Understand Complexity Propagation in New Systems Engineering
Understand Complexity Propagation in New Systems Engineering
Introduction: Complexity Propagation
Initially, Complexity Propagation means that once complexity enters a system, it rarely stays contained. Instead, it spreads in unexpected ways, turning small changes into ripples that force redesigns, shift requirements, or create new risks. Therefore, this spreading quality is what makes complexity especially dangerous.
Philosophically, propagation reflects a basic truth: no element exists in isolation. Every part of a system carries connections, and when one shifts, others inevitably move with it. Nature demonstrates this in ecosystems, where a change in one species reshapes the food chain. Human societies show it in markets, where minor fluctuations can cascade into global recessions. In engineering, the same principle applies: the cost of a change is rarely limited to the line of code, the requirement, or the component where it began. Instead, the effects echo across the system, multiplying as they go.
Finally, In automotive programs, Complexity Propagation is never abstract. A sensor redesign may affect wiring harnesses, software logic, and safety validation. A new regulation may trigger updates to cybersecurity, diagnostics, and backend systems. Hence, the challenge is not just to anticipate propagation but to build structures capable of absorbing it.
The Ripple Effect
Generally, the simplest way to understand propagation is as a ripple. Imagine dropping a stone in water: the disturbance does not stay at the point of impact; it spreads outward. Complexity behaves in the same way.
In engineering projects, the ripple often begins with a change request. A new requirement alters one subsystem, which in turn forces changes in its interfaces, which then cascade into other subsystems. What began as a minor adjustment quickly grows into weeks of redesign.
Furthermore, this effect can be compared to dominoes. One piece tips the next, then the next, until the whole row falls. The initial push was small, but the accumulated effect is large. In software systems, this can resemble the butterfly effect: a small update in one area—say, encryption logic—can ripple into unexpected failures in distant parts of the system.
The more tightly coupled the system, the faster and more destructive the ripple. In contrast, loosely coupled architectures absorb change more gracefully. The ripple still spreads, but its energy fades more quickly. Designing systems with this in mind is one way engineers manage the inevitable spread.
To strengthen this approach, engineers increasingly rely on working models and simulation tools developed in parallel with design. These virtual environments make propagation visible one step ahead of hardware and allow predicted ripples to be compared against real-world validation, ensuring correlation and building confidence in the results.
Organizational Complexity Propagation
Overall, Complexity Propagation is not limited to technical systems. It also moves through organizations. Consequently, a small shift in one team’s priorities often forces changes across others. For example, if cybersecurity receives a new compliance requirement, teams in infotainment, diagnostics, and even powertrain must adjust their designs.
Large programs magnify this ripple. A single decision at the executive level—such as delaying a feature or re-prioritizing safety certification—cascades downward through engineering, suppliers, and validation. Each group must realign schedules, budgets, and deliverables.
Suppliers introduce another layer. A change at Tier 2, such as a redesign of a chip or sensor element, propagates upward to Tier 1 integrators and finally to OEMs. By the time it reaches the vehicle level, the impact is far larger than the original modification.
Communication gaps make this worse. If one team fails to notify another about a change, the ripple arrives late, often during validation or launch. At that point, the cost multiplies. This is why formal impact analysis, change boards, and cross-functional reviews exist—not to slow projects, but to contain propagation before it runs unchecked.
Organizational Complexity Propagation follows the same rule as technical propagation: the more dependencies, the stronger the ripple. Siloed organizations are especially vulnerable. A change travels upward for approval, sideways for coordination, and downward into supplier chains. Each layer multiplies the effect.
One way to mitigate this is to use virtual development in parallel with organizational workflows. Working models and simulations allow teams to test scenarios in advance, compare outputs with correlated physical validation, and anticipate where organizational ripples will land. This reduces the shock of late discovery and makes cross-team alignment more predictable.
Automotive Case Examples
a. Sensor Redesigns and Hardware Changes
Sensor updates provide a clear example of how changes propagate. Imagine a supplier introduces a new radar module with higher resolution. On paper, the change seems limited to the sensor itself. In practice, it ripples through the system.
The new radar requires revised wiring harnesses, updated ECU interfaces, and new calibration procedures. Functional safety validation must be repeated, since the detection behavior may differ from the previous module. The ripple extends even further: homologation documents need updating, supplier contracts must be renegotiated, and diagnostic service tools require adjustment.
