SOLUTION – ASSURE

Assure the Quality of Reusable Engineering Assets

Ensure correctness, consistency, completeness, and traceability across all engineering artifacts continuously. This solution enables engineering teams to systematically assess and improve the quality of requirements, models, and other assets across the entire systems engineering lifecycle. By embedding quality assurance into daily workflows, organizations reduce risk, improve reliability, and enable safe and scalable reuse.

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USE CASES

Use Cases for Assuring the Quality of Reusable Engineering Assets

Integrate Engineering Tools into a Unified Quality Framework

Engineering data is distributed across multiple tools-MBSE platforms, documents, PLM systems, and more. This solution connects them into a unified environment where quality, traceability, and validation can be managed consistently without replacing existing tools.

Outcome:

Centralized quality governance
Full traceability across tools
Improved interoperability

Assess Requirements Quality Automatically

Evaluate requirement quality using ontology-driven rules and CCC (Correctness, Consistency, Completeness) metrics.

Capabilities:

Detection of ambiguous or inconsistent requirements
Identification of incompleteness gaps
Terminology alignment with standards, models and glossaries
Automated quality scoring

Outcome: Higher-quality requirements and reduced downstream errors.

Evaluate Architecture and Model Quality

Assess models against ontology rules, metamodels, and consistency criteria.

Capabilities:

Detection of missing or incorrect relationships
Validation against reference architectures and ontologies
CCC quality rules for models and architecture

Outcome: Reliable and consistent system models across the lifecycle.

Automate Verification and Validation (V&V)

Digitalize V&V processes in alignment with ISO 15288.

Capabilities:

Digitalization of the Verification and Validation actions
Automated execution of tests, simulations, and checks
Capture of verification evidence
Traceability between requirements, models, and V&V results

Outcome: Continuous validation instead of late-stage verification.

Manage Configuration, Versions, and Changes

Control changes across engineering artifacts at any level of granularity, independently of the source tools.

Capabilities:

Version control across tools and models
Change tracking and impact analysis
Merge, compare, and synchronization operations

Outcome: Controlled evolution of engineering changes with reduced risk.

Enable Global Configuration Management (GCM)

Manage configurations consistently across distributed teams and tools.

Capabilities:

Cross-tool versioning and baselines
Federated configuration management
Support for multi-organization collaboration

Outcome: Consistent system configurations across complex environments.

Ensure End-to-End Traceability

Establish dynamic traceability across requirements, capabilities, functions, and architectures.

Capabilities:

Configurable traceability models
Real-time impact analysis
Cross-domain trace linking

Outcome: Improved decision-making and system visibility.

Discover Traceability Automatically with AI

Use AI to identify and suggest traceability links across artifacts.

Capabilities:

Automatic trace link generation
AI-assisted validation and governance
Integration with existing traceability workflows

Outcome: Faster trace creation with maintained control and reliability.

Analyze Risk Propagation Across Systems

Understand how risks propagate across interconnected systems.

Capabilities:

Integration with FMEA, FMECA, FTA, ETA methods
Risk propagation analysis through traceability networks
Automated risk insights across architecture

Outcome: Improved risk management and system robustness.

Why Engineering Asset Quality Matters

In complex systems engineering environments, engineering artifacts evolve continuously across tools, teams, and organizations.

Common issues include

Inconsistent quality across requirements and models
Lack of objective evaluation criteria
Late detection of defects
Disconnected verification and validation processes

Impact

Increased system integration risks
Costly rework and delays
Reduced confidence in engineering outputs
High V&V Expenses

Common Challenges in Engineering Quality Assurance

Lack of a Unified Semantic Foundation

Different tools and teams use inconsistent terminology and structures, making alignment difficult.

Limited Scalability of Quality Processes

Manual reviews cannot keep pace with the size and complexity of modern systems.

Insufficient Automation in V&V

Verification and validation are often performed too late and are poorly integrated into engineering workflows.

Static and Incomplete Traceability

Trace links are often outdated or manually maintained, limiting their usefulness for quality assurance.

AI-Powered Framework for Engineering Quality Assurance

Our solution introduces an ontology-driven, AI-enabled framework to operationalize quality across the entire lifecycle using CCC principles—without replacing existing tools.

At its core, the platform

Integrates requirements, models, and verification artifacts
Establishes a shared semantic layer (ontology)
Applies automated quality rules and metrics
Continuously evaluates engineering artifacts

Key capabilities

Objective quality assessment across tools
Detection of inconsistencies and missing elements
Continuous monitoring of quality metrics
Integration of V&V into daily engineering workflows

Result ⇔ Quality becomes a continuous, measurable, and enforceable property of the engineering system.

How the Quality Assurance Process Works

1

Connect engineering data sources

Integrate tools, models, and repositories.

2

Define quality metrics and rules

Apply CCC and ontology-based criteria.

3

Run automated quality assessments

Evaluate artifacts and generate insights, digitalizing V&V.

4

Review and improve artifacts

Correct issues directly in tools or through the platform.

5

Monitor quality over time

Track evolution and ensure continuous compliance.

Powered by SES ENGINEERING Studio

This solution is enabled by SES ENGINEERING Studio, a federated, tool-agnostic platform for engineering quality and reuse.

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Integration across tools

Requirements, MBSE, PLM, and document-based tool integration.

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Traceability and quality

Built-in support for traceability, quality, and V&V.

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AI-powered analysis

AI-powered analysis and recommendation capabilities.

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Automated identification

Automated identification and evaluation of engineering data.

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Advanced search

Advanced search and discovery of engineering artifacts.

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Semantic alignment

Semantic alignment through a shared knowledge base.

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Interoperability

Interoperability across standards, formats, and tools.

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Benefits of Engineering Quality Assurance

Continuous Quality Control

Automatically enforces correctness, consistency, and completeness across all artifacts.

Early Defect Detection

Identify issues at creation time, reducing downstream rework and integration failures.

Reduced Verification Effort

Automate V&V execution and evidence capture, freeing engineers for higher-value work.

Reliable Traceability

Maintain dynamic, up-to-date traceability across the lifecycle.

Increased Engineering Confidence

Apply objective, standardized quality criteria across all engineering outputs.

Reduced Cost and Risk

Minimize late-stage failures, integration issues, and costly design changes.

Built for Complex Systems Engineering

This solution is designed for organizations developing complex, multi-domain systems:

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Aerospace & Defense

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Automotive

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Railway

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Maritime

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Energy & Environment

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Telecommunications

Ready to ensure the quality and reliability of your engineering assets?

Join leading aerospace, automotive and defence teams already using SES ENGINEERING Studio.

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