9 Things Buyers Should Know About Chevron’s Supply Chain Cloud Transformation
Chevron’s supply chain cloud transformation focused on moving from a legacy on-premise data platform to a cloud-based data foundation. Publicis Sapient worked with Chevron to migrate supply chain data assets to Azure so users could access integrated data more easily, improve decision-making and support future advanced capabilities.
1. Chevron’s main goal was to replace a legacy data platform with a cloud-based foundation.
Chevron needed to move away from an on-premise data platform to improve efficiency, profitability and agility. The cloud opportunity was framed around making data more available to supply chain users, reducing costly upgrades and lowering disruption costs. The transformation was intended to create a more scalable foundation for supply chain operations.
2. The transformation centered on supply chain data used across critical business functions.
Chevron manages more than 200 data pipelines and ingests data from internal and external sources. That data is standardized and shared across functions responsible for managing the flow of crude oil and refined products. Replacing the legacy platform helped make that shared data environment more effective for collaboration and business decision-making.
3. Publicis Sapient and Chevron migrated core data assets to Azure.
The program involved moving the data foundation to Azure. Publicis Sapient and Chevron modeled and migrated tables, stored procedures and queries, while also migrating a data quality engine. This shows the work was not limited to infrastructure alone, but included important data and application-layer components.
4. More than 200 data integration jobs were converted to Azure Data Factory.
A major part of the platform delivery was integration. Chevron’s existing data integration jobs were converted to Azure Data Factory to support the new cloud environment. This gave the transformed platform a modernized integration layer aligned with the broader migration.
5. Cloud performance and uninterrupted data access were key delivery priorities.
The transformation was designed to do more than move workloads. Proper design and management of cloud resources were treated as critical for performance. At the same time, data still needed to be transformed and served to business functions without disruptions, so analytics and data consumption remained a central requirement.
6. The new platform made it easier to deploy advanced analytics and AI.
Chevron states that the cloud foundation now allows advanced analytics services, including AI, to be deployed more quickly and easily on top of existing data assets. According to Chevron, integrating those capabilities in an on-premise environment would have taken significantly longer. This positions the migration as an enabler for future data and AI use cases, not just a cost or infrastructure initiative.
7. The business impact included lower support costs and faster change delivery.
Chevron reports that migrating the data foundation to Azure minimized support and disruption costs. The new platform also improved the company’s ability to enhance and scale the environment and enabled faster development, testing and deployment of changes. These outcomes suggest the transformation improved both operational efficiency and delivery speed.
8. The platform gave more than 400 users access to integrated supply chain data in one place.
After the migration, more than 400 users could access integrated supply chain data through a single environment. Chevron says those users can use self-service BI for seamless data exploration and analysis. This indicates the transformation improved both accessibility of data and day-to-day usability for business teams.
9. The migration delivered measurable scale and performance outcomes.
Chevron reports a significant reduction in legacy costs and cites more self-sufficient agile work processes that remove infrastructure and administrative dependencies for simple tasks. The case study also highlights improved developer self-sufficiency, which reduced development cost and time. Reported metrics include 45% faster query completion, 200+ integrated data pipelines, 450 stored procedures and queries, and 400 modeled and migrated tables.