Real-time Analytics · Supply Chain Optimization
Puma Energy needed a better way to support global fuel stockout prediction, safety reporting, and customer analytics because fragmented regional systems and slow refresh cycles meant frontline teams waited hours for insights and could not prevent fuel disruptions fast enough.
Reported impact
Puma Energy improved global fuel stockout prediction, safety reporting, and customer analytics with measurable results including 98-99% modernizing fuel access in underserved communities, Decrease in data latency, going from 4–5 hours to under 5 minutes, and 20%+, while giving teams a faster path from governed data to production decisions.
Puma Energy uses Databricks Data + AI Platform to support global fuel stockout prediction, safety reporting, and customer analytics. The implementation replaced a legacy state where fragmented regional systems and slow refresh cycles meant frontline teams waited hours for insights and could not prevent fuel disruptions fast enough. Global fuel operations data and inventory levels move through Lakeflow Jobs and land in Delta Lake, giving teams one trusted data foundation. Unity Catalog governs access, lineage, and compliance across sensitive datasets. Databricks SQL, MLflow, and Model Serving operationalize models, agents, and analytics that deliver predictive stockout alerts and safety audit reports.
Global fuel operations data and inventory levels land on a shared platform, pipeline services clean and standardize them, governance controls enforce trusted access, and AI, model-serving, and analytics services deliver predictive stockout alerts and safety audit reports.
Our teams wanted speed and control, but the setup made us choose one or the other,
Puma Energy is a downstream energy company that supplies fuel, lubricants and aviation products across Latin America, Africa and Asia-Pacific. With a purpose to “energize communities,” the business sought to predict fuel
The infrastructure wasn’t built to scale how the business needed — everything required too much DevOps effort.
The vision is for Databricks to become the central highway that connects tools within the business, not just for data ingestion, but also for orchestrating decisions and outcomes across the enterprise.
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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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