Identifying critical supply chain risks through social network analysis: ICT company in Iran

Document Type : Research Paper

Authors

Industrial Engineering Department, Kharazmi University, Tehran, Iran

Abstract

Identifying and evaluating supply chain risks is one of the most challenging issues related to supply chain risk management (SCRM). Many risks may threaten a supply chain, but upon the costs, managers had better pay attention to those with the highest impact. The paper advances to identify and rank Information and Communication Technology (ICT) supply chain risks and investigate their intereffects in a directed graph through the social network analysis approach and experts' opinions. Firstly, ICT supply chain risks were determined based on semi-structured interviews with organizational experts on the viable system model (V.S.M.). Then, they were asked to set a score between zero and five based on the impact of each risk on the other risks to assigning appropriate weight to edges. Finally, ICT supply chain risks were ranked based on centrality measures. The findings indicate that social and political conditions affect the ICT supply chain. As well as, the accuracy of the information and the emergence of new technologies are other factors that have the most significant impact on additional risks in the supply chain. We also situated the analysis on Tehran Internet Holding, a large company representative sales and after-sales service agent of Iran's most outstanding digital operator.

Keywords

Main Subjects


Adham, K. A., Kasimin, H., Said, M. F., & Igel, B. (2012). Functions and inter-relationships of operating agencies in policy implementation from a viable system perspective. Systemic Practice and Action Research, 25, 149-170.
Albert, R., & Barabási, A. L. (2000). Topology of evolving networks: local events and universality. Physical review letters, 85(24), 5234.
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.
Asadabadi, M. R., Chang, E., & Saberi, M. (2019). Are MCDM methods useful? A critical review of analytic hierarchy process (AHP) and analytic network process (ANP). Cogent Engineering, 6(1), 1623153.
Badea, A., Prostean, G., Goncalves, G., & Allaoui, H. (2014). Assessing risk factors in collaborative supply chain with the analytic hierarchy process (AHP). Procedia-Social and Behavioral Sciences, 124, 114-123.
Beer, S. (1981). Brain of the Firm, Chichester”.
Bloch, F., Jackson, M. O., & Tebaldi, P. (2023). Centrality measures in networks. Social Choice and Welfare, 1-41.
Bonacich, P. (1972). Factoring and weighting approaches to status scores and clique identification. Journal of mathematical sociology, 2(1), 113-120.
Boyens, J., Paulsen, C., Moorthy, R., Bartol, N., & Shankles, S. A. (2015). Supply chain risk management practices for federal information systems and organizations. NIST Special publication, 800(161), 32.
Cross, R. L., & Parker, A. (2004). The hidden power of social networks: Understanding how work really gets done in organizations. Harvard Business Press.
Das, D., Datta, A., Kumar, P., Kazancoglu, Y., & Ram, M. (2021). Building supply chain resilience in the era of COVID-19: An AHP-DEMATEL approach. Operations Management Research, 1-19.
Dong, Q., & Cooper, O. (2016). An orders-of-magnitude AHP supply chain risk assessment framework. International journal of production economics, 182, 144-156.
Dua, S., Sharma, M. G., Mishra, V., & Kulkarni, S. D. (2022). Modelling perceived risk in blockchain enabled supply chain utilizing fuzzy-AHP. Journal of Global Operations and Strategic Sourcing, 16(1), 161-177.
Fazli, S., Kiani Mavi, R., & Vosooghidizaji, M. (2015). Crude oil supply chain risk management with DEMATEL–ANP. Operational Research, 15, 453-480.
Freeman, L. C. (2002). Centrality in social networks: Conceptual clarification. Social network: critical concepts in sociology. Londres: Routledge, 1, 238-263.
Gurtu, A., & Johny, J. (2021). Supply chain risk management: Literature review. Risks, 9(1), 16.
Hagen, L., Keller, T., Neely, S., DePaula, N., & Robert-Cooperman, C. (2018). Crisis communications in the age of social media: A network analysis of Zika-related tweets. Social science computer review, 36(5), 523-541.
Harvey, J., Smith, A., Goulding, J., & Illodo, I. B. (2020). Food sharing, redistribution, and waste reduction via mobile applications: A social network analysis. Industrial Marketing Management, 88, 437-448.
Heckmann, I., Comes, T., & Nickel, S. (2015). A critical review on supply chain risk–Definition, measure and modeling. Omega, 52, 119-132.
Hoverstadt, P., & Bowling, D. (2002, May). Modelling organisations using the viable system model. In Royal Academy of Engineering Systems Engineering Workshop (Vol. 14). Fractal Consulting.
Jackson, M. (2003). System Thinking: Creative holism for managers. (T. Naser Shariatie; Trans.) Tehran: Industrial Managment Organization.
