🛡️ Global Supply Chain Risk Engine

A real-time, enterprise-grade supply chain risk monitoring platform — built entirely with open-source Python tools, deployed on the cloud, and protected with secure authentication.

🚀 Live Demo  •  📂 Repository

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📸 Application Preview

🖼️ [ ]

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🎬 Full Demo

🎥 [https://youtu.be/rYTDYH3ZAt4]

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💡 What Is This?

The Global Supply Chain Risk Engine is a full-stack data intelligence platform that helps procurement teams, logistics managers, and risk analysts identify, monitor, and simulate supply chain disruptions — before they happen.

Unlike generic BI dashboards, this engine combines:

• Real-time news intelligence from 8 industry-specific RSS feeds
• Live weather risk scoring along shipping routes via Open-Meteo
• Dynamic risk modeling using financial, geopolitical, and delay factors
• What-if scenario simulation for disruption planning
• One-click PDF audit reports for executive stakeholders
• Secure SHA-256 login system — no plain-text passwords, ever

All of this runs on Streamlit Community Cloud — completely free to host.

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✨ Feature Breakdown

🔐 Secure Access Terminal

A professional neon-themed login screen protects the entire application. Credentials are SHA-256 hashed with a cryptographic salt. No plain-text passwords stored anywhere in the codebase. Built for single-tenant or multi-client deployment.

📊 Executive Risk Dashboard

The command center of the application. Upload any supplier CSV and instantly see:

• Average risk score across your entire supplier network
• Count of high-risk suppliers (score > 75)
• Statistical anomaly detection via Z-score analysis
• Risk score distribution histogram
• Top 10 highest-risk suppliers via live DuckDB SQL query

🌍 Global Risk Heatmap

An interactive Plotly scatter-geo world map that plots every supplier by geographic coordinates, sized and colored by risk score. Instantly visualize where your supply chain is most exposed — from APAC manufacturing hubs to Middle East trade corridors.

📡 Supply Chain Intelligence Feed

Live news pulled from 8 industry-specific RSS sources — zero API keys required:

Source	Coverage
Supply Chain Dive	Industry operations & disruptions
Logistics Management	Warehousing, freight, 3PL
FreightWaves	Freight markets & trucking
DC Velocity	Distribution & materials handling
JOC	Port & container shipping
Hellenic Shipping News	Global maritime intelligence
Reuters Trade	Global trade & commerce
Bloomberg Supply Chain	Financial & market risk

Filter by source, search by keyword (tariff, port strike, logistics), and read full articles directly from the dashboard in expandable cards.

⚗️ Risk Scenario Simulator

A three-part what-if analysis engine for proactive risk planning:

Region Disruption Simulator — Select any region and apply a disruption intensity multiplier (1x–5x). See a live bar chart comparing baseline vs simulated risk scores across your entire supplier network.

Inventory Impact Calculator — Input current stock, daily demand, and lead time to compute days-of-cover and shortfall probability with a color-coded risk banner.

Lead Time Projector — Model the combined effect of port congestion index and active labor strikes on expected delivery timelines.

🌦️ Shipping Route Weather Monitor

Powered by Open-Meteo — completely free, no API key, no credit card. Select any supplier from your dataset and get:

• Current temperature, wind speed, and precipitation probability
• Shipping risk classification: 🟢 LOW → 🟡 MODERATE → 🟠 HIGH → 🔴 SEVERE
• 24-hour wind speed forecast (line chart)
• 24-hour precipitation probability (color-coded bar chart)

📄 Automated PDF Risk Report

One-click generation of a professional audit-ready PDF containing:

• Customizable executive summary
• Risk statistics (average, min, max scores across all suppliers)
• Critical supplier table (auto-filtered at risk score > 50)
• Auto-detects column names from any CSV schema — no manual mapping

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🧠 Risk Scoring Model

Each supplier receives a composite risk score (0–100) from three weighted components:

Total Risk = (Geo Risk × 0.40) + (Delay Risk × 0.30) + (Financial Risk × 0.30)

Component	Methodology	Weight
Geo Risk	Logistic growth curve over event density × regional sensitivity coefficient	40%
Delay Risk	Lead time volatility model: avg delay days + historical variance	30%
Financial Risk	Log-scaled revenue exposure + supplier health penalty (1 − rating)	30%

Scores normalize to 0–100 via Min-Max scaling and classify into tiers: LOW (0–25) → MEDIUM (26–50) → HIGH (51–75) → CRITICAL (76–100)

