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

pip install networkx pandas numpy matplotlib seaborn scipy scikit-learn

Running the Analysis

1. Generate Network Data:

   python analysis/main_analysis.py

2. Run Stress Tests:

   python analysis/stress_testing.py

3. Create Visualizations:

   python analysis/visualization_generation.py

4. Validate Results:

   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

@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

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