Refer the full report which provides a comprehensive overview of the vendor performance analysis project, detailing the methodologies, data-driven insights, and strategic recommendations derived from the provided sources.
Key Concepts and Methodology The project utilized a robust end-to-end data engineering and analysis pipeline, incorporating several key technical concepts:
- ETL (Extract, Transform, Load): Data was ingested from CSV files into a SQLite database using SQLAlchemy. To ensure memory efficiency, a chunking technique was implemented to read 10,000 rows at a time.
- Data Cleaning and Feature Engineering: Data types were standardized (e.g., Volume to float64), and null values were replaced with zero. New performance measures were created, including Gross Profit, Profit Margin, Stock Turnover, and Sales-Purchase Ratio.
- Exploratory Data Analysis (EDA): Insights were gathered through distribution plots (histograms, KDE), outlier detection using boxplots, and correlation heatmaps to understand relationships between variables like purchase price and profitability.
- Statistical Analysis: Advanced techniques included Quantile analysis for performance thresholds, 95% Confidence Intervals for profit margins, and Hypothesis Testing (Two-sample T-test) to determine statistical significance between vendor groups.
- Visualization Techniques: The project employed Quadrant analysis for brand performance, Pareto charts for vendor contribution, and Donut charts for procurement dependency
Business Problem
Effective inventory and sales management are critical for optimizing profitability in the retail and wholesale industry. Companies need to ensure that they are not incurring losses due to inefficient pricing, poor inventory turnover, or vendor dependency.
The goal of this analysis is to:
- Identify underperforming brands that require promotional or pricing adjustments.
- Determine top vendors contributing to sales and gross profit.
- Analyze the impact of bulk purchasing on unit costs.
- Assess inventory turnover to reduce holding costs and improve efficiency.
- Investigate the profitability variance between high-performing and low-performing vendors.