UPS delivers resilience, flexibility with predictive analytics

The logistics titan has created a business intelligence platform that uses advanced analytics and machine learning to support forecasting, operations visibility, optimization, and reporting.

managing binary clusters of cubes / data blocks
Cemile Bingol / Getty Images

On any given day, UPS delivers roughly 21 million packages (far more in December), creating millions upon millions of data points as it tracks the real-time status of each of those packages as they move across the company’s shipping network.

In the past, UPS relied on historical data and know-how from expert planners to track package status, but that approach was imprecise and scaled poorly.

“The processes and approaches of the past are not going to help UPS adjust to the very dynamic nature of supply chain management today,” says UPS chief information and engineering officer (CIEO) Juan Perez. “Our customers today have much more complex supply chains. The movement of shipments from shippers to receivers is getting much more complex because of the way that products are distributed across the network. The customers are demanding more precision in terms of when we deliver things. The tolerance for failure in these networks is lower and lower.”

A single source of truth

To get a better handle on its data, UPS created the Harmonized Enterprise Analytics Tool (HEAT), a business intelligence platform built on Google Cloud with the capacity to run on more than 1 billion data points per day. It captures and analyzes customer data, operational data, and planning data, and can continue adding new events as they happen in the lifecycle of a package.  UPS has won a CIO 100 Award in IT Excellence for HEAT.

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