💵 Landed Cost Model

Advanced total cost optimization for enterprise supply chain operations

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📋 Overview

Landed Cost Model addresses a critical challenge in modern supply chain management: total cost. 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 David Simchi-Levi, 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

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🏗️ Architecture

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

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❗ Problem Statement

The Challenge

Supply chain total cost is a persistent operational challenge that impacts cost, service, and working capital across the enterprise. Organizations that fail to optimize total cost 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."

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✅ Solution Methodology

Methodology

This implementation follows a structured analytical approach:

1. Data Ingestion & Validation — Load operational data, validate completeness, handle missing values and outliers
2. Exploratory Analysis — Statistical profiling, distribution analysis, correlation identification
3. Model Construction — Build the optimization/analytical model with configurable parameters and constraints
4. Solution Computation — Execute the algorithm with convergence checking and solution quality metrics
5. Results & Recommendations — Generate actionable outputs with sensitivity analysis and implementation guidance

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💻 Quick Start

Prerequisites

Requirement	Version
Python	3.8+
pip	Latest

Installation

git clone https://github.com/virbahu/landed-cost-model.git
cd landed-cost-model
pip install -r requirements.txt
python landed_cost_model.py

Usage

# Quick start example
from landed_cost_model import *

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

# Customize parameters
# See docstrings in landed_cost_model.py for full parameter reference

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📦 Dependencies

numpy
scipy
pandas
matplotlib

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📚 Academic Foundation

Based on	Professor David Simchi-Levi, MIT
Key Reference	Simchi-Levi (2010) Operations Rules. MIT Press
Domain	Total Cost

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👤 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
🌍 Scope	Supply chain operations on 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