The Convergence of COSYSMO Parametric Cost Estimation with Model‐Based Systems Engineering (MBSE)

In today’s complex engineering environments, cost estimation and systems engineering are critical components in determining the success of a project. Effective management of costs and systems design ensures that a project is completed within budget, while meeting technical and operational goals. The Cost Estimating Software System Model (COSYSMO) and Model‐Based Systems Engineering (MBSE) are two powerful methodologies that, when converged, offer a comprehensive approach to managing both system complexity and costs.

This blog explores the convergence of COSYSMO parametric cost estimation with MBSE, the benefits it brings to the table, and how organizations can leverage this integration to improve project performance.

What is COSYSMO?

COSYSMO, or Constructive Systems Engineering Cost Model, is a parametric cost estimation model designed specifically for systems engineering. It was developed to estimate the effort required for systems engineering activities based on various factors such as the complexity of the system, the level of integration, and the amount of documentation required. Parametric cost models like COSYSMO are designed to predict project costs by correlating known project characteristics (referred to as cost drivers) with historical project data.

Originally developed by Ricardo Valerdi in the early 2000s, COSYSMO emerged as a solution to challenges in estimating the effort required for systems engineering tasks in large-scale, complex projects, particularly in defense and aerospace industries. Valerdi based the model on the COCOMO (Constructive Cost Model) framework, which had already been widely accepted for software engineering efforts. COSYSMO, however, introduced a tailored focus on systems engineering processes and activities.

The core idea behind COSYSMO is that the cost of systems engineering is driven by the complexity of the system and the interactions between its components. By analyzing key cost drivers, COSYSMO provides reliable estimates of the resources required, thus supporting better planning and budgeting in early stages of project development.

What is Model-Based Systems Engineering (MBSE)?

Model‐Based Systems Engineering (MBSE) is an advanced approach to systems engineering that focuses on creating, analyzing, and managing system models throughout the lifecycle of a project. MBSE replaces traditional document-based approaches with a more dynamic, visual, and integrated modeling environment that captures the entire system architecture. The models are often created using tools such as SysML (Systems Modeling Language), and they serve as the primary reference point for all stakeholders in the project.

MBSE addresses many of the challenges faced in traditional systems engineering, such as poor communication among teams, inefficiencies in managing complex system interactions, and the difficulty of tracing requirements and changes throughout the project lifecycle. MBSE enables better decision-making, more effective collaboration, and faster identification of issues and risks.

Key advantages of MBSE include:

• Improved traceability: System requirements, design, and implementation decisions are documented in a centralized model, making it easier to track and validate each element.
• Collaboration: The model becomes a single source of truth for all stakeholders, allowing for more effective communication and collaboration across teams.
• Risk reduction: MBSE allows for early detection of inconsistencies, gaps, and potential integration issues, which can be addressed early in the design phase.
• Lifecycle management: MBSE models can be updated as the system evolves, providing a dynamic and flexible approach to managing the system throughout its lifecycle.

The Need for Integrating COSYSMO and MBSE

While COSYSMO provides an excellent method for estimating the cost of systems engineering activities, MBSE offers a holistic approach to designing and managing the system itself. Both approaches have traditionally been used in silos, but the growing complexity of systems and the need for more accurate cost estimation has led to the realization that these two methodologies complement each other.

Integrating COSYSMO with MBSE offers several key advantages:

1. Better Cost Estimation: COSYSMO relies on system characteristics to estimate costs, and MBSE models provide detailed information about the system architecture, interactions, and complexity. By feeding MBSE data into COSYSMO, organisations can generate more accurate cost estimates based on a deeper understanding of the system’s architecture.
2. Real-Time Cost Analysis: One of the challenges in systems engineering is that cost estimation is often done in the early stages of a project when many details about the system are still uncertain. MBSE allows for continuous refinement of the system model as new information becomes available. By integrating COSYSMO with MBSE, organisations can update cost estimates in real-time as the system model evolves, improving budget accuracy.
3. Enhanced Collaboration: MBSE promotes collaboration among stakeholders by providing a common, model-based representation of the system. When COSYSMO is integrated into this model, cost considerations become an integral part of the conversation. This ensures that all stakeholders have a shared understanding of both the system design and the associated costs, fostering more informed decision-making.
4. Improved Risk Management: COSYSMO allows organizations to estimate costs for different scenarios based on various system characteristics. By integrating COSYSMO with MBSE, organizations can run cost analyses on different design alternatives, helping to identify potential cost overruns early in the process. This allows for better risk management by giving teams the ability to adjust the system design to meet budget constraints.
5. Traceability of Cost Drivers: In traditional systems engineering, it can be challenging to trace the relationship between system changes and cost implications. MBSE provides a model that links requirements, system elements, and design decisions. Integrating COSYSMO with MBSE makes it easier to trace how changes in the system architecture affect cost drivers, improving transparency and accountability.

