A few weeks ago, my boss Victor Ugwu shared a raw supply chain dataset with me. No instructions. No predefined questions. Just one brief: “See what you can uncover.” Instead of treating it as routine reporting, I approached it as a real business problem: How can leadership use this data to reduce risk, cut costs, and grow profitably? That mindset led to the Global Supply Chain Operations & Risk Intelligence Dashboard, built entirely in Excel. At first glance, the data looked fine. But beneath the tables were silent issues holding performance back: rising logistics costs, inconsistent carrier performance, hidden supplier quality risks, inventory imbalance across product lines, and revenue concentration in just a few regions. None of these were obvious from raw spreadsheets, so I redesigned the analysis around decision-making, not reporting. Revenue visibility came first. By visualizing regional contributions, leadership can clearly see where growth is coming from. Cities like Mumbai and Kolkata emerge as key drivers, while other regions highlight the need for better distribution or demand stimulation. With $577.6K in total revenue and $46.1K in products sold, investment decisions become sharper and more focused. Logistics efficiency followed. Comparing transport costs with delivery times showed that air freight is fast but expensive, while sea and rail offer better cost efficiency. This helps answer a critical question: Where can we cut costs without slowing deliveries? Carrier performance revealed another insight. A one-day delivery difference may seem small, but at scale it affects customer satisfaction, inventory turnover, and holding costs. Making this visible enables smarter contract negotiations and volume rebalancing. Inventory health also stood out. Stock levels varied across product lines cosmetics (59), haircare (48), skincare (40) highlighting risks of both overstock and stockouts. One of the most critical findings was supplier quality risk, with some suppliers showing defect rates as high as 52%. Instead of reacting after complaints or returns, teams can now identify and mitigate risks early. This analysis was done using Excel, Power Query, Pivot Tables, and calculated KPIs. The result isn’t just a dashboard, it’s a decision engine that delivers clarity, cost savings, early risk detection, and smarter growth decisions. If you’re a founder or operations leader dealing with rising costs or poor visibility, I help turn your data into clear, practical actions. 📩 DM me if you want to uncover the growth opportunities hidden in your data.