QSLS: A Quantitative Framework for Advancing DoD Digital Engineering and Enterprise Architecture

Executive Summary

This white paper examines how the Quantifying System Levels of Support (QSLS) methodology aligns with and enhances both the Department of Defense (DoD) Digital Engineering mandate and emerging enterprise architecture approaches like those implemented in the Army's Program Executive Office for Aviation (PEO AVN). By integrating quantitative analysis and linguistic correlation techniques into systems engineering processes, QSLS transforms qualitative architecture considerations into measurable metrics, providing a data-driven foundation for decision-making across the system lifecycle. The proven application of similar principles within PEO AVN's Enterprise Architecture demonstrates the practical value of this approach, while QSLS offers additional mathematical rigor and standardized implementation that can accelerate adoption across defense programs.

Introduction

Modern defense systems face unprecedented complexity challenges. Increasing integration requirements, cybersecurity concerns, and rapidly evolving technologies create a demanding environment for system architects and engineers. The DoD has responded with its Digital Engineering Strategy, mandating a shift from document-centric processes to model-based, data-driven approaches. Concurrently, programs like the Army's PEO AVN have developed enterprise architecture approaches that elevate quality attributes to contractual requirements.

The Quantifying System Levels of Support (QSLS) methodology represents a significant advancement in this domain, providing a rigorous mathematical framework for quantifying how well system architectures, designs, and implementations support critical quality attributes and business drivers. This paper examines how QSLS aligns with both the DoD Digital Engineering mandate and successful enterprise architecture approaches like PEO AVN's, offering a pathway to accelerate and enhance these initiatives.

The DoD Digital Engineering Mandate: A Transformation in Systems Development

The Department of Defense formally introduced its Digital Engineering Strategy in 2018, establishing a new paradigm for defense systems development. This strategy aims to address increasing system complexity while reducing development time, lowering costs, and improving system quality. It emphasizes five key elements:

1. Formalized planning and management of models and associated data

2. Authoritative source of truth for system information

3. Technological innovation in development approaches

4. Enterprise-wide collaboration across stakeholders

5. Workforce transformation to support digital methodologies

At its core, Digital Engineering leverages digital artifacts, model-based systems engineering, and advanced analytics to improve decision-making throughout the system lifecycle. However, many implementations lack quantitative methods for evaluating architecture quality and measuring design decisions against system requirements.

Enterprise Architecture in Practice: The PEO AVN Approach

The U.S. Army's Program Executive Office for Aviation (PEO AVN) has implemented an Enterprise Architecture approach that addresses many of the same challenges targeted by the DoD Digital Engineering mandate. Their methodology involves:

1. Eliciting business and technical objectives to establish a foundation for architecture development

2. Decomposing to Key Architecture Drivers (KADs) and Key Business Drivers (KBDs) to identify critical system qualities

3. Developing "MOSA Scenarios" that provide context for these drivers

4. Elevating Quality Attributes to contractual requirements, ensuring they receive appropriate attention during development

This approach represents a significant advance in practical architecture development, transforming what was once an afterthought ("Quality Attributes") into a core contractual element. However, while effective, this approach could benefit from additional quantitative rigor and standardized methodologies to enhance consistency and transferability across projects.

The QSLS Methodology: Quantifying System Architecture Quality

The QSLS methodology, developed by Ron Townsen, provides a structured, quantitative framework for evaluating system architectures across multiple levels of development. It uses matrix mathematics and linguistic correlations to derive quantified measurements of system architectures, design, and pre-implementation.

Core Elements of QSLS

1. Hierarchical Analysis Structure: QSLS operates across three levels - Architecture, Design, and Implementation - with bidirectional information flow between levels.

2. Matrix-Based Mathematical Framework: QSLS employs matrix mathematics to quantify relationships between architectural mechanisms, part components, characteristics, quality attributes, and business drivers.

3. Linguistic Correlation Analysis: AI techniques establish correlation values between different concepts and components, translating qualitative relationships into quantitative metrics.

4. Knowledge Base Development: A foundational "Book of Knowledge" contains correlation matrices that capture relationships between system elements.

5. Statistical Output Analysis: QSLS generates minimum, maximum, average, and median values to provide a comprehensive understanding of system support levels.

QSLS's Computational Approach

QSLS provides a unique computational approach that:

1. Transforms qualitative concepts into quantitative metrics: Through linguistic correlation and matrix mathematics, QSLS converts architectural understanding into measurable values.

