Supply Chain Decision Engine

An interactive supply chain optimization and analytics project designed to support data-driven operational decision-making through linear programming, scenario simulation, and visualization.

---

Project Overview

This project demonstrates how optimization and analytics can improve supply chain planning across a network of plants and markets.

The workflow includes:

• Data preprocessing
• Linear programming optimization
• Scenario simulation
• Data visualization
• Interactive dashboard development

The model determines the most profitable shipment strategy while considering operational constraints such as production capacity, transportation costs, and market demand.

---

Business Problem

Supply chain teams often struggle to evaluate:

• Demand fluctuations
• Transportation cost changes
• Inventory bottlenecks
• Profit tradeoffs
• Capacity allocation across plants and markets

This project helps visualize and optimize these scenarios interactively, enabling more informed operational and strategic decisions.

---

Key Features

• Linear programming optimization model
• Shipment allocation optimization
• Scenario-based simulation analysis
• Supply chain network visualization
• Interactive Streamlit dashboard
• Operational KPI tracking

---

Tech Stack

• Python
• Pandas
• Streamlit
• Plotly
• PuLP
• NetworkX
• Matplotlib

---

Visualizations

Supply Chain Network Graph

---

Top Shipment Lanes

---

Plant Utilization

---

Scenario Profit Comparison

---

Key Insights

• Identified high-volume shipment lanes across the network
• Visualized plant utilization and operational bottlenecks
• Compared profitability under different supply chain scenarios
• Enabled scenario-based operational planning and decision-making

---

Run the Project

Step 1 — Preprocess Data

python src/preprocess.py

Step 2 — Run Optimization Model

python src/optimization_model.py

Step 3 — Run Scenario Simulation

python src/scenario_simulation.py

Step 4 — Generate Visualizations

python src/visualize_results.py
python src/network_graph.py

Step 5 — Launch Interactive Dashboard

streamlit run dashboard/app.py

---

Project Goal

The goal of this project is to demonstrate how analytics, optimization, and interactive visualization can support real-world supply chain decision-making and operational strategy.