Slooze Inventory Analysis

Project Overview

This project performs inventory, sales, and purchase analysis for a retail wine & spirits company.

The goal is to:

• Optimize inventory levels
• Perform ABC analysis
• Forecast demand
• Calculate EOQ
• Analyze supplier lead time
• Improve procurement efficiency

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Dataset

Sample dataset is included (reduced size for GitHub upload).

Original dataset was large and trimmed for repository size compliance.

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Analysis Performed

1. Demand Forecasting

• Time-series based monthly sales aggregation
• Revenue trend visualization

2. ABC Analysis

• Inventory categorized into A, B, C classes
• Prioritization based on contribution

3. EOQ Calculation

• Economic order quantity optimization
• Holding vs ordering cost comparison

4. Reorder Point

• Safety stock consideration
• Lead time based reorder strategy

5. Lead Time Analysis

• Supplier efficiency evaluation

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Project Structure

data/ → Input datasets
inventory_analysis.py → Python analysis script
outputs/ → Generated results and visualizations
notebooks/ → Optional exploration

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How to Run

1. Install dependencies: pip install -r requirement.txt

2. Run: python inventory_analysis.py

What I Learned

• Applied demand forecasting using historical sales trends.
• Implemented ABC analysis to prioritize high-value inventory.
• Calculated Economic Order Quantity (EOQ) for cost optimization.
• Designed reorder point logic using lead time and demand.
• Translated business problems into data-driven solutions.

Key Insights

• A small percentage of products generate majority of revenue (Pareto principle observed).
• Certain products show irregular demand patterns.
• Reorder point logic helps prevent stockouts.
• EOQ reduces unnecessary ordering frequency.