🤖 Warehouse Automation and Robotics Integration Project

📌 Overview

This project analyzes the impact of robotics integration—such as Amazon’s touch-sensing robot “Vulcan”—on warehouse operational efficiency and workforce dynamics. Using real-world-inspired data, we assess how robotics affects cost savings, productivity, safety, and human-to-robot collaboration within fulfillment centers.

Business Analysts play a key role in identifying underperforming warehouses and recommending data-driven strategies for optimization.

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🎯 Objectives

• Quantify the operational impact of robotics installation in warehouses.
• Predict cost savings based on robotics and workforce metrics.
• Identify underperforming fulfillment centers.
• Provide actionable insights to maximize ROI and minimize inefficiencies.

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📂 Dataset

The dataset (warehouse_robotics_data.csv) includes information about robotic deployment, workforce distribution, and performance indicators.

Key Columns:

• Center_ID
• Robotics_Installed (1 = Yes, 0 = No)
• Robots_Count
• Operational_Hours
• Human_Workforce_Count
• Incidents_Reported
• Processing_Volume_Units
• Avg_Pick_Time_sec
• Cost_Saving_USD

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🧠 Tech Stack

• Python: Core programming
• Pandas, NumPy: Data manipulation
• Matplotlib, Seaborn: Visualization
• Scikit-learn: Modeling and evaluation
• Joblib: Model serialization

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📊 Exploratory Data Analysis (EDA)

• Created metrics such as Robot_Density, Volume_per_Hour, and Human_to_Robot_Ratio.
• Compared cost savings in warehouses with and without robotics using box plots.
• Evaluated correlations between features using heatmaps.

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🔍 Modeling

• Used Random Forest Regressor to predict Cost_Saving_USD.
• Achieved model evaluation using:
  • RMSE (Root Mean Squared Error)
  • R² Score

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📈 Output Files

• optimized_robotics_data.csv: Post-processed dataset with engineered features.
• robotics_optimization_suggestions.csv: List of underperforming centers with suggestions.
• robotics_efficiency_model.pkl: Trained predictive model.

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✅ Recommendations Engine

A custom function identifies centers with lower-than-average cost savings and recommends potential robotic scaling strategies based on performance.

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🚀 How to Run This Project

# Clone the repo
git clone https://github.com/yourusername/warehouse-robotics-analytics.git

# Install required libraries
pip install -r requirements.txt

# Run the Python script or Jupyter Notebook
python Warehouse_robotics_analysis.py    '

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📜 License

This project is licensed under the MIT License.

👨‍💻 Author

Harsh Sonkar

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