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# Model Based Systems Engineering in an Agile Product Development Process

Volkswagen's Herman Goza and LieberLieber's Daniel Siegel reveal why document-centric engineering fails autonomous vehicle innovation. Learn how treating physical system models like code using GitFlow prevents costly architectural drift.

- **Speakers:** Daniel Siegl, Hermann Gollwitzer
- **Event:** WeAreDevelopers LIVE
- **Published:** November 12, 2020
- **Duration:** 32:04
- **URL:** https://www.wearedevelopers.com/videos/68-model-based-systems-engineering-in-an-agile-product-development-process

## Summary

As cyber-physical systems in the automotive and defense industries grow increasingly complex, traditional document-centric engineering processes are struggling to keep up with strict functional safety regulations like ISO 26262. Volkswagen's Herman Goza and LieberLieber's Daniel Siegel explain how a lack of early architectural focus causes system designs to deteriorate, leading to costly refactoring and delayed innovations. To survive the shift toward autonomous driving and vehicle electrification, large enterprises must transition from piles of static documentation to intelligent, machine-readable data models.

The core of this transition relies on Model-Based Systems Engineering (MBSE), which replaces disconnected files with an underlying graph of traceable requirements, static architecture, and behavioral data. Using solutions like Enterprise Architect, teams establish end-to-end traceability that satisfies compliance audits natively. However, MBSE must break away from traditional configurations to operate efficiently at an enterprise scale. As Siegel notes, "if only your team uses the model, you are on the wrong track"—the model must be highly accessible and serve as a unified backbone for thousands of distributed developers and stakeholders.

To achieve agile MBSE, organizations are adapting software engineering workflows—specifically GitFlow and optimistic version control—for their physical systems models. Moving away from restrictive pessimistic version control, teams leverage feature branching and specialized 3-way graph diff-and-merge capabilities to manage variability and parallel development. Treating models like code with natively integrated pull requests creates a perfect trigger for architectural peer reviews, prevents drift, and paves the way for introducing continuous integration (CI) pipelines and component package managers into the hardware engineering ecosystem.

**Keywords:** model-based systems engineering, agile product development, cyber-physical systems, automotive software architecture, iso 26262 functional safety, document-centric engineering, systems engineering traceability, enterprise architect tooling, optimistic version control, gitflow for systems engineering, 3-way model merging, devops for models, mbse traceability, architecture deterioration

## Chapters

1. **Introduction to speakers and model-based systems engineering goals** (00:17) — An overview of the presentation's focus on adopting model-centric techniques for modern safety-critical systems development.
1. **Adopting systems engineering across a large enterprise** (04:25) — How large automotive organizations unify complex functional pipelines requiring integrated architecture, methods, and tooling.
1. **Integrating development processes and realization methods** (06:03) — A layered approach to aligning business roadmaps, core methodological flows, and practical tooling usage.
1. **Architecting a speed indicator with functional requirements** (07:00) — Translating functional and non-functional requirements into a logical technical design with complete traceability.
1. **Preventing software architecture deterioration over product lifespans** (08:37) — Maintaining dedicated architectural oversight prevents escalating maintenance costs and continuous integration delays.
1. **Replacing document-centric workflows with model-based systems engineering** (10:10) — Meeting mandatory regulatory standards for automated driving by adopting traceable, centralized modeling over disconnected documents.
1. **Utilizing trace links and graphs for safety-critical modeling** (13:17) — Modeling behavior with graphs and trace links transforms static architectures into fully analyzed machine-readable data structures.
1. **Structuring architecture with diagrams and containment trees** (16:44) — Representing cyber-physical architectures via block diagrams and activity flows organized systematically in relational trees.
1. **Applying git workflows for agile model iteration** (19:55) — Achieving concurrent engineering and regulatory compliance by treating models like standard software code repositories.
1. **Managing product variability using intelligent branching strategies** (21:49) — Decoupling complex product variations into independent branches rather than maintaining monolithic baseline configurations.
1. **Enabling agile team collaboration with specialized visual merging** (25:02) — Resolving structural discrepancies using visual tools that compare diagrams and element trees during parallel development.
1. **Bringing continuous integration and package management to models** (28:27) — Transitioning traditional modeling environments into modern engineering ecosystems utilizing automated pull requests and reusable components.

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