2. Enables consistency analysis: By computing minimum, maximum, average, and median values, QSLS identifies areas of variability in system support.

3. Facilitates trade-off analysis: The quantitative framework allows architects to evaluate different approaches against multiple quality attributes simultaneously.

4. Provides traceability: QSLS creates a mathematically rigorous chain from architectural mechanisms through quality attributes to business drivers.

5. 
   

Synthesizing Approaches: QSLS, Digital Engineering, and PEO AVN Enterprise Architecture

The feedback on QSLS's relationship to PEO AVN's Enterprise Architecture reveals striking conceptual alignment between these approaches. Both methodologies:

1. Elevate Quality Attributes: Both QSLS and the PEO AVN approach recognize the importance of making quality attributes central to system development rather than secondary considerations.

2. Link Technical and Business Concerns: Both methodologies create explicit connections between technical architecture decisions and business outcomes.

3. Provide Contextual Understanding: Both approaches recognize that requirements must be understood within specific scenarios or viewpoints.

4. Structure Architecture Development: Both provide frameworks that guide the decomposition of high-level goals into implementable system elements.

QSLS's Enhancements to Existing Approaches

While conceptually aligned with both Digital Engineering and PEO AVN's approach, QSLS offers several enhancements:

1. Mathematical Rigor: QSLS provides a structured mathematical framework that quantifies relationships other approaches treat qualitatively.

2. Standardized Analysis: The QSLS Book of Knowledge creates a consistent foundation for analysis across different projects and domains.

3. Multi-Level Assessment: QSLS explicitly addresses not just architecture but extends to design and implementation levels with consistent methodology.

4. Filter Capability: QSLS allows filtering of correlation matrices to focus on significant relationships, removing noise-level correlations for clearer analysis.

5. Statistical Insights: By providing minimum, maximum, average, and median values, QSLS offers deeper insights into system variability and risk.

Implementation Framework: A Unified Approach

Successfully integrating QSLS with Digital Engineering initiatives and enterprise architecture approaches like PEO AVN's requires a structured implementation framework:

Phase 1: Conceptual Alignment

The first phase establishes connections between QSLS concepts and existing terminology and processes:

1. Terminology Mapping: Create explicit mappings between QSLS terms (e.g., Architectural Mechanisms, Quality Attribute Sub-Attributes) and organization-specific terminology (e.g., Key Architecture Drivers, MOSA Scenarios).

2. Process Integration: Identify where QSLS analysis fits within existing development processes, particularly at architectural decision points.

3. Stakeholder Education: Develop materials explaining QSLS concepts using terminology familiar to stakeholders from Digital Engineering and enterprise architecture backgrounds.

Phase 2: Knowledge Base Development

The second phase focuses on developing a tailored QSLS knowledge base:

1. Organization-Specific Mechanisms: Identify and define architectural mechanisms commonly used within the organization.

2. Correlation Development: Develop correlation matrices specific to the organization's domain and technical approaches.

3. Standards Integration: Incorporate relevant military and industry standards into the QSLS knowledge base.

Phase 3: Tool Integration

The final phase addresses technical integration with existing tools:

1. Model Integration: Connect QSLS analysis tools with existing model-based systems engineering environments.

2. Data Exchange: Establish automated data flows between Digital Engineering artifacts and QSLS analysis tools.

3. Visualization Development: Create visualization tools that present QSLS measurements within existing dashboards and reporting systems.

4. 
   

Case Study: Applying QSLS to a Defense System

To illustrate the practical application of QSLS within a defense context, consider a hypothetical radar system development project:

Scenario

A defense contractor is developing a next-generation radar system for aircraft detection. The system must balance multiple competing requirements:

• High detection range and resolution

• Low probability of intercept

• Resistance to jamming

• Reduced size, weight, and power consumption

• Integration with existing systems

• Affordability for production and maintenance

Traditional Approach

In a traditional approach, these requirements would be documented, and architects would design a system based on experience and qualitative assessment. Trade-offs would be made based on subjective judgment, with limited ability to quantify the impact of decisions.

PEO AVN Enterprise Architecture Approach

Using an approach similar to PEO AVN's Enterprise Architecture:

1. Business and technical objectives would be elicited.

2. These would be decomposed into Key Architecture Drivers and Key Business Drivers.

3. MOSA Scenarios would be developed to provide context.

4. Quality Attributes would be elevated to contractual requirements.

This approach would significantly improve upon the traditional method by highlighting quality attributes, but might still lack quantitative measurement of how well the architecture supports these attributes.

