A multi-attribute supply chain network resilience assessment framework based on SNA-inspired indicators

Abstract

This study proposes a supply chain resilience assessment framework at the network (i.e. structural) level based on quantifying supply chain networks' structural factors and their relationships to different resilience strategies, by using a hybrid DEMATEL-ANP approach. DEMATEL is used to quantify interdependencies between the structural resilience factors, and between the resilience strategies. ANP is then used to quantify the outer-dependencies among these elements and to construct the limit supermatrix from which the global weights of all the decision network's elements are estimated. To create the structural resilience factors, different network factors are selected and adopted from the social network analysis and supply chain resilience literatures. A case study is then performed to assess the performance of the proposed approach and to derive important observations to support future decision making. According to the results, the proposed approach can suitably measure the resilience performance of a supply chain network and help decision makers plan for more effective resilience improvement actions.

Can social network analysis contribute to supply chain management? A systematic literature review and bibliometric analysis

“…Network size (i.e., the number of nodes in a network) is one of the fundamental attributes of a network. In a supply chain context, network size can be used as an indicator of the level of supply chain complexity [ 7 , 46 ]. Although large networks are characterized by higher efficiency and innovation capacity [ 47 ], they are more complex to manage because they require further coordination and information sharing.…”

“…Although large networks are characterized by higher efficiency and innovation capacity [ 47 ], they are more complex to manage because they require further coordination and information sharing. In addition, the increased level of complexity can have significant implications for risk diffusion, resilience capabilities, adaptive capacity, and performance after disruptions [ [46] , [47] , [48] ].…”

“…While higher density is conceptually linked to increased level of complexity [ 7 , 49 ] and more challenges in managing risk [ 50 , 51 ], denser networks facilitate knowledge sharing [ 10 , 52 ], allow for higher chances for collaboration [ 53 , 54 ], increase the level of trust between members [ 47 ], and lead to greater degrees of bargaining power in buyer-supplier relationships [ 55 ]. On the other hand, lower network density supports innovative capacity while limiting the homogenization of ideas and perception [ 47 ], unlike higher density networks in which frequent interactions may lead to redundancy of information and knowledge [ 10 , 46 ].…”

“…The longest of the shortest paths between any two nodes is termed the diameter [ 42 , 45 ]. APL and diameter are among the main aspects of supply chain design [ 46 ]. In a supply chain context, shorter APL facilitates faster and more efficient information sharing between supply chain members and enables timely reaction to sudden changes [ 47 ].…”

Enhancing 3D Reconstruction Model by Deep Learning and Its Application in Building Damage Assessment after Earthquake

“…The generalization ability of the model is the most important ability to move towards engineering applications, so it is necessary to use real post-earthquake image data analysis to evaluate the performance of the method. At the same time, these data also fill the gaps in the application of this method to the damage assessment of buildings after earthquakes [29,30]. Therefore, the objective of the study is to propose a deep-learning-based MVS method that is suitable for assessing building damage after an earthquake.…”