♟️ Network Optimization Ai

AI-driven supply chain network optimization with scenario analysis

A Quantisage Open Source Project — Enterprise-grade supply chain intelligence

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📋 Table of Contents

• Overview
• Architecture
• Problem Statement
• Solution Deep Dive
• Quick Start
• Code Examples
• Academic Foundation
• Author

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📋 Overview

Network Optimization Ai addresses a critical challenge in modern supply chain management. This implementation combines rigorous academic methodology with production-ready Python code designed for enterprise deployment.

Based on: Professor Mark Daskin, University of Michigan

AI-driven supply chain network optimization with scenario analysis. In today's volatile supply chain environment — marked by geopolitical disruptions, climate risks, demand volatility, and rapid digitization — organizations need tools that go beyond traditional spreadsheet-based analysis.

✨ Key Capabilities

• Production-ready Python implementation with clean, extensible architecture
• Academically grounded methodology from world-class research institutions
• Configurable parameters for enterprise-scale operations (1K to 100K+ SKUs)
• Comprehensive output metrics with sensitivity analysis and trade-off curves
• API-ready design for integration with ERP, WMS, TMS, and planning systems
• Fully transparent algorithms — no black boxes, every decision is explainable

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🏗️ Architecture

flowchart LR
    A[📥 Input Data] --> B[⚙️ Processing]
    B --> C[🔢 Optimization]
    C --> D[📊 Results]
    D --> E[📋 Actions]
    style C fill:#fff9c4
    style E fill:#c8e6c9

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Process Flow

graph LR
    A[Input] --> B[Analyze]
    B --> C[Optimize]
    C --> D[Execute]
    D --> E[Monitor]
    E -->|Feedback| B
    style C fill:#fff9c4

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❗ Problem Statement

The Challenge

Supply chain strategy is a persistent operational challenge with direct impact on cost, service, and resilience:

Impact Area	Without Optimization	With Optimization	Improvement
Cost	Baseline	15-30% reduction	Significant
Service Level	85-90%	96-99%	+6-14 pts
Working Capital	Over-invested	Right-sized	20-40% freed
Decision Speed	Days/weeks	Minutes/hours	10-50x faster
Risk Exposure	Reactive	Proactive	60-80% fewer disruptions

The complexity compounds when you consider:

• Scale: Thousands of SKUs × hundreds of locations × 365 days = millions of decisions per year
• Uncertainty: Demand volatility, supply disruptions, lead time variability, price fluctuations
• Dependencies: Upstream and downstream ripple effects across multi-tier networks
• Constraints: Capacity limits, budget constraints, regulatory requirements, sustainability targets

"Supply chains compete, not companies. The supply chain that can sense, plan, and respond fastest — wins."

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✅ Solution Deep Dive

Methodology

This implementation follows a structured six-phase approach:

1. Data Ingestion & Validation — Load operational data, validate completeness, handle missing values, detect outliers
2. Exploratory Analysis — Statistical profiling, distribution analysis, correlation identification, pattern detection
3. Model Construction — Build the core analytical model with configurable parameters and business rule constraints
4. Solution Computation — Execute the algorithm with convergence monitoring and solution quality metrics
5. Sensitivity Analysis — Systematic parameter variation to understand solution robustness and critical drivers
6. Results & Deployment — Generate actionable outputs with clear recommendations and expected impact quantification

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🚀 Quick Start

Prerequisites

Requirement	Version	Purpose
Python	3.9+	Runtime
pip	Latest	Package management
Git	2.0+	Version control

Installation

# Clone the repository
git clone https://github.com/virbahu/network-optimization-ai.git
cd network-optimization-ai

# Create virtual environment (recommended)
python -m venv .venv
source .venv/bin/activate  # Linux/Mac
# .venv\Scripts\activate   # Windows

# Install dependencies
pip install -r requirements.txt

# Run
python network_opt_ai.py

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💻 Code Examples

Basic Usage

from network_optimization_ai import *

# Run with default parameters
result = main()
print(result)

Advanced Configuration

# Customize for your environment
# See source code docstrings for full parameter reference

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📦 Dependencies

numpy
scipy

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📚 Academic Foundation

Based on: Professor Mark Daskin, University of Michigan

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👤 About the Author

Virbahu Jain — Founder & CEO, Quantisage

Building the AI Operating System for Scope 3 emissions management and supply chain decarbonization.

🎓 Education	MBA, Kellogg School of Management, Northwestern University
🏭 Experience	20+ years across manufacturing, life sciences, energy & public sector
🌍 Global Reach	Supply chain operations across five continents
📝 Research	Peer-reviewed publications on AI in sustainable supply chains

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📄 License

MIT License — see LICENSE for details.

_{Part of the Quantisage Open Source Initiative | AI × Supply Chain × Climate}