procedure by making use of the Analytic Hierarchy Process (AHP) to derive factors weights, and then the fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL) to evaluate the new factor of “dependence” among the risks. The developed joint analysis constitutes a risk analysis support tool for criticality in systems engineering. The approach also deals with uncertainty and vagueness associated with input data through the use of fuzzy numbers. The results obtained from a relevant case study in the automotive industry showcase the effectiveness of this approach, which brings important value to those companies: When planning interventions of prevention/mitigation, primary importance should be given to (1) supply chain disruptions due to natural disasters; (2) manufacturing facilities, human resources, policies and breakdown processes; and (3) inefficient transport

Contingency plan selection under interdependent risks

Mzougui, I.; Carpitella, S.; Izquierdo Sebastián, J. (2021). Contingency plan selection under interdependent risks. Universitat Politècnica de València. 111-116. http://hdl.handle.net/10251/182740S11111

Multi-criteria risk classification to enhance complex supply networks performance

[EN] Management of complex supply networks is a fundamental business topic today. Especially in the presence of many and diverse stakeholders, identifying and assessing those risks having a potential negative impact on the performance of supply processes is of utmost importance and, as a result, implementing focused risk management actions is a current lively field of research. The possibility of supporting Supply Chain Risks Management (SCRM) is herein explored from a Multi-Criteria Decision-Making (MCDM)-based perspective. The sorting method ELimination Et Choix Traduisant la REalite (ELECTRE) TRI is proposed as a structural procedure to classify Supply Chain Risks (SCRs) into proper risk classes expressing priority of intervention so as to ease the implementation of prevention and protection measures. This approach is intended to offer structured management insights by means of an immediate identification of the most highly critical risks in a wide set of previously identified SCRs. A real-world case study in the field of the automotive industry is implemented to show the applicability and usefulness of the approach.Carpitella, S.; Mzougui, I.; Izquierdo Sebastián, J. (2022). Multi-criteria risk classification to enhance complex supply networks performance. OPSEARCH. 59(3):769-785. https://doi.org/10.1007/s12597-021-00568-876978559

A risk evaluation framework for the best maintenance strategy: the case of a marine salt manufacture firm

This paper intends to contribute with a multi-criteria decision-making (MCDM) framework to support risk evaluation for maintenance activities carried out on critical systems in industry. We propose to first select the best maintenance strategy tailored to companies\u2019 requirements and systems\u2019 features, and second to perform a risk prioritisation aimed at highlighting priorities of intervention. The Analytic Network Process (ANP) is suggested to select the maintenance policy representing the best trade-off considering the complex and varied interdependencies among a diversity of clustered elements characterising the system. Then, the main risks related to the interventions associated to the selected maintenance policy are ranked using the ELimination Et Choix Traduisant la REalit\ue9 III (ELECTRE III) method, using the same criteria weighted by the previous ANP application. This hybrid MCDM framework is applied to a core subsystem of a real-world marine salt manufacture firm

A risk evaluation framework for the best maintenance strategy: the case of a marine salt manufacture firm

[EN] This paper intends to contribute with a multi-criteria decision-making (MCDM) framework to support risk evaluation for maintenance activities carried out on critical systems in industry. We propose to first select the best maintenance strategy tailored to companies' requirements and systems' features, and second to perform a risk prioritisation aimed at highlighting priorities of intervention. The Analytic Network Process (ANP) is suggested to select the maintenance policy representing the best trade-off considering the complex and varied interdependencies amongst a diversity of clustered elements characterising the system. Then, the main risks related to the interventions associated to the selected maintenance policy are ranked using the ELimination Et Choix Traduisant la REalite III (ELECTRE III) method, using the same criteria weighted by the previous ANP application. This hybrid MCDM framework is applied to a core subsystem of a real-world marine salt manufacture firm.Carpitella, S.; Mzougui, I.; Benítez López, J.; Carpitella, F.; Certa, A.; Izquierdo Sebastián, J.; La Cascia, M. (2021). A risk evaluation framework for the best maintenance strategy: the case of a marine salt manufacture firm. Reliability Engineering & System Safety. 205:1-14. https://doi.org/10.1016/j.ress.2020.107265S11420