Document Type : Research Paper

Authors

1 Ph.D. Candidate , Department of Industrial Management, Tabriz Branch, Islamic Azad University, Tabriz, Iran

2 Professor , Department of Industrial Management, Tabriz Branch, Islamic Azad University, Tabriz, Iran

3 Assistant Professor , Department of Industrial Management, Tabriz Branch, Islamic Azad University, Tabriz, Iran

Abstract

The purpose of this study is to design supply chains' upstream structure evaluation model in the automotive industry with spectral clustering based on the theory of complex adaptive systems. In this research, a method for evaluating the intersectionalities related to the structural complexity (horizontal, vertical, and spatial) of supply chains by considering the functional characteristics of its components based on the resilience paradigm is presented. In this regard, a set of algebraic calculations and computational algorithms have been adapted to evaluate the structural design from the perspective of complex components. In the structural design evaluation model through spectral clustering, it is possible to enter information about supply chains in terms of interactions between components in the form of a network as a comprehensive model called similarity graph. According to the field findings, supply chain characteristics in terms of complexity can have interaction with component processing performance. This means that according to the concept of entanglement, the lack of a favorable environmental structure in supply chains can also negatively affect the resilience performance of its components. Findings from the perspective of achieving a supply chain evaluation model as an integrated whole have provided a suitable practical tool for evaluation and pathology of supply chains from the perspective of risk management.

Keywords

Main Subjects

Bode, C., & Wagner, S. M. (2015). Structural drivers of upstream supply chain complexity and the frequency of supply chain disruptions. Journal of operations Management, 36, 215-228.
Chen, C., & Morris, S. (2003). Visualizing evolving networks: minimum spanning trees versus pathfinder networks. Paper presented at the Symposium on Information Visualization (IEEE Cat. No.03TH8714), 19-21 Oct. 2003, USA, 67-74.
Costa, A. S., Govindan, K., & Figueira, J. R. (2018). Supplier classification in emerging economies using the ELECTRE TRI-nC method: A case study considering sustainability aspects. Journal of Cleaner Production, 201, 925-947. doi:https://doi.org/10.1016/j.jclepro.2018.07.285
Ding, S., Jia, H., Du, M., & Xue, Y. (2018). A semi-supervised approximate spectral clustering algorithm based on HMRF model. Information Sciences, 429, 215-228. doi:https://doi.org/10.1016/j.ins.2017.11.016
Kim, Y., Chen, Y.-S., & Linderman, K. (2015). Supply network disruption and resilience: A network structural perspective. Journal of operations Management, 33, 43-59.
Li, X.-Y., & Guo, L.-J. (2012). Constructing affinity matrix in spectral clustering based on neighbor propagation. Neurocomputing, 97, 125-130. doi:https://doi.org/10.1016/j.neucom.2012.06.023
Mellat Parast, M. (2020). The impact of R&D investment on mitigating supply chain disruptions: Empirical evidence from U.S. firms. International Journal of Production Economics, 227, 107671. doi:https://doi.org/10.1016/j.ijpe.2020.107671
Partl, C., Gratzl, S., Streit, M., Wassermann, A. M., Pfister, H., Schmalstieg, D., & Lex, A. (2016). Pathfinder: Visual analysis of paths in graphs. Paper presented at the Computer Graphics Forum.
Pettit, T. J., Croxton, K. L., & Fiksel, J. (2019). The Evolution of Resilience in Supply Chain Management: A Retrospective on Ensuring Supply Chain Resilience. Journal of Business Logistics, 40(1), 56-65.
Poudel, S. R., Marufuzzaman, M., & Bian, L. (2016). Designing a reliable bio-fuel supply chain network considering link failure probabilities. Computers & Industrial Engineering, 91, 85-99. doi:https://doi.org/10.1016/j.cie.2015.11.002
Pournader, M., Rotaru, K., Kach, A. P., & Hajiagha, S. H. R. (2016). An analytical model for system-wide and tier-specific assessment of resilience to supply chain risks. Supply Chain Management: An International Journal, 21(5), 589-609. doi:doi:10.1108/SCM-11-2015-0430
Shi, J., & Malik, J. (2000). Normalized cuts and image segmentation. IEEE Transactions on pattern analysis and machine intelligence, 22(8), 888-905.
Sokolov, B., Ivanov, D., Dolgui, A., & Pavlov, A. (2016). Structural quantification of the ripple effect in the supply chain. International Journal of Production Research, 54(1), 152-169. doi:10.1080/00207543.2015.1055347
Tomaskovic-Devey, D., Leiter, J., & Thompson, S. (1994). Organizational survey nonresponse. Administrative Science Quarterly, 439-457.
Tukamuhabwa Rwakira, B., Busby, J., & Stevenson, M. (2015). Supply chain resilience: a case study analysis of a supply network in a developing country context. (Ph.D), Lancaster University.  
Von Luxburg, U. (2007). A tutorial on spectral clustering. Statistics and computing, 17(4), 395-416.
Wagner, S. M., & Neshat, N. (2010). Assessing the vulnerability of supply chains using graph theory. International Journal of Production Economics, 126(1), 121-129. doi:http://dx.doi.org/10.1016/j.ijpe.2009.10.007
Xu, S., Zhang, X., Feng, L., & Yang, W. (2020). Disruption risks in supply chain management: a literature review based on bibliometric analysis. International Journal of Production Research, 58(11), 3508-3526. doi:10.1080/00207543.2020.1717011