Complexity in Practice: Lessons for New Systems Engineering
Discover Complexity in Practice - New Systems Engineering
Introduction: Why Complexity in Practice Matters
Complexity in Practice shows that engineers cannot manage what they fail to measure. Programs often feel “too complex,” yet without metrics, that sense stays vague and subjective. Measurement translates invisible growth into visible form. Complexity creates the structure and numbers that allow leaders to act with foresight instead of reacting after problems appear.
Moreover, complexity is never one-dimensional. It exists in the number of parts, the interfaces that link them, the requirements that govern them, and the organizational structures that coordinate them. A design with 1,000 parts may seem manageable, but when nearly half a million possible interfaces are counted, the challenge shifts to another scale. Similarly, a single unclear requirement can ripple into hundreds of new tests or rework cycles, turning what looks like a small detail into a program-level burden.
In addition, measurement matters because complexity does not remain static. It grows, spreads, and multiplies in ways that remain hidden until late in development. By establishing ways to count, track, and analyze complexity early, engineers gain the ability to identify risks before they become unmanageable.
Therefore, management follows naturally. Once complexity is measured, it can be prioritized, reduced, optimized, or restructured. Without measurement, management is little more than intuition; with measurement, it becomes a disciplined practice.
For automotive engineering, this distinction is decisive. Programs that measure complexity—through parts, interfaces, requirements, and change impacts—develop the ability to control it. Those that ignore measurement inevitably find themselves overwhelmed.
Case Study 1: Wiring Harness Overload
Wiring harnesses are among the most visible examples of interface-driven complexity in vehicles. Each new connector, sensor, and module multiplies potential signal paths, validation steps, and failure points. Yet the challenge is not only about the number of wires—it is about two physical outcomes that determine whether the harness can even function in a vehicle: weight and diameter.
Both of these parameters matter. Excessive weight makes the harness difficult to install and strains vehicle packaging. Excessive diameter creates another limit: the harness must physically route through openings, bends, and spaces in the vehicle body. If the diameter exceeds what the design allows, the harness simply cannot fit, no matter how carefully it is constructed.
The real complexity problem is that these outputs—weight and diameter—are not known precisely until late in the program, when the harness design has matured enough for process control evaluation. By then, architectural choices, connector strategies, and routing paths have already been locked in. If weight or diameter prove excessive at this stage, engineering has little room to react. Redesign means major rework across multiple domains, from body structure to module placement.
This case illustrates a fundamental truth of complexity: numbers accumulate invisibly until they surface as hard limits. Harness teams may focus on meeting electrical requirements and connector counts, yet the emergent properties—weight and diameter—carry the final verdict. Only by anticipating these physical constraints earlier, through predictive modeling or simulation, can OEMs manage the risk instead of discovering it too late in validation or launch.
Wiring harnesses highlight Complexity in Practice: a commodity where emergent properties such as weight and diameter are invisible until late in development.
Case Study 2: Complexity in Practice in ADAS Integration
At the same time, Advanced Driver Assistance Systems (ADAS) demonstrate how complexity hides in interfaces rather than in the parts themselves. Adding a driver-monitoring camera or radar sensor may appear simple: one part, one function. Yet when this part enters the system, dozens of new interfaces appear—CAN bus data, power supply, display outputs, cybersecurity checks, diagnostic routines. Each interface spawns requirements, and each requirement multiplies test cases.
The systemic challenge is that the true “weight” of these interfaces—like the weight and diameter of wiring harnesses—is not visible until late in development. The part may be designed and tested in isolation, but when integration begins, unexpected behaviors surface. Timing mismatches, bandwidth conflicts, or security gaps force costly rework across subsystems.
A systemic approach changes the outcome. Instead of discovering these mismatches at integration, modeling success early allows engineers to expose the hidden complexity of interfaces before hardware exists. Predictive models and simulations reveal how data flows, where bottlenecks occur, and which requirements collide. In this way, Systems Engineering does not eliminate complexity, but it prevents the “boo boos” that emerge when invisible interactions surface too late.
ADAS shows why interface complexity is as dangerous as physical limits. The system may look fine on paper until it runs in a real environment, just as a harness may look fine until its diameter exceeds a tunnel in the body-in-white. By embedding systemic modeling and requirements traceability from the start, engineers gain foresight, turning late-stage surprises into early design decisions.
This is Complexity in Practice, where hidden interfaces transform a simple part into a systemic risk.
Case Study 3: EV Battery Packs and Statistical Reality
Electric vehicle (EV) battery packs reveal another form of systemic complexity: the scale of probability. Each pack may contain thousands of individual cells. At first glance, a defect rate of six parts per million (6 ppm) per cell seems nearly perfect. Statistically, however, when thousands of cells are assembled into one pack, the chance that at least one will be defective becomes significant. At the fleet level—tens or hundreds of thousands of vehicles—these probabilities grow into tangible risks of recalls, warranty claims, or safety incidents.
The critical issue is that this risk is not visible until late in the process, when large-scale production exposes defect patterns. Cell quality may look acceptable in isolation, but systemic probability reveals the real vulnerability. By the time failures appear in the field, redesign or supplier remediation is costly and disruptive.
A systemic approach prevents this late discovery. Using statistical models and fleet-scale simulations, engineers can calculate defect propagation before production ramps. These models expose the mismatch between ppm-level targets at the component level and ppb-level expectations at the vehicle level. With foresight, OEMs can demand tighter supplier quality, design redundancy into packs, or implement advanced monitoring systems that mitigate risks proactively.
