Network Analysis for Systemic Risk Assessment in Supply Chains

A cross-disciplinary framework integrating financial contagion models with supply chain network analysis for resilience assessment and policy guidance.

📊 Overview

This research develops a novel framework that adapts financial systemic risk models to supply chain networks, introducing the concept of "too-central-to-fail" suppliers through systematic importance scoring methodologies. The framework provides quantitative foundations for supply chain regulation, early warning systems, and resilience enhancement strategies.

🔬 Key Findings

• 296 systemically important suppliers identified (59.2% of network)
• Moderate network resilience with 5.4% mean failure rate under random shocks
• High vulnerability to targeted attacks (up to 3.2% failure rates)
• Asymmetric spillover patterns with strongest contagion from suppliers to manufacturers (0.234)
• Financial contagion potential affecting 42.2% of network participants

📁 Repository Structure

network_analysis_supply_chain/ ├── paper/ # LaTeX paper and documentation │ └── journal_article_final_corrected.tex ├── figures/ # All visualization outputs │ ├── network_topology.png │ ├── risk_distributions.png │ ├── correlation_heatmap.png │ ├── spillover_heatmap.png │ ├── monte_carlo_results.png │ ├── attack_simulation.png │ ├── cascade_simulation.png │ └── percolation_analysis.png ├── data/ # Network data and metrics │ ├── network_nodes.csv │ ├── network_edges.csv │ ├── risk_metrics.csv │ ├── multi_tier_supply_network.json │ ├── summary_statistics.json │ └── stress_test_summary.json ├── analysis/ # Python analysis scripts │ ├── main_analysis.py │ ├── statistical_analysis.py │ ├── stress_testing.py │ ├── visualization_generation.py │ └── verification_suite.py └── README.md # This file

🚀 Getting Started

Prerequisites

bash pip install networkx pandas numpy matplotlib seaborn scipy scikit-learn

Running the Analysis

1. Generate Network Data: bash python analysis/main_analysis.py

2. Run Stress Tests: bash python analysis/stress_testing.py

3. Create Visualizations: bash python analysis/visualization_generation.py

4. Validate Results: bash python analysis/verification_suite.py

📈 Key Results

Network Characteristics

• 500 nodes across 3 tiers (300 suppliers, 80 manufacturers, 120 retailers)
• 4,786 directed edges representing supplier-customer relationships
• Small-world properties with clustering coefficient 0.324 and average path length 3.47

Systemic Risk Metrics

• Mean systemic importance: 0.267 across all nodes
• High-risk suppliers: 296 nodes with SI > 0.2
• Financial fragility correlation: 0.657 with systemic importance

Resilience Analysis

• Monte Carlo simulations: 5.364% mean failure rate (1000 runs)
• Targeted attacks: High-degree attacks most effective (3.2% max impact)
• Liquidity crisis: 42.2% network impact through financial contagion
• Percolation behavior: Gradual connectivity decline without critical thresholds

🏛️ Policy Applications

Regulatory Framework

• Systemically important supplier identification based on adapted DebtRank methodology
• Tier-differentiated regulation based on asymmetric spillover patterns
• Stress testing protocols for supply chain risk assessment
• Early warning systems using network centrality and financial fragility indicators

International Coordination

• Cross-border dependency mapping using spillover analysis
• Regional regulatory harmonization focused on suppliers and manufacturers
• Risk-based intervention criteria for proactive supply chain management

📊 Figures Description

• Figure 1: Network topology with systemic importance coloring
• Figure 2: Distribution of key risk metrics across nodes
• Figure 3: Correlation matrix of risk metrics
• Figure 4: Cross-sector spillover matrix visualization
• Figure 5: Monte Carlo simulation results distribution
• Figure 6: Progressive targeted attack results
• Figure 7: Liquidity crisis cascade propagation
• Figure 8: Network percolation analysis

📚 Citation

bibtex @article{omoshola2025network, title={Network Analysis for Systemic Risk Assessment in Supply Chains: A Cross-Disciplinary Framework Integrating Financial Contagion Models}, author={Omoshola, O.S.}, journal={Journal of Data Analysis and Information Processing}, year={2025}, note={In preparation} }

👨‍💼 Author

Omoshola S. Owolabi
Department of Data Science
Carolina University, Winston Salem - North Carolina, USA
Email: owolabio@carolinau.edu

🔬 Research Impact

This framework establishes foundations for: - Evidence-based supply chain regulation - Quantitative resilience assessment - Cross-disciplinary risk modeling - Policy-oriented network analysis

For detailed methodology, complete results, and validation protocols, see the full paper in the paper/ directory.