モデルベースシステムズエンジニアリング（MBSE）
Complex product development spans system, electrical/electronic, software and mechanical engineering, each with its own processes, tools and lifecycle considerations. MBSE creates a structured way to connect those domains while supporting demanding safety, cybersecurity, process-assessment and regulatory requirements
Putting models at the center of system design creates a common structure for requirements, functions, architectures and physical implementation helping teams coordinate decisions before downstream consequences multiply
Use Requirements Functional, Logical, Physical approaches to connect what the system must achieve with how it will ultimately be realized
Reference architectures and system modeling help teams work from a more consistent representation of system relationships and dependencies
Structured models and Digital Twins support simulation and earlier identification of potential issues before they become operational disruption
Connect development decisions across disciplines so products remain anchored to requirements throughout the engineering lifecycle
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We adapt model based approaches, including RFLP and system modeling, to the organization rather than forcing engineering into a generic template
Define end-to-end processes spanning system, electrical/electronic, software and mechanical engineering around consistent systems-engineering principles
Align processes with relevant assessment models and standards covering functional safety, cybersecurity, software updates and other project specific requirements
Bring together people, organization, tools, toolchains and lifecycle choices so MBSE operates as a development model rather than an isolated modeling activity
We don’t leave teams with a new model and a manual. Training and coaching help employees apply the defined MBSE approach in day-to-day development
Combining MBSE with Digital Twins extends model-based thinking from system development into monitoring, simulation and operational learning
Use simulation and predictive insight to identify problems before they drive avoidable disruption
Give engineering disciplines a structured foundation for collaborating around the same system intent
Use data-driven insight from Digital Twins to support better operational and allocation decisions
Strengthen coordination and requirement alignment to support higher productivity and reduced time-to-market
Lead the shift toward putting models at the center of smarter product development →
