Advanced risk pooling optimization for enterprise supply chain operations
Inventory Pooling Model addresses a critical challenge in modern supply chain management: risk pooling. This implementation combines rigorous academic methodology with production-ready Python code, suitable for both research and enterprise deployment.
Built on the foundational work of Professor Edward Silver, this tool provides supply chain professionals with an analytical framework that transforms raw operational data into actionable optimization decisions. Whether you're managing a single warehouse or a global multi-echelon network, this toolkit scales to your complexity.
The solution follows industry best practices from APICS/ASCM, CSCMP, and ISM frameworks, implemented with clean, extensible Python code that integrates with existing ERP, WMS, and TMS systems.
Key capabilities:
- Configurable parameters for enterprise-scale operations
- Production-ready Python implementation with clean architecture
- Academic rigor with peer-reviewed methodology foundation
- Extensible design for custom business rules and constraints
- Comprehensive output metrics with sensitivity analysis
flowchart LR
A[📥 Input\nData] --> B[⚙️ Processing &\nAnalysis]
B --> C[🔢 Optimization\nEngine]
C --> D[📊 Results &\nMetrics]
D --> E[📋 Recommendations\n& Actions]
style C fill:#fff9c4
style E fill:#c8e6c9
Supply chain risk pooling is a persistent operational challenge that impacts cost, service, and working capital across the enterprise. Organizations that fail to optimize risk pooling typically see:
| Impact Area | Without Optimization | With Optimization | Improvement |
|---|---|---|---|
| Cost | Baseline | 15-30% reduction | Significant |
| Service Level | 85-90% | 95-99% | +5-14 pts |
| Working Capital | Over-invested | Right-sized | 20-40% freed |
| Decision Speed | Days/weeks | Minutes/hours | 10-50x faster |
"The goal is not to optimize individual functions, but to optimize the entire supply chain system — which often means sub-optimizing individual nodes for the benefit of the whole."
This implementation follows a structured analytical approach:
- Data Ingestion & Validation — Load operational data, validate completeness, handle missing values and outliers
- Exploratory Analysis — Statistical profiling, distribution analysis, correlation identification
- Model Construction — Build the optimization/analytical model with configurable parameters and constraints
- Solution Computation — Execute the algorithm with convergence checking and solution quality metrics
- Results & Recommendations — Generate actionable outputs with sensitivity analysis and implementation guidance
| Requirement | Version |
|---|---|
| Python | 3.8+ |
| pip | Latest |
git clone https://github.com/virbahu/inventory-pooling-model.git
cd inventory-pooling-model
pip install -r requirements.txt
python inventory_pooling_model.py# Quick start example
from inventory_pooling_model import *
# Run with default parameters
result = main()
print(result)
# Customize parameters
# See docstrings in inventory_pooling_model.py for full parameter referencenumpy
scipy
pandas
matplotlib
| Based on | Professor Edward Silver, University of Calgary |
| Key Reference | Silver et al. (2017) Inventory and Production Management in Supply Chains. CRC Press |
| Domain | Risk Pooling |
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 |
| 🌍 Scope | Supply chain operations on five continents |
| 📝 Research | Peer-reviewed publications on AI in sustainable supply chains |
MIT License — see LICENSE for details.
Part of the Quantisage Open Source Initiative | AI × Supply Chain × Climate