Testing "efficient supply chain propositions" using topological characterization of the global supply chain network

Inequality in economic shock exposures across the global firm-level supply network

For centuries, national economies have been engaging in international trade and production. The resulting international supply networks not only increase wealth for countries, but also allow for economic shocks to propagate across borders. Using global, firm-level supply network data, we estimate a country's exposure to direct and indirect economic losses caused by the failure of a company in another country. We show the network of international systemic risk-flows. We find that rich countries expose poor countries stronger to systemic risk than vice-versa. The risk is highly concentrated, however, higher risk levels are not compensated with a risk premium in GDP levels, nor higher GDP growth. Our findings put the often praised benefits for developing countries from globalized production in a new light, by relating them to risks involved in the production processes. Exposure risks present a new dimension of global inequality that most affects the poor in supply shock crises.

Resilience of supply-chain systems under perturbations: A network approach

Supply-chain systems (SCSs) are an indispensable part of our daily infrastructures. Note that a small perturbation in a SCS can be amplified, eliciting cascading failures. It is of significant value to ensure a high resilience of SCSs. However, due to the complexity of SCSs, it is quite challenging to study their resilience under conditions of perturbations. In view of this, this paper presents a complex network perspective toward the resilience of SCSs. To achieve this goal, a complex SCS is modeled as a multilayer supply-chain network (SCN) with physical organizations being modeled as nodes and interactions among them as edges. A modeled SCN contains three types of nodes, i.e., suppliers, manufacturers, and retailers. An algorithm is proposed to construct a multilayer SCN. For each layer of a multilayer SCN, two kinds of networks, i.e., networks with Poisson degree distributions and networks with power-law degree distributions, are considered. For a given multilayer SCN, a ripple-effect network model is proposed to analyze its resilience under perturbations. Regarding the perturbations, two scenarios, i.e., random node failures and target node failures, are adopted in this research. In order to validate the effectiveness of the proposed network perspective, simulations on computer-generated SCNs are carried out. Interestingly, it is found that the resilience of SCNs under both random and target perturbations presents a discontinuous phase-change phenomenon, which indicates that SCNs are quite fragile under perturbations. It is further noticed that SCNs with power-law degree distributions are relatively more robust than SCNs with Poisson degree distributions. Although SCNs are found to be fragile, it has been discovered that denser interactions between different system organizations can enhance the network's resilience.

A Sustainable Supply Chain Network Model Considering Carbon Neutrality and Personalization

The development of a carbon-neutral production and distribution method that minimizes the consumption of input resources and energy and facilitates resource recycling is an important global issue. Therefore, this study aimed to explore a new type of sustainable supply chain network (SSCN) that focuses on carbon neutrality and personalization. The first significance of the SSCN model is that it combines carbon neutrality and personalization problems into one research framework for the sustainable development of corporate management environments. In addition, evaluation and optimization mechanisms are crucial for decision-making in an SSCN. Thus, in this study, a creative evaluation and optimization mechanism was developed as a second significance for the sustainable development of the supply chain. The model used two evaluation indices (EIs) to measure the performance of the entire network in terms of the degree of personalization and carbon neutrality. The proposed SSCN is formulated as a mathematical model using mixed-integer nonlinear programming, and it is implemented by combining two types of approaches: hypergeometric distribution and a genetic algorithm. The results show the evaluation values and comparisons by the average values of transportation cost, handling cost, fixed costs, carbon dioxide emission cost, total cost, and average processing time. In addition to providing help for mass-customized production enterprises, this study also provided constructive suggestions for the conversion of small or venture enterprises to multi-variety and small-scale production, and it suggests the direction of job creation for such enterprises.