Are Hidden Blind Spots Costing Your Supply Chain Millions?

Manufacturers that rely solely on internal data often miss the bigger picture. Without visibility into global risks, supplier performance, and real-time movements, you’re left reacting to disruptions rather than preventing them.

External data—ranging from IoT sensors to ESG reports and regulatory updates—enables manufacturers to map, monitor, and manage their supply chains end-to-end. That means fewer surprises, better decisions, and a more resilient operation.

Why External Data Drives Greater Supply Chain Transparency

✅ Visualize your full supply chain—including subcontractors and raw material sources
✅ Monitor global events, ESG risks, and market trends that affect delivery and sourcing
✅ Get real-time tracking of inventory, shipments, and suppliers
✅ Ensure authenticity, compliance, and sustainability across every link in the chain
✅ Reduce disruption costs and increase production agility

How It Works

🔹 Gradient Boosting Machines analyze supplier performance to detect early signs of risk
🔹 Monte Carlo simulations test transportation scenarios for cost, speed, and disruption sensitivity
🔹 ESG and labor practice data support supplier audits and transparency goals
🔹 Sensor data from IoT devices and GPS ensures real-time traceability of shipments

Real-World Impact: 5.3 Petabytes of Visibility

UPS delivers 21 million packages a day—but with HEAT, its enterprise analytics engine, it now tracks every one in real time using customer data, planning tools, and operational feeds. That’s 5.3 petabytes of insight weekly—turned into faster decisions and fewer delivery failures.

📩 Want to build a smarter, more transparent supply chain? Let’s talk about how external data can help you eliminate blind spots and protect your bottom line

Live Webinar

Is “Quality” Killing Your AI? Defining Data Fit for Strategic Success

February 18th, 2026 / 1:00 PM EST

Every data investment carries risk unless you know how to measure its “fit” for the mission. Many organizations assume that “high-quality” data is sufficient for AI and analytics, only to discover too late that data fit is the real determinant of success. In this live session from Blue Street Data’s Building with Better Data series, Andy Hannah and Malcolm Hawker unpack why data that works for BI can be dangerous for AI, leading to model failure, wasted spend, and lost trust. You’ll learn how to define, measure, and validate data fit so your models deliver reliable, business-aligned outcomes. Reserve your spot today!