How I Built a Graph-Based Digital Twin to Simulate Cascading Supply Chain Failures

Building a simulation engine using Python, NetworkX, and Streamlit to model cascading failures in supply chain networks.

Most supply chain failures don’t happen all at once — they unfold in cascades.

I recently built a graph-based simulation engine to understand how disruptions propagate through complex logistics networks. The system models supply chains as directed weighted graphs and simulates how failures spread step-by-step across infrastructure.

In this post, I’ll walk through how I built it, how the cascade logic works, and what I learned from designing the system.

Tech Stack

• Python
• NetworkX
• Streamlit
• Pandas
• Pytest (for deterministic validation)

Modeling the Network

The supply chain is modeled as a directed weighted graph:

python
import networkx as nx

G = nx.DiGraph()

G.add_node("A", type="factory")
G.add_node("B", type="hub")
G.add_node("D", type="market")

G.add_edge("A", "B", weight=2)
G.add_edge("B", "D", weight=2)

Routing is computed using Dijkstra’s algorithm:
 - nx.dijkstra_path(G, "A", "D", weight="weight")

## Explore the Project

👉 GitHub: https://github.com/jithinmathws/supplyChainRiskAnalyzer

If you work with graph systems, simulation engines, or infrastructure modeling, I’d love to hear your thoughts.