SOLUTION – IDENTIFY
Identify and Manage Reusable Engineering Assets and Features
Identify, capture, and reuse engineering knowledge across your tool ecosystem-automatically. This solution enables engineering teams to discover and manage reusable assets such as requirements, models, glossaries, and architecture across projects and tools. By structuring this knowledge and making it searchable, organizations reduce duplication, improve consistency, and accelerate system development towards a reuse approach.
USE CASES
Use Cases for Identifying and Managing Reusable Engineering Assets and Features
Integrate Disparate Engineering Tools into a Unified Reuse Hub
Engineering environments often rely on multiple tools-MBSE platforms, RMS, Excel, PLM systems, documents, and more. This solution connects all tools into a unified framework in which assets can be identified, linked, and reused without replacing existing tools.
Outcomes:
- Full traceability across tools
- Improved interoperability
- Centralized access to engineering knowledge
Structure Glossaries, Relationships, and Requirement Patterns
Organize terminology and relationships to create a consistent foundation for reuse.
Capabilities:
- Centralized glossary and thesaurus management
- Standardized requirements and model patterns
- Explicit relationships between engineering concepts
Outcome: Improved consistency and shared understanding across teams and programs.
Automatically Build Ontologies with AI
Leverage AI to extract and structure engineering knowledge from existing artifacts.
Capabilities:
- Automatic terminology extraction
- Relationship detection across models and requirements
- Generation of thesauri (ISO 25964) and ontologies
- Identification of reusable patterns and clusters
Outcome: A consistent semantic layer enabling reliable reuse across domains and tools.
Generate Product Line Structures from Existing Systems
Identify commonality and variability across systems to enable product line engineering.
Capabilities:
- Detection of shared components and variations
- Automatic generation of feature models
- Definition of configuration rules and reusable architecture
Outcome: Accelerated product line development and improved reuse of system architectures.
Why Identifying Commonality and Variability Matters
Typical issues include
- Engineering assets stored across multiple tools and formats
- Inconsistent terminology across teams and domains
- Limited visibility of existing assets
- Repeated creation of already existing artifacts
Impact
- Increased engineering costs
- Delays in development cycles
- Inconsistent system definitions
- Reduced alignment across teams and suppliers
Common Challenges in Engineering Reuse
Fragmented Tool Ecosystems
Engineering data distributed across heterogeneous tools with different structures and semantics, making reuse difficult.
Uncertain Asset Quality
Even when assets are accessible, their correctness, completeness, and consistency are often unclear.
Lack of Intelligent Discovery
Traditional repositories rely on manual tagging and rigid structures, making it difficult to find relevant assets efficiently.
AI-Powered Identification of Reusable Engineering Assets
Our solution introduces an ontology-driven, AI-enabled approach to systematically identify and manage reusable assets-without replacing existing tools.
At its core, the platform
- Extracts knowledge from requirements, models, documents, and PLM systems
- Normalizes terminology and relationships across domains
- Builds a semantic knowledge graph (ontology)
- Identifies reusable assets and engineering patterns automatically
Key capabilities
- Semantic alignment across tools and formats
- AI-driven pattern recognition and clustering
- Qualification of assets based on quality criteria
- Federated, searchable repository of reusable elements
Result ⇔ Reuse becomes a systematic, scalable, and reliable engineering practice.
How the Reuse Identification Process Works
Define data sources
Select the tools, models, and repositories to analyze.
Connect engineering tools
Integrate data sources into the platform.
Extract and normalize knowledge
Automatically structure terminology and relationships.
Identify reusable assets
Validate and organize assets
Continuously improve
Powered by SES ENGINEERING Studio
The solution is enabled by SES ENGINEERING Studio, a federated, tool-agnostic environment for managing engineering knowledge and reuse.
Integration across tools
Requirements, MBSE, PLM, and document-based tool integration.
Traceability and quality
Built-in support for traceability, quality, and V&V.
AI-powered analysis
AI-powered analysis and recommendation capabilities.
Automated identification
Automated identification of engineering information.
Advanced search
Advanced search and discovery of engineering artifacts.
Semantic alignment
Semantic alignment through a shared knowledge base.
Interoperability
Interoperability across standards, formats, and tools.
Benefits of enabling reusable assets identification
Reduce Non-Recurring Engineering (NRE)
Improve Consistency Across Programs
Standardize terminology, structures, and engineering practices.
Accelerate Engineering Workflows
Reduce time spent searching for or recreating assets.
Increase Quality and Reliability
Reuse only validated, consistent, and traceable artifacts.
Capitalize Engineering Knowledge
Transform engineering outputs into long-term strategic assets.
Enable Advanced Reuse Capabilities
Built for Complex Systems Engineering
Aerospace & Defense
Automotive
Railway
Maritime
Energy & Environment
Telecommunications
Ready to systematically identify and reuse engineering assets across your organization?
Join leading aerospace, automotive and defence teams already using SES ENGINEERING Studio.