Jackson, M. O. (2008). Social and economic networks (Vol. 3). Princeton: Princeton university press.
Jose, P. R. (2012). Design and diagnosis for sustainable organizations: The viable system method. Springer Science & Business Media.
Kayis, B., & Dana Karningsih, P. (2012). SCRIS: A knowledge‐based system tool for assisting manufacturing organizations in identifying supply chain risks. Journal of Manufacturing Technology Management, 23(7), 834-852.
Kim, Y., Choi, T. Y., Yan, T., & Dooley, K. (2011). Structural investigation of supply networks: A social network analysis approach. Journal of operations management, 29(3), 194-211.
Kumar, P., Singh, R. K., & Kumar, V. (2021). Managing supply chains for sustainable operations in the era of industry 4.0 and circular economy: Analysis of barriers. Resources, Conservation and Recycling, 164, 105215.
Lei, X., & MacKenzie, C. A. (2019). Assessing risk in different types of supply chains with a dynamic fault tree. Computers & Industrial Engineering, 137, 106061.
Li, Y., Zobel, C. W., Seref, O., & Chatfield, D. (2020). Network characteristics and supply chain resilience under conditions of risk propagation. International Journal of Production Economics, 223, 107529.
Liu, W., Wei, W., Yan, X., Dong, D., & Chen, Z. (2020). Sustainability risk management in a smart logistics ecological chain: An evaluation framework based on social network analysis. Journal of Cleaner Production, 276, 124189.
Luo, L., Qiping Shen, G., Xu, G., Liu, Y., & Wang, Y. (2019). Stakeholder-associated supply chain risks and their interactions in a prefabricated building project in Hong Kong. Journal of Management in Engineering, 35(2), 05018015.
Magableh, G. M., & Mistarihi, M. Z. (2022). Applications of MCDM approach (ANP-TOPSIS) to evaluate supply chain solutions in the context of COVID-19. Heliyon, 8(3).
Noroozian, A., Amiri, B., & Kermani, M. A. M. A. (2022). Investigation of cinematic genre diversity based on social network analysis: the lost ring of the Iranian cinema industry. Kybernetes, (ahead-of-print).
Ongkowijoyo, C. S., & Doloi, H. (2018). Understanding of impact and propagation of risk based on social network analysis. Procedia engineering, 212, 1123-1130.
Osunji, O. (2021). Know your suppliers: A review of ICT supply chain risk management efforts by the US government and its agencies. Cyber Security: A Peer-Reviewed Journal, 4(3), 232-242.
Otte, E., & Rousseau, R. (2002). Social network analysis: a powerful strategy, also for the information sciences. Journal of information Science, 28(6), 441-453.
Pournader, M., Kach, A., & Talluri, S. (2020). A review of the existing and emerging topics in the supply chain risk management literature. Decision sciences, 51(4), 867-919.
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.
Rodriguez-Rodriguez, R., & Leon, R. D. (2016). Social network analysis and supply chain management. International Journal of Production Management and Engineering, 4(1), 35-40.
Saaty, R. W. (1987). The analytic hierarchy process—what it is and how it is used. Mathematical modelling, 9(3-5), 161-176.
Saberhoseini, S. F., Edalatpanah, S. A., & Sorourkhah, A. (2022). Choosing the best private-sector partner according to the risk factors in neutrosophic environment. Big data and computing visions, 2(2), 61-68.
Saxena, A., & Iyengar, S. (2020). Centrality measures in complex networks: A survey. arXiv preprint arXiv:2011.07190.
Schoenherr, T., S. Talluri, and V. Verter, (2020). What C.E.O.s Need to Know as U.S. Industry Rallies to Fight COVID-19. Retrieved from Chief Executive Website. https://chief execu tive. net/what-ceos-need-to-know-as-u-s-indus try-ralli es-to-figh t-covid-19.
 
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.
Tsai, M. C., Liao, C. H., & Han, C. S. (2008). Risk perception on logistics outsourcing of retail chains: model development and empirical verification in Taiwan. Supply Chain Management: An International Journal, 13(6), 415-424.
Wang, J., Zhou, H., & Jin, X. (2021). Risk transmission in complex supply chain network with multi-drivers. Chaos, Solitons & Fractals, 143, 110259.
Warren, L. (2002). Rational analysis for a problematic world revisited: Problem structuring methods for complexity, uncertainty and conflict. Systems Research and Behavioral Science, 19(4), 383-385.
Wichmann, B. K., & Kaufmann, L. (2016). Social network analysis in supply chain management research. International Journal of Physical Distribution & Logistics Management, 46(8), 740-762.
Wu, 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.
Zhang, Y., & Siemsen, E. (2019). A meta‐analysis of newsvendor experiments: revisiting the pull‐to‐center asymmetry. Production and Operations Management, 28(1), 140-156.
Zschache, J. (2012). Producing public goods in networks: Some effects of social comparison and endogenous network change. Social Networks, 34(4), 539-548.