🚛 LSP Digital Capacity Twin: Multi-Modal Stochastic Engine

Author: Sandesh Hegde
Version: v3.8.0 (Strategic Research Edition)

🚀 Live Demo

Click here to launch the Research Artifact (Note: Hosted on Render Free Tier. Please allow 30s for cold start.)

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📖 Executive Summary

This artifact operationalizes the "Pixels to Premiums" research framework, serving as a Decision Support System (DSS) for Logistics Service Providers (LSPs). It now includes a Global Sourcing Simulator to quantify the "China Plus One" strategy and the impact of Free Trade Agreements (FTAs).

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🧮 Methodological Framework

1. Multi-Modal Trade-off Logic

The engine applies strategic multipliers to simulate mode-specific constraints. The "Iron Triangle" of Logistics is modeled as:

• Lead Time ($L_m$): $L_{base} \times M_{time}$ (e.g., Air = 0.2x, Rail = 1.5x)
• Unit Cost ($C_m$): $C_{base} \times M_{cost}$ (e.g., Air = 3.0x, Rail = 0.7x)
• Emissions ($E_m$): $E_{base} \times M_{co2}$ (e.g., Air = 5.0x, Rail = 0.3x)

2. Total Landed Cost (TLC) Model (New!)

To compare Domestic vs. Offshore sourcing, the system calculates the hidden cost of risk:

$$\text{TLC} = \text{Base Price} + \text{Freight} + \text{Duty} + \left( \frac{\text{Lead Time}}{365} \times \text{Demand} \times \text{Holding Rate} \right)$$

3. Monte Carlo Risk Engine

To quantify financial tail risk, the system runs 10,000 stochastic iterations for every scenario. Instead of a single "average" profit, we generate a probability distribution:

$$P_{sim} = (D_{stoch} \cdot SP) - (Q_{order} \cdot UC_{mode}) - (I_{safety} \cdot H) - (S_{missed} \cdot \pi)$$

• Metric: Value at Risk (VaR 95%) = The worst 5% financial outcome.

4. Volatility Modelling (RSS)

The system calculates Risk-Adjusted Safety Stock using a Root Sum of Squares approach, integrating demand variability ($\sigma_{D}$) and lead time variability ($\sigma_{LT}$):

$$\text{Safety Stock} = Z_{\alpha}\sqrt{(\overline{L}\sigma_{D}^{2})+(\overline{D}^{2}\sigma_{LT}^{2})}$$

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🚀 Key Features (v3.8.0)

🚚 1. Multi-Modal Transport Engine

• Mode Selection: Toggle between Road (Standard), Rail (Green/Slow), and Air (Express/Costly).
• Dynamic Economics: "Air Mode" instantly triples costs and spikes CO2, but slashes lead time to near zero.
• Impact Analysis: See how switching to Rail affects your stockout risk due to slower replenishment.

🌏 2. Global Sourcing Strategy (New in v3.8!)

• "China Plus One" Simulator: Compares Domestic/Nearshore sourcing against Offshore (e.g., India) sourcing.
• Trade Policy Lever: Interactive Tariff slider (0-20%) to test the viability of Free Trade Agreements (FTAs).
• Risk vs. Reward: Visualizes the "tipping point" where logistics risk outweighs labor cost savings.

🌍 3. Strategic Scorecard (Triple Bottom Line)

• Sustainability: Tracks CO2 Emissions (kg) and calculates "Green Savings" from modal shifts.
• Customer Loyalty: Dynamic score based on Fill Rate reliability vs. SLA targets.
• Resilience Score: Composite index (0-100) measuring network robustness.

🌪️ 4. Disruption Simulator

• "Stress Test" Mode: Simulates a Supply Chain Shock (e.g., Port Strike).
• Real-Time Impact: Instantly doubles lead time variance ($\sigma_{LT}$), crashing Resilience Scores in real-time.

🔬 5. Research Laboratory (Enhanced)

• Stochastic Validation: Runs 10,000 iterations to prove the "Newsvendor Optimal" strategy.
• Loss Predictability: New metric quantifying the Probability of Loss (%) to identify dangerous cost structures.
• Profit Heatmaps: Visualizes the "Zone of Profitability" across demand and volatility scenarios.

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⚙️ Technical Architecture

• Core Logic: numpy (Monte Carlo) & scipy.stats (Stochastic Calculus).
• Intelligence Layer: Google Gemini 1.5 Flash (via ai_brain.py) for strategic context.
• Visualization: plotly.graph_objects (Geospatial & Risk Histograms).
• CI/CD: GitHub Actions (Automated Testing & Deployment).
• Frontend: Streamlit (React-based) for interactive simulation.

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🚀 Installation & Usage

Prerequisites

You need a Google AI Studio API Key.

Option A: Run Locally (Python)

# 1. Clone the repository
git clone https://github.com/sandesh-s-hegde/digital_capacity_optimizer.git
cd digital_capacity_optimizer

# 2. Install dependencies
pip install -r requirements.txt

# 3. Set your API Key (Create a .env file)
# GOOGLE_API_KEY="AIzaSy..."
# DATABASE_URL="postgresql://..."

# 4. Generate Research Data (Optional)
python seed_data.py

# 5. Launch the Digital Twin
streamlit run app.py

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📄 Citation

If you use this software in your research, please cite it as follows:

Harvard Style:

Hegde, S.S. (2026). LSP Digital Capacity Twin: Multi-Modal Stochastic Engine (Version 3.8.0) [Software]. Available at: https://github.com/sandesh-s-hegde/digital_capacity_optimizer

BibTeX:

@software {Hegde_LSP_Digital_Twin_2026,
  author = {Hegde, Sandesh Subramanya},
  month = feb,
  title = {LSP Digital Capacity Twin: Multi-Modal Stochastic Engine},
  url = {(https://github.com/sandesh-s-hegde/digital_capacity_optimizer)},
  version = {3.8.0},
  year = {2026}
}

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🔮 Roadmap

Phase	Maturity Level	Key Capabilities	Status
Phase 1	Descriptive	Static Rule-Based Logic (EOQ)	✅ Done
Phase 2	Predictive	Cloud Database + Forecasting	✅ Done
Phase 3	Stochastic	Multi-Modal, Monte Carlo & Resilience, Research	✅ Released (v3.8)
Phase 4	Strategic	Global Sourcing (FTA) & Trade Policy	✅ Released (v3.8)
Phase 5	Autonomous	Multi-Echelon Reinforcement Learning	🚧 Planned