Purchasing Data Analysis

Overview

This project provides an in-depth analysis of purchasing data from 2023 to 2024. The analysis aims to:

1. Identify class-A items using the Pareto principle (80/20 rule).
2. Calculate safety stock and reorder points (ROP) for these items.
3. Visualize purchasing trends over time for better decision-making.

The project demonstrates the use of Python for data cleaning, analysis, and visualization, showcasing skills in data manipulation, trend analysis, and optimization.

Features

• Class-A Item Identification: Focus on high-priority items contributing most to purchasing activities.
• ROP Calculation: Provide accurate reorder points using safety stock and demand forecasting.
• Purchasing Trend Visualization: Line plots and charts to highlight monthly and yearly trends.

Tools & Libraries

• Pandas: For data manipulation and aggregation.
• Matplotlib & Seaborn: For data visualization.
• Jupyter Notebook: Interactive environment for coding and documentation.

File Structure

• purchasing-analysis.ipynb: Main notebook containing the analysis and visualizations.
• data/: Contains the purchasing data files (not uploaded for confidentiality).
• output/: Includes exported files like summary CSVs.

Usage

1. Clone the repository:

   git clone https://github.com/yourusername/purchasing-data-analysis.git
   

2. Install required libraries:

    pip install pandas matplotlib seaborn
   

3. Open the notebook and run the cells to reproduce the analysis:

   jupyter notebook purchasing-analysis.ipynb  
   
   

Acknowledgments

Special thanks to Mr. Yasser Al Nawaghi for his support in understanding the data and contributing to the success of this analysis.

License

This project is licensed under the MIT License.