What began as a small hardware improvement ends up touching engineering design, safety compliance, supply chain agreements, and service infrastructure. The visible change is one sensor; the hidden change is dozens of dependent elements across the program.
b. Regulatory Changes and Compliance Ripples
Regulatory shifts often create the largest waves of propagation. When UNECE introduced mandatory cybersecurity requirements, the rule did not remain at the policy level. It spread across every layer of the automotive ecosystem.
OEMs had to redesign onboard networks, strengthen ECU security, update diagnostic protocols, and create secure backend server connections. Supplier agreements needed revisions to reflect the new obligations. Even training programs for dealership technicians and customer documentation were affected.
What started as a regulation written in legal language cascaded into engineering redesign, IT integration, supplier management, and service operations. Compliance was not just a technical task but an organizational transformation.
c. Software Updates and Recalls
Over-the-air updates illustrate another kind of propagation. From the outside, they look simple: a patch delivered wirelessly to the vehicle. Inside the system, however, the ripple touches many dependencies.
A small infotainment update may require compatibility checks with Bluetooth modules, GPS timing, voice recognition engines, and audio hardware. The validation burden spreads across multiple teams. Beyond engineering, the change also reaches customer support (to handle update failures) and dealerships (to manage rollback procedures).
The patch itself may be a few lines of code. The propagation effect stretches into testing, validation, service, and customer experience. This mismatch between the size of the change and the size of its ripple explains why software recalls can overwhelm organizations.
To contain this effect, engineers rely on simulation environments and working models that mirror the vehicle’s architecture. These virtual platforms allow thousands of interactions to be exercised before release, making it possible to correlate predicted outcomes with real-world validation and reduce the risk of uncontrolled propagation.
d. EV Battery Systems and ADAS Integration
Electric vehicles highlight propagation at scale. A change in battery thermal management—such as a new coolant valve—does not remain contained. It ripples into battery management software, charging profiles, safety certification, and warranty models. It also triggers supply chain adjustments for sensors, fluids, and spare parts, as well as dealership training for technicians.
Advanced driver assistance systems (ADAS) show similar spread. Adding automated lane change may appear to be a single feature, but it depends on multiple sensors, high-definition maps, and new human-machine interface logic. Legal teams must review liability, marketing teams must refine feature descriptions, and validation teams must expand testing scenarios.
Both EVs and ADAS prove the same point: no change exists alone. Every addition, however small, propagates outward into design, validation, regulation, supply chains, and customer experience.
These cases show why propagation is so costly: it is not the change itself, but the network of interactions it triggers.
Conclusion: Complexity Propagation
In conclusion, Complexity Propagation is the force that makes complexity difficult to control. Moreover, a single addition or change rarely stays in place; it spreads through technical systems and organizational structures alike. Furthermore, each ripple multiplies, often reaching areas far removed from the original source.
Philosophically, propagation reminds us that systems are not static objects but living networks. Additionally, change in one node cannot remain isolated; the connections guarantee movement elsewhere. This is both the beauty and the danger of complex systems.
For engineers, the lesson is to anticipate propagation, not deny it. Tools like impact analysis, change management, and modular architectures exist for this purpose. They cannot eliminate the ripple, but they can slow it, contain it, and make its path more predictable.
The next article in this series will focus on Complexity Reduction—how deliberate simplification can reduce unnecessary layers, making propagation easier to manage and systems more resilient.
References
-
INCOSE Systems Engineering Handbook – Overview of systems engineering principles, including V-Model context
www.incose.org/products-and-publications/se-handbook -
ISO/IEC/IEEE 15288: System Life Cycle Processes – International standard defining system development and verification processes
www.iso.org/standard/63711.html - 3. ISO 26262: Road Vehicles – Functional Safety – Automotive functional safety standard where the V-Model is often applied
www.iso.org/standard/43464.html
References to Complexity in Systems Engineering Series:
- What Do We Mean by Complexity?
- The Growth of Complexity
- Counting Complexity – Why Interfaces Grow Faster Than Parts
- Propagation: How Complexity Spreads <—You are here
- Complexity Reduction: The Discipline of Simplification
- Optimization: Improving the Existing System
- Requirements: The First Line of Defense
- Measuring and Managing Complexity
- From ppm to ppb – The Statistical Reality of Vehicle Defects
- Complexity in Practice: Case Studies & Critiques
Simulation and Virtual Models – Managing Complexity in Verification and Validation
Systems Engineering References
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