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🏗️ Project Architecture

ROOT/
├── app.py                    ← Main entry point + SHA-256 login gate
├── theme.py                  ← Neon/terminal CSS theme + Plotly dark layout
├── requirements.txt
│
├── engine/
│   ├── ingest.py             ← CSV loader, schema auto-detection, GDELT
│   ├── news_stream.py        ← RSS intelligence feed + Open-Meteo weather
│   ├── risk_model.py         ← Geo, delay & financial risk scoring engine
│   └── scenario_sim.py       ← Disruption simulator, inventory & lead time calc
│
├── database/
│   └── risk_queries.py       ← DuckDB in-memory SQL analytics layer
│
├── components/
│   ├── alerts.py             ← Z-score anomaly detection & alert banners
│   ├── map_viz.py            ← Scatter-geo & choropleth map builders
│   └── views.py              ← Page-level UI components
│
├── utils/
│   └── pdf_gen.py            ← FPDF2 professional report generator
│
└── data/
    └── suppliers.csv         ← Sample dataset (50 global suppliers)

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🛠️ Tech Stack

Layer	Technology	Reason
UI & Hosting	Streamlit 1.31	Rapid deployment, free cloud tier
Data Engine	Polars 0.20	10–100x faster than Pandas on large CSVs
Analytics	DuckDB 0.10	In-memory SQL on DataFrames, zero setup
Visualization	Plotly 5.18	Interactive geo maps, charts, hover tooltips
News Feed	RSS feeds (8 sources)	Free, reliable, no API key required
Weather	Open-Meteo API	100% free, no registration needed
PDF Export	FPDF2 2.7	Lightweight professional PDF generation
Security	SHA-256 + Salt	Industry-standard password hashing

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🚀 Getting Started

1. Clone the repository

git clone https://github.com/Ali-datasmith/SupplyChain-Risk-Engine.git
cd SupplyChain-Risk-Engine

2. Install dependencies

pip install -r requirements.txt

3. Run locally

streamlit run app.py

4. Login

Username : Ali-datasmith
Password : Contact the developer

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📄 CSV Format

The engine accepts any CSV format — column names are auto-detected and normalized at upload time:

Column	Required	Accepted Aliases
supplier	✅ Yes	supplier_name, vendor, name, company
risk_score	✅ Yes	Risk_Score, risk, score, rating
region	⭐ Recommended	Supplier_Region, country, area, zone
lat	🗺️ Map only	latitude, y
lon	🗺️ Map only	longitude, lng, x

Extra columns (industry, lead_time_days, financial_score, reliability_score, inventory_value) are automatically used where applicable.

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💼 Real-World Use Cases

Procurement Teams — Identify which suppliers pose the highest risk before contract renewal. Prioritize audits based on data, not gut feeling.

Logistics Managers — Monitor live weather risk along shipping routes. Get early warnings before a typhoon or port strike disrupts your network.

Risk & Compliance Officers — Generate audit-ready PDF reports with one click. Present executive summaries to leadership without manual data preparation.

Investment Analysts — Model supply chain exposure for portfolio companies. Run what-if scenarios on geopolitical disruptions — Red Sea conflict, China tariffs, labor strikes.

Consulting Firms — White-label this platform for clients. Deploy a branded version within hours with custom supplier datasets.

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📈 Growth Roadmap

The modular architecture makes the following extensions straightforward:

• Multi-tenant SaaS — Per-client login with isolated data views
• ERP Integration — Connect to SAP, Oracle, or NetSuite via REST API
• ML Risk Prediction — Train a gradient boosting model on historical disruptions
• Real-time Alerts — Email/Slack notifications when risk scores cross thresholds
• Supplier Benchmarking — Peer comparison within same region or industry
• Mobile Responsive UI — Progressive Web App for field use

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☁️ Deployment

Optimized for Streamlit Community Cloud (Free Tier):

• ✅ In-memory only — no persistent database or file system needed
• ✅ All modules have graceful fallbacks if imports fail on cold start
• ✅ RAM usage stays under 1 GB for datasets up to 100,000 rows
• ✅ No API keys required for any core feature

1. Push repository to GitHub
2. Go to share.streamlit.io
3. Connect repository → set app.py as entry point
4. Click Deploy — live in 60 seconds

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📦 Dependencies

streamlit==1.31.1
polars==0.20.10
duckdb==0.10.0
plotly==5.18.0
fpdf2==2.7.8
requests==2.31.0
numpy==1.26.4
pillow==10.2.0

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👨‍💻 About the Developer

Built by Ali-datasmith — a data engineer specializing in supply chain analytics, real-time intelligence dashboards, and Python-based risk platforms.

Open to:

• Freelance projects — Upwork / Direct contract
• Consulting engagements for logistics & procurement firms
• Full-time Data Engineering / Analytics Engineering roles

📧 rjptmhmmd@gmail.com 🔗 GitHub Profile

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📜 License

MIT License — free to use, modify, and distribute with attribution.

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⚡ Built by Ali-datasmith

Polars · DuckDB · Streamlit · Plotly · Open-Meteo · FPDF2

"From raw supplier data to executive risk intelligence — in seconds."