When we first sat down with the client, they shared key challenges slowing down their project execution and reporting:
Disconnected Planning & Execution
MBSE models (urban planning, structural analysis, and sustainability) weren’t integrated with real-world construction data.
Scattered Project Data
Critical information from CAD/BIM, ERP, GIS, IoT sensors, and financial systems existed in silos, delaying decisions.
Budget Overruns & Delays
Project inefficiencies led to 15–20% cost overruns and delays of up to 6 months.
Manual Compliance Processes
Environmental audits and documentation were done manually, delaying approvals by 20–30%.
To bridge the gap between MBSE models and real-world operations, our developers built a robust Azure-based MBSE solution that connected all engineering, construction, and compliance data into one intelligent ecosystem:
Data Integration & Storage
Azure Data Factory (ADF): Ingested data from various sources, including MBSE tools, CAD files, PLM, ERP systems, MES, SCADA, and IoT sensors to ensure complete data coverage across projects.
ADLS Gen2: Provided secure, scalable storage for both structured and unstructured engineering and project data.
Azure IoT Hub & Event Hubs: Enabled real-time ingestion of machine data from construction sites for up-to-the-minute tracking and alerts.
Data Processing & Analytics
Azure Synapse Analytics: Helped transform raw data, validate MBSE models, and generate engineering insights aligned with live site data.
Azure Databricks: Powered AI/ML-driven comparisons between MBSE simulations and actual construction progress, helping identify deviations early.
BI & Visualization (Power BI)
Design Dashboards: Tracked design iterations, failure rates, and model validation metrics for faster feedback loops.
Manufacturing Dashboards: Monitored real-time KPIs such as defect rates, cycle times, and equipment performance.
Compliance Dashboards: Automatically generated audit trails and tracked adherence to regulatory requirements, cutting down manual reporting time.
AI-Driven MBSE Optimization
Azure Machine Learning: Provided predictive maintenance insights, system-level optimizations, and early risk detection using historical and real-time data.
Azure Cognitive Services: Automated generation of compliance documents and reporting summaries, making audit prep effortless for the client teams.
Data-driven MBSE workflows improved efficiency, reducing project completion time by 40%. Real-time monitoring helped identify and address delays early.
AI-powered forecasting cut budget overruns and resource wastage by 25%, enabling more accurate cost projections and better resource management.
Automated reporting ensured full compliance with safety and environmental standards, reducing delays caused by manual audits.
Power BI dashboards provided executives with real-time insights, helping them make data-driven decisions and keeping projects on track.
Real Estate & Construction
North America
End to End Project Lifecycle Management
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