Modernizing Model-based System Engineering for a North American Machinery Manufacturing Firm

The project involved designing and implementing a scalable Azure architecture to create an end-to-end digital thread between MBSE models and real-world operations. In the process, our experts with their internal tech team established: 

Data Integration & Storage

• Our team used Azure Data Factory to pull in data from a variety of engineering and manufacturing systems—including MBSE tools, CAD files, PLM, ERP, MES, SCADA, and IoT sensors—into a unified pipeline.
• We centralized all structured and unstructured data into Azure Data Lake Storage Gen2, creating a scalable and secure repository. Real-time equipment data was captured using Azure IoT Hub and Event Hubs, enabling continuous factory-floor visibility.

Data Processing & Engineering Analytics

• We built powerful data transformation and analysis workflows using Azure Synapse Analytics, allowing engineers to validate MBSE models against real-world production data. 
• For deeper insights, we used Databricks to run AI/ML models that could compare simulated system behaviors with actual performance metrics. 

Dashboards & Visualization (Power BI)

To ensure data-driven action across teams, we created interactive Power BI dashboards:

• Designed dashboards to track iteration cycles, validation outcomes, and failure patterns.
• Manufacturing dashboards were implemented to monitor machine health, defect rates, and production cycles.
• Compliance dashboards were created to automate traceability reports and track regulatory metrics in real time.

AI-Driven MBSE Optimization

• Using Azure Machine Learning, we implemented predictive models that helped in forecasting system failures, optimizing performance, and identifying design bottlenecks early. 
• We also used Azure Cognitive Services to automate compliance documentation, saving time and reducing manual errors.

Technical Implementation

• Data Integration: Connected MBSE tools, CAD, PLM, ERP, MES, and IoT systems using Azure Data Factory and APIs for streamlined ingestion.
• Storage: Used Azure Data Lake Gen2 and Blob Storage to store and manage structured/unstructured data securely and at scale.
• Real-Time Pipeline: Built a model validation framework with Azure Synapse Analytics and Event Hubs for real-time data processing.
• AI-Powered Modeling: Deployed Azure ML, AutoML, and Cognitive Services to optimize systems and predict failures.
• Dashboards: Created interactive Power BI reports for engineering, operations, and compliance teams to support decision-making.

Security & Compliance: Applied Azure Policy, RBAC, and data encryption to maintain compliance and safeguard all data.