Wipro optimizes manufacturing with Azure predictive maintenance
Wipro utilizes Azure serverless architecture to address manufacturing sector needs such as predictive maintenance and operational optimization. The solution incorporates various Microsoft technologies including Azure IoT Hub, Azure Stream Analytics, and Azure Machine Learning. The architecture connects devices either directly or via field gateways, processes data in real-time with analytics, and enables storage in appropriate paths via Cosmos DB and Blob Storage. Additionally, Power BI dashboards offer intuitive insights for stakeholders.
- Organization
- Wipro
- Industry
- Manufacturing
- Location
- India
- Published
- May 2025
Reported outcomes
Strategic outcomes
Primary read
Use case focus
Showing 1 of 1
- 1Predictive maintenance
- High downtime costs and operational inefficiencies in manufacturing.
- Need for secure handling of large-scale IoT data.
- Lack of real-time monitoring capabilities.
- Challenges in deploying predictive maintenance workflows efficiently.
- Implemented Azure serverless architecture incorporating IoT Hub.
- Enabled real-time data processing using Azure Stream Analytics.
- Developed predictive maintenance models using Azure ML.
- Data segregation via Cosmos DB and Blob Storage systems.
- Dashboard visualization using Power BI for data insight delivery.
- Minimized downtime costs through predictive maintenance.
- Enhanced data processing and storage security.
- Improved operational visibility with real-time monitoring.
- Optimized manufacturing workflows, enhancing productivity.
Architecture
The solution connects devices using Azure IoT Hub, processes data through Stream Analytics, and trains models using Azure ML. Data is stored in warm (Cosmos DB) and cold (Blob Storage) paths. It integrates Power BI dashboards for intuitive insights.
Sources & evidence2
AI-generated summary. Verify important details with the linked sources before relying on this case.
Explore related AI use cases
Was this useful?