EV batteries show that systemic limits often live in scale rather than in design detail. Just as wiring harness diameter only becomes visible when installed, and ADAS interface mismatches surface only in integration, cell defect probabilities only reveal their danger at production volume. Systems Engineering, with its emphasis on modeling and anticipating outcomes, turns these hidden risks into manageable parameters before they cascade into systemic failures.
Case Study 4: Complexity in Practice in Over-the-Air Updates
From the outside, an OTA patch looks simple: push new software, fix a bug, move on. Inside the system, however, a small change touches a long chain of dependencies. A minor infotainment update, for example, must align with Bluetooth stacks, GPS timebases, voice engines, HMI behavior, power modes, cybersecurity policies, diagnostic routines, and backend services that coordinate campaign eligibility and rollback.
The hidden complexity appears late. Teams validate the feature in isolation, yet the integration burden surfaces when the update meets real vehicles and live infrastructure. Timing mismatches, stale certificates, or resource conflicts trigger failures that span the car, the cloud, and the dealer network. Consequently, the “small patch” becomes a program event.
EV batteries show Complexity in Practice at fleet scale, where statistics magnify small probabilities into program-level risks. A systemic approach changes the outcome. First, dependency mapping (what modules, services, and data paths the patch touches). Next, simulation-in-the-loop with realistic timing and fault injection to expose brittle interactions. Then, progressive rollout (canary rings, staged populations) plus automatic rollback. Finally, telemetry with a prove-it loop: measure post-update behavior against expected signatures and halt the campaign if signals drift.
OTA demonstrates a core lesson: software changes travel farther than the diff suggests. Only by modeling success beforehand—and instrumenting the field for feedback—can teams prevent small updates from propagating into large failures. OTA updates represent Complexity in Practice in its purest form: a small patch rippling into a system-wide event.
Cross-Cutting Critiques: Why Complexity Slips Through
Across harnesses, ADAS, batteries, and OTA, the same failure patterns recur.
- Requirements quality. Vague or over-specified requirements invite divergence or lock in needless constraints. Use measurable wording early; defer “how” until architecture stabilizes.
- Interface ownership. Overlaps across teams (e.g., two owners of the same bus behavior) create contradictions. Assign single-point ownership and publish interface contracts.
- Model maturity. Immature or uncorrelated models give a false sense of security. Correlate models to physical results; retire models that consistently miss reality.
- Change governance. Late changes propagate fastest. Practice impact analysis before approval; require dependency lists and mitigation plans for each ECR.
- Metrics that matter. Count interfaces (not just parts), requirements per interface, ECR volume/closure time, reuse percentage, and telemetry regressions post-OTA. These expose growth earlier than schedule burndowns do.
- Organizational dynamics. Silos and late communication amplify ripples. Use cross-functional reviews, shared dashboards, and program-level dependency maps to surface conflicts before integration.
In short, complexity slips through when teams optimize locally and measure the wrong things. A **systemic discipline—clear requirements, owned interfaces, correlated models, guarded change, and program-level visibility—**keeps growth bounded.
Philosophical Reflection: Complexity in Practice and Foresight
Complexity will not vanish; it emerges from relationships we cannot fully enumerate. Still, Systems Engineering gives us a way to bound uncertainty. We accept that small variations can produce large effects, yet we choose to model those effects early rather than discover them late.
Practically, this shift means moving from “test to find” toward “model to prevent.” Harness weight and diameter must be treated as early system constraints, not late outcomes. ADAS interfaces should be understood as living behaviors rather than static tables. Cell-level quality must be scaled realistically to vehicle-level risk before production. Finally, OTA must be regarded as a socio-technical change—vehicle + cloud + service—never a single-module patch.
Ethically, this stance matters. Customers live with the consequences of our surprises. Therefore, we carry a responsibility to prefer predictive modeling, staged rollout, and transparent telemetry over heroic fixes at launch. Philosophy, then, is not decoration; it is the posture that turns engineering from reaction to foresight.
Ultimately, systemic foresight is the discipline that converts complexity from a source of late-stage crises into a managed property of modern programs.
Conclusion: Systemic Foresight vs. Late Discovery
The four case studies—wiring harnesses, ADAS integration, EV battery packs, and organizational dynamics—reveal a common lesson: the most critical limits of complexity often remain invisible until late in development. Harness weight and diameter are not known until process control stabilizes. ADAS interface mismatches emerge only when integration begins. Battery cell defect probabilities look trivial in isolation but compound into system-level risks at fleet scale. Organizational changes ripple across teams, yet their full impact surfaces only at launch.
These examples highlight why complexity cannot be managed reactively. By the time outcomes are visible, the cost of correction is high and options for simplification are limited. Systems Engineering provides the alternative: a systemic approach that models success early, turning hidden risks into measurable parameters. Predictive models, simulations, and structured requirements make weight, interfaces, probabilities, and dependencies visible before they evolve into crises.
Philosophically, this reflects the essence of managing complexity. Engineering is not about eliminating unpredictability—it is about preventing late-stage surprises by shifting discovery forward. Instead of discovering “boo boos” at the end, systemic foresight allows organizations to design with awareness, manage with intent, and deliver with confidence.
For automotive programs, this is not theory but survival. Only by treating weight, interfaces, probabilities, and organizational dynamics as measurable, modelable systems can OEMs and suppliers contain complexity. Visibility creates control, and control creates resilience. That is the true discipline of Complexity in Practice.
References
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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
- 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 <—You are here
Simulation and Virtual Models – Managing Complexity in Verification and Validation
Systems Engineering References
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