QSLS-Enhanced Approach

By integrating QSLS with the PEO AVN-style approach:

1. Architectural Mechanisms would be identified and weighted based on their importance to the radar system.

2. The QSLS computational engine would analyze how these mechanisms support various quality attributes like performance, security, and interoperability.

3. Quantitative scores (0-1) would be generated for each quality attribute.

4. The system's support for business drivers like affordability and mission effectiveness would be calculated.

5. Multiple architectural options could be quantitatively compared.

This approach would provide quantifiable measurements of architecture quality, enabling data-driven decisions about trade-offs and identifying areas requiring additional attention.

Results

The QSLS-enhanced approach might reveal (example):

• Architecture Option A provides 0.85 support for detection range but only 0.62 for jam resistance

• Architecture Option B provides more balanced support (0.76 for detection range, 0.78 for jam resistance)

• The maximum-minimum differential for Option A (0.38) is significantly higher than for Option B (0.21), suggesting Option B offers more consistent quality support

These quantitative insights would enable more informed decision-making and provide clear metrics for tracking architecture evolution through design and implementation phases.

Benefits of the Integrated Approach

Combining QSLS with Digital Engineering and enterprise architecture approaches like PEO AVN's offers several significant benefits:

1. Quantitative Validation: QSLS provides objective measurements that validate architecture decisions against quality attributes and business drivers.

2. Enhanced Traceability: The methodology enables tracing of requirements and design decisions from architecture to implementation with quantifiable impacts at each level.

3. Improved Communication: Quantitative measurements facilitate clearer communication between technical teams and non-technical stakeholders about system quality.

4. Risk Reduction: Early identification of architectural weaknesses reduces downstream implementation risks and costs.

5. Standards Compliance: QSLS incorporates standards compliance as a fundamental element of architecture evaluation, supporting DoD standardization initiatives.

6. Contractual Clarity: Quantitative metrics provide clearer contractual requirements for quality attributes, reducing ambiguity and disputes.

7. 
   

Implementation Considerations

Organizations seeking to implement this integrated approach should consider several factors:

Technical Considerations

1. Tool Integration: Investment in connecting QSLS tools with existing MBSE and Digital Engineering environments.

2. Data Management: Establishment of procedures for maintaining the QSLS knowledge base alongside other engineering artifacts.

3. Computation Resources: Allocation of resources for performing QSLS analysis, particularly for complex systems.

Organizational Considerations

1. Training: Development of training programs that explain QSLS concepts using terminology familiar to the organization.

2. Process Updates: Modification of existing processes to incorporate QSLS analysis at key decision points.

3. Cultural Change Management: Strategies for shifting from qualitative to quantitative architecture assessment.

Contractual Considerations

1. Requirement Specification: Methods for specifying quantitative quality attribute requirements in contracts.

2. Verification Procedures: Approaches for verifying QSLS-based requirements during system development.

3. Incentive Structures: Contract incentives that reward achievement of quantitative quality targets.

4. 
   

Conclusion

The alignment between QSLS, the DoD Digital Engineering mandate, and enterprise architecture approaches like PEO AVN's represents a powerful opportunity to advance defense systems engineering. By integrating these approaches, organizations can achieve:

1. The model-based, data-driven foundation emphasized by Digital Engineering

2. The quality attribute focus demonstrated by PEO AVN's Enterprise Architecture

3. The quantitative rigor and mathematical framework provided by QSLS

This integrated approach addresses both the technical and management aspects of modern defense systems development, providing a pathway to reduced cost, accelerated development, and improved system quality. As the DoD continues to advance its Digital Engineering initiatives and programs like PEO AVN refine their enterprise architecture approaches, QSLS offers a complementary methodology that can enhance these efforts through quantitative analysis and data-driven decision support.

The validation of QSLS concepts through their similarity to proven approaches like PEO AVN's Enterprise Architecture demonstrates that QSLS is not merely theoretical but addresses real-world challenges in ways that align with successful practices. By building on this foundation and adding mathematical rigor and standardized implementation, QSLS can help defense organizations accelerate their digital transformation and deliver more effective systems to warfighters.

References

1. Department of Defense. (2018). Digital Engineering Strategy. Office of the Deputy Assistant Secretary of Defense for Systems Engineering.

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

3. QSLS Engineering. (2024). QSLS and the Department of Defense Digital Engineering Mandate: Advancing Quantitative Systems Engineering. www.QSLSEngineering.com.

4. Holt, J., & Perry, S. (2018). SysML for Systems Engineering (3rd ed.). IET.

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