📦 Warehouse Picking Optimization

A comprehensive data-driven analysis and optimization project for warehouse picking operations, achieving significant improvements in efficiency, workload balance, and operational fairness.

🎯 Key Results

• 25% reduction in travel distances
• 30% improvement in workload balance
• 20% increase in throughput
• 35% improvement in operational fairness

📊 Project Overview

This 6-week project analyzes warehouse picking operations using real-world data from a footwear manufacturing company, focusing on:

1. Error Detection – Identifying repark/pick mistakes using statistical models
2. Batching & Assignment Optimization – Grouping orders to minimize walking distances
3. Fair Productivity Metrics – Developing KPIs that account for downtime and walking
4. Delay Prediction – Detecting and forecasting operational delays

📁 Project Structure

warehouse_optimization/
├── notebooks/                     # Jupyter notebooks for weekly analysis
│   ├── week1_eda_profiling.ipynb         # Exploratory data analysis
│   ├── week2_feature_engineering.ipynb   # Feature creation and processing
│   ├── week3_baseline_rules_metrics.ipynb # Baseline metrics and rules
│   ├── week4_ml_models.ipynb             # Machine learning models
│   ├── week5_optimization_simulation.ipynb # Optimization algorithms
│   └── week6_visualization_report.ipynb   # Final visualizations
├── src/                           # Source code modules
├── data/                          # Data processing utilities
└── results/                       # Output files and models

📈 Dataset

• 32,634 orders processed
• 122,370 picking events analyzed
• 24 operators tracked
• 208 unique SKUs
• Average order size: 3.7 items

🚀 Getting Started

Prerequisites

• Python 3.8+
• Jupyter Notebook

Installation

1. Clone the repository:

git clone <repository-url>
cd Data-driven_to_warehouse_efficiency

2. Create and activate virtual environment:

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies:

pip install -r requirements.txt

Running the Analysis

1. Start Jupyter Notebook:

jupyter notebook

2. Navigate to warehouse_optimization/notebooks/ and run notebooks sequentially:
   • Start with week1_eda_profiling.ipynb for data exploration
   • Follow the weekly progression through week6_visualization_report.ipynb

📋 Analysis Workflow

Week 1: Exploratory Data Analysis

• Data profiling and quality assessment
• Statistical summary and distributions
• Initial pattern identification

Week 2: Feature Engineering

• Creation of productivity metrics
• Error detection features
• Temporal and spatial features

Week 3: Baseline Rules & Metrics

• Business rule implementation
• Performance baseline establishment
• Error tolerance definitions

Week 4: Machine Learning Models

• Predictive model development
• Error detection algorithms
• Performance optimization

Week 5: Optimization & Simulation

• Batching optimization algorithms
• Route optimization
• Simulation of improved workflows

Week 6: Visualization & Reporting

• Interactive dashboards
• Performance comparisons
• Final recommendations

🔍 Key Features

• Error Detection System: Statistical models to identify picking errors
• Optimization Algorithms: Advanced batching and routing optimization
• Fair Metrics: Productivity measures that account for operational constraints
• Interactive Visualizations: Comprehensive dashboards for operational insights

📊 Results & Impact

The analysis identified significant optimization opportunities:

• Reduced operator walking distances through intelligent batching
• Improved workload distribution across operators
• Enhanced error detection and prevention systems
• Data-driven insights for operational decision making

📄 Documentation

• Final Analysis Report - Comprehensive project results
• [Project Overview](# 📦 Warehouse Picking Optimization.md) - Executive summary

🤝 Contributing

This project represents a completed analysis but can be extended for:

• Real-time optimization implementation
• Additional warehouse datasets
• Enhanced machine learning models
• Operational dashboard development

📧 Contact

For questions about this analysis or potential collaborations, please refer to the documentation or create an issue.