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Integrating MBSE, Digital Twins, and SoS Approach in Value Engineering

In the context of value engineering, the marriage of Model-Based Systems Engineering (MBSE), Digital Twins, and a System of Systems (SoS) approach promises to revolutionize the way we optimize value in our systems and processes.


Defining the Concepts: MBSE, Digital Twins, SoS, and Value Engineering

  • MBSE is a systems engineering methodology that uses system models as the primary means of information exchange, offering a more efficient way to visualize, simulate, and analyze system behaviors and interactions.

  • Digital Twins are real-time digital replicas of physical entities that allow for system simulation, analysis, and optimization throughout the lifecycle.

  • System of Systems (SoS) is an approach that views a complex system as an assembly of interconnected subsystems (or systems), each with their own capabilities. When combined, these systems achieve an overall functionality that exceeds the sum of the functionalities of the individual systems.

  • Value Engineering is a systematic method aimed at improving the "value" of goods or products by examining their function. The value is defined as the function-to-cost ratio, with Value Engineering aiming to increase this ratio by improving the function and/or reducing the cost.

Applying MBSE, Digital Twins, and SoS in Value Engineering

  1. Enhanced Function Understanding: With MBSE's ability to provide a comprehensive view of the entire system, we can gain a deeper understanding of each subsystem's function in a SoS context. This enhanced understanding allows for more informed decisions when seeking to improve function or reduce costs.

  2. Real-Time Function Analysis: Digital Twins allow for real-time monitoring and predictive analysis of the system's function, which can highlight areas for improvement or potential cost reductions.

  3. Improved Decision Making: By combining MBSE and Digital Twins in a SoS approach, we can simulate different scenarios and their impacts on the overall value. This allows for data-driven decision-making, reducing risks associated with changes and ensuring that modifications truly add value.

  4. Life-Cycle Cost Analysis: The combination of these technologies allows for a thorough life-cycle cost analysis. By understanding costs throughout the system's lifecycle, we can identify opportunities for cost reduction that may not be apparent in a simple up-front cost analysis.

  5. Optimized System Interactions: In a SoS context, improving the value of one system can inadvertently affect others. Using MBSE and Digital Twins, we can model and understand these interactions, ensuring that value optimization efforts in one system enhance, rather than diminish, the overall SoS value.


Incorporating MBSE, Digital Twins, and a SoS approach into Value Engineering provides a holistic, data-driven method for value optimization. Through enhanced function understanding, real-time analysis, improved decision-making, comprehensive cost analysis, and optimized system interactions, we can ensure our Value Engineering efforts deliver the highest value for the entire system of systems. In this complex and interconnected world, this integrated approach is the key to unlocking true system value.

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