How to Integrate COSYSMO with MBSE

Integrating COSYSMO with MBSE requires both technical and organizational alignment. On the technical side, organizations need to ensure that their MBSE tools (e.g., SysML or other modeling tools) can generate the data required for COSYSMO’s cost estimation process. This may involve automating data extraction from the MBSE model and creating interfaces between the MBSE tool and the COSYSMO cost estimation tool.

On the organizational side, teams need to adopt a mindset of collaboration where cost considerations are an integral part of the systems engineering process. This may require training and education on both COSYSMO and MBSE, as well as fostering a culture of cross-functional collaboration between engineering, cost estimating, and management teams.

The steps for integrating COSYSMO and MBSE include:

1. Develop a unified data structure: The first step in integrating COSYSMO with MBSE is to develop a unified data structure that can capture both system characteristics (from MBSE) and cost drivers (from COSYSMO). This may require customisation of existing MBSE tools to ensure that they can provide the necessary input for COSYSMO.
2. Automate data extraction: Once the unified data structure is in place, organizations should automate the extraction of relevant data from the MBSE model to feed into COSYSMO. This automation ensures that cost estimates are continuously updated as the system model evolves.
3. Conduct scenario analysis: One of the key benefits of integrating COSYSMO with MBSE is the ability to conduct scenario analysis. Organisations should run multiple scenarios based on different design alternatives to determine the cost implications of each option.
4. Validate and refine cost estimates: As the system design evolves, organizations should use the integrated COSYSMO-MBSE framework to validate and refine cost estimates. This ensures that cost considerations are continuously incorporated into the systems engineering process.
5. Implement feedback loops: Finally, organizations should implement feedback loops that allow for continuous improvement of both the system design and cost estimation process. This involves regularly reviewing the cost estimates and comparing them to actual costs, then updating the COSYSMO model and MBSE practices accordingly.

Case Study: Aerospace Industry

The aerospace industry provides a prime example of the benefits of integrating COSYSMO and MBSE. Aerospace projects are typically large, complex, and highly regulated, making accurate cost estimation critical. In this industry, companies have started using MBSE to manage the complexity of system design, while also leveraging COSYSMO to estimate the costs of systems engineering activities.

For example, in a large aerospace defense project, the integration of COSYSMO with MBSE allowed the organization to:

• Reduce cost overruns: By continuously updating the cost estimates as the system model evolved, the organization was able to identify potential cost overruns early in the project and adjust the design accordingly.
• Improve collaboration: The integrated model provided a common reference point for engineers, cost estimators, and project managers, leading to better collaboration and more informed decision-making.
• Enhance risk management: The ability to run cost analyses on different design alternatives helped the organization manage risks more effectively by identifying cost drivers and making trade-offs between system capabilities and budget constraints.

Conclusion

The convergence of COSYSMO parametric cost estimation with Model‐Based Systems Engineering (MBSE) represents a powerful approach to managing both system complexity and costs. By integrating COSYSMO into the MBSE process, organizations can improve cost estimation accuracy, enhance collaboration, and manage risks more effectively. As systems become more complex and budgets become tighter, this convergence will play a critical role in ensuring that projects are completed on time, within budget, and to the desired specifications.

In the future, we can expect to see even more advanced integrations of cost estimation and systems engineering tools, driven by advances in automation, artificial intelligence, and digital twins. These innovations will further enhance the ability of organisations to design, manage, and deliver complex systems in an efficient and cost-effective manner.