SOLUTION – MEASURE
Measure Systems Engineering Performance and Reuse
Turn engineering data into actionable insights—automatically. This solution enables engineering teams to define, calculate, and monitor performance metrics such as KPIs, MOPs, TPMs, and reuse indicators directly from engineering artifacts. By embedding measurement into the systems engineering lifecycle, organizations gain real-time visibility, improve decision-making, and continuously optimize engineering performance.
USE CASES
Use Cases for Engineering Measurement and Analytics
Integrate Engineering Data into a Unified Measurement Framework
Engineering metrics rely on data from multiple tools—requirements, MBSE models, simulations, and more. This solution connects all sources into a unified environment where metrics can be defined and calculated consistently.
Outcome:
- Centralized measurement across tools
- Consistent KPI definitions
- Improved data reliability
Build Real-Time Engineering Scoreboards
Create dynamic dashboards that reflect the current state of engineering activities.
Capabilities:
- Aggregation of metrics across quality, traceability, risk, and reuse
- Real-time visualization of system performance
- Monitoring of asset maturity and engineering progress
Outcome: Immediate visibility into engineering performance and project status.
Automate KPI, MOP, and TPM Calculation
Eliminate manual reporting and ensure consistency in metric computation.
Capabilities:
- Automatic extraction of data from engineering artifacts
- Continuous calculation of metrics
- Alignment with system models and requirements
Outcome: Accurate, up-to-date performance indicators without manual effort.
Track Reuse Effectiveness Across Programs
Measure how engineering assets are reused and their impact.
Capabilities:
- Identification of reused artifacts
- Measurement of reuse rates and impact
- Comparison across projects and domains
Outcome: Quantifiable improvement of reuse strategies.
Enable Data-Driven Decision Making
Support engineering and management decisions with reliable data.
Capabilities:
- Correlation of metrics across domains
- Trend analysis and anomaly detection
- Alerts for deviations and risks
Outcome: Better decisions based on real engineering data.
Why Measuring Systems Engineering and Reuse Matters
Typical issues include
- Inconsistent definition of KPIs, MOPs, and TPMs
- Fragmented data across tools and projects
- Manual and delayed reporting processes
- Limited visibility into reuse effectiveness
Impact
- Decisions based on incomplete or outdated information
- Reduced ability to manage risks proactively
- Difficulty assessing engineering performance and progress
Without continuous measurement, organizations cannot effectively control or improve their systems engineering processes.
Common Challenges in Engineering Measurement
Lack of a Unified Measurement Framework
Different teams define and interpret metrics differently, limiting comparability.
Manual and Error-Prone Processes
Metrics are often calculated manually, leading to delays and inconsistencies.
Weak Traceability Between Metrics and Artifacts
Metrics are disconnected from actual engineering data, reducing reliability.
Limited Visualization and Usability
Data is not transformed into actionable dashboards for decision-making.
AI-Enabled Measurement Framework for Systems Engineering
This solution introduces an integrated, semantic measurement framework that connects engineering artifacts directly to performance indicators—without replacing existing tools.
At its core, the platform
- Links requirements, models, and verification data to metrics
- Defines KPIs, MOPs, TPMs, and reuse indicators
- Automates metric calculation in real time
- Aggregates results into dynamic dashboards
Key capabilities
- Real-time measurement of engineering performance
- Direct traceability between metrics and system artifacts
- Automated data collection and processing
- Cross-domain analytics and correlation
Result ⇔ Measurement becomes a continuous, reliable, and actionable engineering capability.
How the Measurement Process Works
Connect engineering data sources
Integrate tools, models, and repositories.
Define metrics and indicators
Configure KPIs, MOPs, TPMs, and reuse measures.
Automate data collection and calculation
Extract and process data continuously.
Visualize results in dashboards
Monitor performance in real time.
Analyze trends and detect deviations
Identify risks and improvement opportunities.
Iterate and refine metrics
Adapt measurement to evolving systems.
Powered by SES ENGINEERING Studio
Integration across tools
Requirements, MBSE, simulation, and document-based tool integration.
Traceability and V&V
Built-in support for traceability, V&V, and technical management.
AI-powered analytics
AI-powered analytics and recommendations for engineering insights.
Automated data extraction
Automated extraction and processing of engineering data.
Advanced search
Advanced search and discovery of engineering artifacts.
Semantic alignment
Semantic alignment through a shared knowledge base.
Interoperability
Interoperability across standards and formats.
Benefits of Engineering Measurement and Analytics
Real-Time Visibility
Improved Decision-Making
Automated Metric Calculation
Strong Alignment with Engineering Reality
Ensure metrics reflect actual system artifacts and their evolution.
Measurable Reuse Impact
Track and optimize reuse strategies across programs and domains.
Early Detection of Risks and Deviations
Data-Driven Engineering Culture
Built for Complex Systems Engineering
Aerospace & Defense
Automotive
Railway
Maritime
Energy & Environment
Telecommunications
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