Vendor Performance Analysis

📌 Project Overview

This project focuses on analyzing vendor performance in the retail/wholesale industry to optimize profitability, inventory turnover, and pricing strategy. Using Python for data analysis and Power BI for visualization, the project identifies underperforming brands, evaluates vendor contributions, and provides actionable insights for decision-making.

🎯 Objectives*

• Identify underperforming vendors and brands requiring pricing or promotional adjustments.

• Evaluate vendor contributions to overall sales and profitability.

• Assess impact of bulk purchasing on cost savings.

• Analyze inventory turnover and unsold stock.

• Compare profitability between high-performing and low-performing vendors.

📈 Key Insights

• Bulk Purchasing Advantage: Vendors buying in large quantities achieved 72% lower unit cost.

• Vendor Dependency: Top 10 vendors contribute ~66% of purchases, indicating supply chain risks.

• Unsold Inventory: $2.71M tied up in slow-moving stock.

• Profitability Differences: High-margin vendors struggle with sales volume, while top vendors balance cost efficiency with volume.

🛠️ Tools & Technologies

• Python (Pandas, NumPy, Matplotlib) – Data cleaning, EDA, and statistical analysis.

• SQL – Data extraction and filtering.

• Excel – Exploratory calculations and initial data exploration.

• Power BI – Interactive dashboard for visualization & insights.

📊 Dashboard Preview

• The Power BI dashboard highlights:

• Top vendors & brands by sales and gross profit.

• Vendor contribution percentages.

• Low-performing vendors/brands.

• Inventory turnover and unsold capital