Modeling and resilience analysis of multi-group supply chain network

Area of Science:

• Operations Research
• Supply Chain Management
• Network Science

Background:

• Modern supply chains exhibit complex, networked structures (SCNs).
• Traditional SCN models use multi-level structures, assuming nodes at the same level are homogenous.
• Real-world SCNs feature enterprises at the same level producing diverse products, defining their function.

Purpose of the Study:

• To propose a novel Multi-Group Supply Chain Network (MGSCN) model that groups supply chain nodes based on enterprise function.
• To analyze the resilience of the proposed MGSCN model under cascading failures.
• To identify strategies for enhancing SCN resilience.

Main Methods:

• Development of the Multi-Group Supply Chain Network (MGSCN) model.
• Construction of an underload cascading failure model incorporating recovery strategies.
• Simulation analysis to compare MGSCN with traditional SCN models and evaluate resilience factors.

Main Results:

• The traditional, ungrouped SCN model underestimates the impact of cascading failures.
• Reducing the number of functional groups in the MGSCN model significantly enhances network resilience.
• Adjusting node capacity bounds (increasing upper bound or decreasing lower bound) improves MGSCN resilience.

Conclusions:

• The MGSCN model provides a more accurate representation of real-world supply chain complexities.
• Functional grouping is a critical factor in assessing and improving supply chain network resilience.
• The findings offer valuable insights for managing and optimizing real-world supply chains.