Exploring global supply chain performance across revenue, logistics, and customer behavior using Power BI.
While analyzing a global supply chain dataset with over 180,000 order records, one issue stood out:
Nearly 35% of all orders were delivered later than scheduled.
This raises important questions around logistics efficiency, operational planning, and delivery performance.
The dataset was structured using a star schema to enable efficient and scalable analysis across multiple dimensions.
A high-level summary of revenue, orders, profit, and delivery performance across global markets.
Analysis of delivery delays, shipping modes, and operational efficiency across regions.
Evaluation of product categories and top-performing products by revenue and profitability.
Insights into customer behavior, revenue distribution, and market-level performance.
~35% of orders are delayed, indicating operational inefficiencies in fulfillment and logistics processes.
Revenue is concentrated in a few product categories, showing opportunities for diversification.
Standard shipping mode dominates order volume but accounts for most delays.
Customer revenue follows a long-tail distribution, with a small number of high-value customers driving significant revenue.