AI-Factory-Optimization-System

Enterprise AI-powered factory optimization and recommendation system using Machine Learning.

🏭 AI Factory Optimization System

Enterprise AI-powered factory optimization and recommendation system using Machine Learning, Data Analytics, and Streamlit Dashboard.

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📌 Project Overview

The AI Factory Optimization System is designed to improve operational efficiency by predicting factory lead times and recommending the best-performing factory using Machine Learning algorithms.

The system analyzes operational parameters such as:

• Distance
• Cost
• Sales
• Shipping Mode
• Region
• Units Sold

and generates business insights for smarter logistics and production decisions.

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🚀 Key Features

✅ Machine Learning-based lead-time prediction ✅ Factory recommendation engine ✅ Business analytics dashboard ✅ Interactive Streamlit web application ✅ Model comparison and evaluation ✅ Feature importance analysis ✅ Data visualization and reporting ✅ Enterprise-style AI project architecture

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📊 Dataset Information

Dataset Used:

• Nassau Candy Distributor Dataset

The dataset contains:

• Product information
• Shipping details
• Sales data
• Regional data
• Operational metrics
• Factory performance indicators

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🧠 Machine Learning Models Used

The following models were implemented and compared:

Model	Purpose
Linear Regression	Baseline prediction model
Random Forest Regressor	Ensemble learning model
Gradient Boosting Regressor	Advanced boosting model

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📈 Model Evaluation Metrics

The models were evaluated using:

• MAE (Mean Absolute Error)
• RMSE (Root Mean Squared Error)
• R² Score

These metrics help measure prediction accuracy and operational reliability.

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📌 Business Insights

Key findings from the analysis:

• Distance is the strongest operational factor
• Cost and sales influence lead-time performance
• Factory selection significantly impacts efficiency
• AI can improve logistics optimization and operational planning

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🖥️ Streamlit Dashboard

The project includes an interactive Streamlit dashboard with:

• Factory performance table
• Lead-time analysis charts
• Efficiency comparison graphs
• Recommendation system outputs
• Business intelligence visualizations

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📷 Dashboard Preview

AI Dashboard

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🛠️ Technologies Used

• Python
• Pandas
• NumPy
• Scikit-learn
• Matplotlib
• Streamlit
• GitHub

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📂 Project Structure

AI-Factory-Optimization-System/
│
├── app/
│   └── streamlit_app.py
│
├── data/
│   └── Nassau Candy Distributor.csv
│
├── notebooks/
│   ├── eda.ipynb
│   └── notebook724f8e6ca1.ipynb
│
├── outputs/
│   └── factory_recommendations.csv
│
├── ppt/
│   └── FINAL_Enhanced_AI_Factory_Optimization_PPT.pptx
│
├── reports/
│   └── screenshots/
│       ├── dashboard.png
│       ├── dataset_loaded.png
│       ├── feature_importance.png
│       └── model_comparison.png
│
└── README.md

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⚙️ Installation & Setup

Clone Repository

git clone https://github.com/Shauryaveer02/AI-Factory-Optimization-System.git

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Open Project Folder

cd AI-Factory-Optimization-System

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Install Dependencies

pip install pandas numpy scikit-learn matplotlib streamlit

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Run Streamlit Dashboard

python -m streamlit run app/streamlit_app.py

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🌐 Deployed Streamlit App

https://ai-factory-optimization-system-candy-project.streamlit.app/

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📌 GitHub Repository

https://github.com/Shauryaveer02/AI-Factory-Optimization-System

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🎯 Future Improvements

• Cloud deployment integration
• Real-time logistics tracking
• Advanced AI optimization
• SQL database integration
• Deep learning implementation
• API integration for live factory analytics

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📚 Learning Outcomes

Through this project, I gained practical experience in:

• Machine Learning workflows
• Data preprocessing
• Business analytics
• Dashboard development
• Model evaluation
• GitHub project management
• Enterprise AI system design

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👨‍💻 Author

Shaurya Veer

AI/ML Enthusiast | Data Analytics | Business Intelligence

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## Project Preview ## Technologies Used - Python - Pandas - NumPy - Scikit-Learn - Matplotlib - Kaggle Notebook

Key Features

• Factory recommendation engine
• Lead time prediction
• Logistics optimization
• Feature importance analysis
• AI-based operational insights