Measuring the Impact of Supply Network Topology on the Material Delivery Robustness in Construction Projects

  • Heo, Chan (Department of Architecture and Architectural Engineering, Seoul National University) ;
  • Ahn, Changbum (Department of Architecture and Architectural Engineering, Seoul National University) ;
  • Yoon, Sungboo (Institute of Construction and Environmental Engineering, Seoul National University) ;
  • Jung, Minhyeok (Department of Architecture and Architectural Engineering, Seoul National University) ;
  • Park, Moonseo (Department of Architecture and Architectural Engineering, Seoul National University)
  • Published : 2022.06.20

Abstract

The robustness of a supply chain (i.e., the ability to cope with external and internal disruptions and disturbances) becomes more critical in ensuring the success of a construction project because the supply chain of today's construction project includes more and diverse suppliers. Previous studies indicate that topological features of the supply chain critically affect its robustness, but there is still a great challenge in characterizing and quantifying the impact of network topological features on its robustness. In this context, this study aims to identify network measures that characterize topological features of the supply chain and evaluate their impact on the robustness of the supply chain. Network centrality measures that are commonly used in assessing topological features in social network analysis are identified. Their validity in capturing the impact on the robustness of the supply chain was evaluated through an experiment using randomly generated networks and their simulations. Among those network centrality measures, the PageRank centrality and its standard deviation are found to have the strongest association with the robustness of the network, with a positive correlation coefficient of 0.6 at the node level and 0.74 at the network level. The findings in this study allows for the evaluation of the supply chain network's robustness based only on its topological design, thereby enabling practitioners to better design a robust supply chain and easily identify vulnerable links in their supply chains.

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Acknowledgement

This work was supported by the New Faculty Startup Fund from Seoul National University.