From Unsolvable to Solvable: System Thinking in Industrial Automation

In the world of industrial automation, engineers often face problems that seem technically impossible to solve. A material keeps cracking under stress, a motor overheats, or a control system becomes unstable. These challenges can feel like dead ends. But experienced engineers know a secret: many “unsolvable” problems become solvable when you change the system boundaries. This shift in perspective is at the heart of system thinking, and it’s a powerful tool for innovation in automation and control engineering.

Upgrading: Solving Problems in a Larger System

One common approach is to move the problem from a small subsystem to a larger system. In a factory, a single machine might have a persistent fault that seems impossible to fix without a costly redesign. But when you look at the entire production line, you might find that adjusting upstream or downstream processes eliminates the issue entirely.

Consider a real-world example from steel manufacturing. A steel plant struggled with cracking in their final product. Analysis showed that phosphorus (P) impurities were too high. Reducing P content would require an expensive upgrade to the refining process—a classic “unsolvable” problem within the small system of chemical composition control. However, a metallurgist proposed a different solution: increase carbon (C) content and decrease manganese (Mn) content. This adjustment changed the material’s tolerance to phosphorus, so the cracking stopped even with high P levels. The goal of the larger system (preventing cracks) was achieved without solving the original problem (lowering P).

In industrial automation, this principle appears frequently. For instance, a variable frequency drive (VFD) might trip due to voltage sags from the power grid. Instead of trying to make the drive immune to all power disturbances (an expensive and often impossible task), engineers can add an uninterruptible power supply (UPS) or a dynamic voltage restorer at the facility level. The larger system—the plant’s power distribution—absorbs the problem, allowing the drive to operate reliably.

Another classic historical example is the invention of the steam locomotive. Early steam engines were extremely heavy—George Stephenson’s first locomotive weighed around 8 tons. No road could support such weight, and reducing the engine’s weight was technologically infeasible at the time. The breakthrough came by expanding the system: instead of making the engine lighter, they built steel rails to support it. The problem moved from “how to lighten the engine” to “how to create a suitable track,” and the railway age began.

Downgrading: Taking Half a Step Forward

Sometimes, the smartest engineering move is to lower your ambitions—temporarily. Instead of aiming for a perfect solution, you aim for a “good enough” one that can be improved later. This is especially relevant in industrial automation, where 100% reliability or zero downtime is often the ultimate goal, but achieving it in one leap is unrealistic.

For example, a factory might want to eliminate all unplanned downtime. A full predictive maintenance system with AI and digital twins could cost millions and take years to implement. A “half-step” approach would be to install basic vibration sensors on critical motors and use simple threshold alarms. This doesn’t eliminate all failures, but it catches the most catastrophic ones, reducing downtime by 50% at a fraction of the cost. Over time, the system can be expanded.

This philosophy aligns with the concept of “minimum viable product” in software, but it applies equally to hardware and control systems. When designing an electrical control panel, you might not achieve the perfect wire routing or the most compact layout on the first try. But by building a prototype, testing it, and iterating, you converge on an optimal design. Each iteration is a half-step that reduces risk and uncovers hidden problems.

Why TRIZ Alone Isn’t Enough for Automation Engineers

TRIZ is a brilliant methodology for resolving technical contradictions, but it has two blind spots that are critical in industrial settings: the trade-off between technical performance and cost (economic feasibility), and the challenge of ensuring safety, stability, and reliability under real-world conditions (technical feasibility in the broad sense).

In a factory, a technically elegant solution that costs ten times more than the budget allows is not a solution at all. Similarly, a control algorithm that works perfectly in simulation but fails when the ambient temperature changes or when a worker accidentally bumps a sensor is not feasible. These constraints force engineers to think beyond the immediate technical system.

Consider the design of a DC drive system like the Siemens 6RA80. The drive itself is a marvel of power electronics, but its success depends on the larger system: the quality of the incoming AC supply, the cooling of the thyristors, the tuning of the current and speed loops, and the mechanical load characteristics. If the drive trips on overcurrent, the root cause might not be in the drive at all—it could be a binding bearing in the motor, a problem that requires looking at the entire electromechanical system.

This is where system thinking becomes essential. The “system” in industrial automation isn’t just the controller and the machine; it’s the entire chain from power distribution to raw materials to operator training. As digitalization connects more devices and systems, the boundaries expand further. A problem in a SCADA system might originate from a network switch configuration, a database server, or even a cybersecurity policy. Solving it requires zooming out.

The Economic System Always Wins

In business, the economic system is larger than the technical system. A technology is only valuable if it can be deployed cost-effectively, on time, and with acceptable risk. This reality often forces engineers to choose “inferior” technologies that are cheaper, faster to implement, or easier to maintain.

For instance, a plant might choose to use simple on-off control for a heating process instead of a sophisticated PID loop with feedforward. The PID would give tighter temperature control, but the on-off control is good enough for the product quality requirements and costs significantly less to install and maintain. The decision makes perfect sense from a system perspective, even if it offends a purist’s technical sensibilities.

This is the essence of Joseph Schumpeter’s distinction between invention and innovation. An invention is a new technical idea; innovation is the successful application of that idea in the economy. For automation engineers, innovation means delivering solutions that work not just in the lab, but in the messy, unpredictable, budget-constrained world of real factories.

Applying System Thinking in Your Automation Projects

How can you use these ideas today? Start by mapping the system you’re working on. Draw a boundary around the problem. Then ask:

• Can I expand the boundary to include upstream or downstream processes that might absorb the problem?
• Can I change the goal to something less ambitious but still valuable?
• Is there a way to create conditions in the larger system that make the problem irrelevant?
• What is the economic constraint, and how can I work within it?

When you face a stubborn issue with a PLC program, a control cabinet design, or a motion control application, remember that the best solution might not be inside the box. It might be in the power supply, the mechanical design, the operator training, or even the production schedule. System thinking turns “unsolvable” into “solvable” by changing the question.

In the era of Industry 4.0 and the Industrial Internet of Things (IIoT), system boundaries are blurring. Machines talk to each other, to the cloud, and to business systems. An engineer who can think across these boundaries—who can see the factory as a system of systems—will find solutions that others miss. That’s the real innovation.