Real-time Analytics · Supply Chain Optimization
GLS needed to replace a high-maintenance on-prem analytics platform that could explain past delays but could not predict future delivery problems, and whose slow queries, outages, and operational overhead limited the company’s ability to provide dependable forecasts to customers.
Reported impact
GLS reduced report-generation time by 92%, lowered total operating expenditure by 50%, improved some query performance by up to 10x, cut last-mile report preparation from 12 hours to 1 hour, and gave customers more reliable delivery forecasts through faster, more transparent analytics.
GLS rebuilt its analytics estate on Snowflake's AI Data Cloud running on AWS and paired the migration with a data-mesh operating model. Snowflake now serves as the company's cloud-only database and central analytics store, giving satellite teams governed access to data instead of routing every report through one central team. Because Snowflake scales on demand and requires near-zero maintenance, large queries no longer disrupt operations and the platform supports predictive-delivery use cases alongside routine reporting. GLS also added AI-assisted natural-language access that converts written questions into SQL and visualizations, helping business users get answers without learning complex interfaces. The combination of cloud scalability, shared data ownership, and low-friction analytics delivery cut reporting times dramatically, lowered total operating costs, and improved the reliability of delivery forecasts across a parcel network that spans 220 countries and territories.
Parcel and operational data is centralized in Snowflake on AWS, shared to domain teams through a data-mesh model, queried directly or through natural-language AI assistance, and turned into last-mile and closing reports that support delivery forecasting and customer updates.
GLS replaced its existing database management system with Snowflake’s AI Data Cloud on Amazon Web Services (AWS).
We have more transparency in our process now. We are faster in seeing where things went wrong and we can look into the future.
With Snowflake, GLS has been able to bring new efficiencies to its analytics work, including a reduction in the time required to create last-mile reports from 12 hours to just one.
With Snowflake’s 99.9% uptime and ability to scale on demand, GLS no longer has to battle outages.
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Scoring methodology
Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.
Traceability
Field coverage shows which facts each source supports. Dates are shown only when documented by the source.
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