Productivity Increases Enabled by Quantifying System Levels of Support (QSLS) Methodology for Model-Based Systems Engineering (MBSE) Projects

Abstract

This paper examines the productivity increases that the Quantifying System Levels of Support (QSLS) methodology can provide to teams using Model-Based Systems Engineering (MBSE) for Systems Engineering projects. By enabling quantitative analysis of system architectures and traceability from architecture to design to implementation, QSLS can significantly reduce time and effort, improve system quality, and increase the probability of winning competitive bids compared to traditional MBSE approaches.

Introduction

Model-Based Systems Engineering has emerged as a powerful way to manage the complexity of modern systems engineering projects. By creating an integrated system model as the "single source of truth", MBSE promotes consistency, enables impact analysis, and facilitates communication among stakeholders [1].

However, translating the system model into an implementable architecture that optimally satisfies competing quality attributes and business drivers remains a challenge. Existing MBSE methodologies lack a rigorous, quantitative approach for assessing architectures and lack strong traceability from the architecture to downstream design and implementation [2].

The Quantifying System Levels of Support (QSLS) methodology, developed by QSLS Engineering, addresses these gaps. QSLS provides a comprehensive, quantitative approach for assessing system architectures in terms of key system characteristics, quality attributes, and business drivers, with traceability and assessment extended to the design and implementation levels [3].

This paper analyzes how applying QSLS within an MBSE environment can drive significant productivity increases in terms of reduced time and effort, improved system quality, and increased win probability. A systems engineering project for an example radar system is used to illustrate the benefits.

QSLS Methodology

Productivity Analysis

Time and Effort Reduction

A primary benefit of QSLS is the automation of the time-consuming and error-prone task of manually assessing architectures against quality attributes and business needs. By capturing architectural mechanisms and system attributes in a reusable knowledge base and applying matrix-based linguistic analysis, QSLS can rapidly generate quantitative assessments of how well an architecture supports key attributes and drivers [3].

In the example radar project defined within the paper, it's estimated that QSLS could reduce the time spent on architecture assessment from 3-4 weeks of manual analysis to 2-3 days, a productivity gain of over 80%. This is achievable by encoding/identifying the 50-60 key architectural mechanisms and attributes in the QSLS knowledge base (of the current 400+ mechanisms in the database) and using the tool to automatically score candidate architectures.

Furthermore, the traceability and assessment that QSLS provides from architecture to design to implementation can significantly reduce manual alignment and impact analysis efforts downstream. In the radar example, extending the architecture knowledge base with 40-50 design mechanisms (of the over 1,700 currently in the database) and attributes could reduce design assessment and alignment effort by an estimated 50-60%.

Quality Improvement

Another key benefit is the ability of QSLS to drive higher quality system architectures and designs compared to manual approaches. By providing a comprehensive framework considering all key system attributes and quality drivers, along with using a data-driven approach to optimize the architecture, QSLS can help avoid costly design flaws and rework.

For the example radar, QSLS could help identify architecture options that provide a 15-20% improvement in key attributes such as maintainability and reliability compared to a manual approach, by methodically considering all attributes and making optimal trade-offs. Issues that may otherwise be found late in design reviews can be identified and resolved much earlier.

The ability to quantitatively demonstrate to the customer how well the proposed architecture supports their key business and quality drivers is also a powerful advantage. In the radar project, presenting QSLS analysis results along with showing 95%+ support for critical needs like radar detection range and QSLS analysis of operational availability could significantly boost customer confidence.

Increased Win Probability

Deploying QSLS can provide a substantial competitive edge in winning systems engineering bids. Unique differentiators include:

1.       Quantitative architectural optimization vs. competitors' manual approaches

2.       Comprehensive assessment of all customer key business and quality needs 

3.       Rigorous traceability from architecture to design to implementation

4.       Metrics-based project status and risk assessment

For competitive radar procurements in the $50-$100M range, even a 10-15% increase in win probability enabled by these QSLS advantages could be worth $5M-$15M in additional expected revenue. Early investment in QSLS knowledge bases and training can pay large dividends.

More efficient proposal development, with less time spent on manual architecture definition and assessment, can also enable pursuing more opportunities. A 20-30% reduction in architecture and design proposal effort could allow an additional 1-2 bids per year, further increasing win potential.

Conclusion

Deploying the QSLS methodology within an MBSE environment can enable significant productivity gains for systems engineering projects, including:

1.       80-90% reduction in architecture assessment effort

2.       50-60% reduction in design assessment and alignment effort

3.       15-20% improvement in key system quality attributes

4.       10-15% increase in bid win probability

By investing in developing QSLS knowledge bases and proficiency, systems engineering organizations can reap substantial returns in reduced time and cost, higher quality solutions, and increased business capture. The advantages are particularly compelling for competitive bids with complex quality and business needs.

References

[1] J. Holt and S. Perry, "SysML for Systems Engineering", 3rd ed., IET, 2018.

[2] A. Pyster et al., "Guide to the Systems Engineering Body of Knowledge (SEBoK)", v2.1, 2019.

[3] R. Townsen, "Quantifying System Levels of Support (QSLS): Applying Linguistics with Matrix/Vector Mathematics to Derive Quantified System Architecture, Design and Pre-Implementation Measurements", RST Consulting, 2024.