Good Moves Don’t Come From Thinking Too Long: The Power of Rapid Cognition in Manufacturing Excellence
We were recently reminded of the term, “Good things don’t come from thinking too long”, and wondered how this would fit in process engineering where the stakes are high for rapid cognition and educated decision-making.
The best chess players don't spend five minutes analyzing every move. The most successful emergency room doctors don't deliberate endlessly over obvious diagnoses. And the top manufacturing operators don't overthink every process adjustment.
There's a counterintuitive truth that connects these disparate fields: good moves don't come from thinking for too long.
This principle, backed by decades of cognitive science research and validated daily on factory floors around the world, challenges our traditional assumption that more analysis always leads to better decisions.
Instead, it reveals why the most effective manufacturing professionals often rely on what Malcolm Gladwell termed "rapid cognition" – the process by which people make quick assessments of the world using a limited amount of evidence.
The science behind split-second excellence
The concept isn't about making reckless decisions or abandoning careful analysis entirely. Rather, it's about understanding when our subconscious mind, trained through experience and pattern recognition, can deliver superior results faster than our conscious analytical processes.
In Malcolm Gladwell’s book “Blink”, he explores how our subconscious, in only a few seconds, can make extremely accurate decisions. This rapid cognition isn't intuition in the emotional sense – it's the product of deliberate effort and hours of practice, smart systems, and environments designed for success.
For process engineers and operators, this translates into a profound insight: the veteran operator who can "feel" when a process is drifting before the sensors register it, or the process engineer who instinctively knows which variable to adjust during an upset, aren't operating on mystical intuition. They're demonstrating the power of what researchers call "thin-slicing" – taking a narrow slice of data, just what you can capture in the blink of an eye, and letting your intuition do the work for you.
It’s this experience-driven, rapid decision-making that creates a gap for less experienced engineers which can only be bridged by AI solutions like Fero Labs.
The Manufacturing Paradox: When analysis becomes paralysis
Modern manufacturing generates unprecedented amounts of data. A single steel mill or chemical plant can produce millions of data points every hour. While this information wealth creates incredible optimization opportunities, it also creates a dangerous trap: analysis paralysis.
Rather than choosing between many different options - which actually makes it harder to decide and creates more "buyer's regret"- it's better to purposely limit your choices.
In manufacturing contexts, this means recognizing when you have enough information to act decisively rather than continuing to gather more data while the process continues to drift or opportunities slip away.
Consider the typical scenario: a process breakout occurs, quality metrics begin to deviate, and the natural response is to dive deeper into the data, run more analyses, convene additional meetings, and seek more input.
Meanwhile, production continues, potentially producing off-spec material, consuming excess energy, or creating safety risks. We often end up feeling overwhelmed and mentally freeze up. Then, we end up not deciding at all.
The most effective manufacturing teams understand that in many situations, a good decision implemented quickly beats a perfect decision that comes too late.
How AI and machine learning accelerate good decision-making
This is where Fero Labs' approach to industrial AI becomes transformative. Rather than replacing human decision-making with black-box algorithms, Fero Labs amplifies the power of rapid cognition by providing process engineers with the precise, actionable insights they need to make confident decisions quickly. This is typically based on historical actions taken and predictive modeling.
You can build a virtual replica of any process, then draw on powerful white-box machine learning insights to optimize performance. This approach doesn't eliminate the need for rapid decision-making – it enhances it by giving operators and engineers of any experience level the right information at the right time to make those critical split-second calls.
From pinpointing root causes 90x faster to automating complex data analysis of hundreds of process readings in minutes, our platform saves you time.
When you can identify the root cause of a process deviation in seconds rather than hours, when you can understand which of hundreds of variables actually matter for your current situation. That's when rapid cognition becomes a superpower.
Real-World Applications: Where speed meets precision
The principle of "good moves don't come from thinking too long" manifests differently across various manufacturing contexts:
Steel Production
In steelmaking, predictive machine learning models are replacing outdated rule-based approaches for slag management. Instead of operators spending precious time during a heat manually calculating complex chemical interactions, AI provides instant recommendations that experienced operators can evaluate and implement within seconds. The result: superior steel quality, reduced energy consumption, and significant cost savings.
Chemical Processing
In chemical plants, where process conditions can change rapidly and safety is paramount, the ability to make quick, accurate decisions is literally life-or-death. AI optimizes resource utilization (reducing catalyst usage by 12% for one chemical plant!) by providing operators with real-time optimization recommendations that they can implement immediately rather than waiting for lengthy analysis cycles.
The Trust Factor: Making AI decisions transparent
One critical challenge with rapid decision-making in manufacturing is trust. How can operators and process engineers feel confident making quick decisions based on AI recommendations? This is where explainable AI becomes crucial.
All insights or incident alerts and emails from the Fero Labs platform are highly explainable. Providing critical detail to a particular incident or condition, including recommended actions that they can consider taking. This transparency allows a process engineer to understand not just what the AI recommends, but why – enabling them to make rapid decisions with confidence.
When an experienced operator can see that an AI recommendation aligns with their process knowledge and understanding, they can act quickly. When they can see the reasoning behind a counterintuitive recommendation, they can make informed decisions about when to trust the system and when to override it. Fero’s machine learning will relearn based on the action that the operator ultimately chooses to take, and the result of that action.
Additionally, when a process engine establishes a model within the Fero platform they can add personal commentary as notes so the operator can obtain full context into when and why to deploy a change.
Building a culture of rapid excellence
Implementing the principle of "good moves don't come from thinking too long" isn't just about technology – it's about culture. Organizations that excel at rapid decision-making share several characteristics:
Psychological Safety: Teams need to know that making a quick, well-reasoned decision that doesn't work out perfectly won't result in punishment. Good decision-making is circular; it needs a feedback loop as we gather information and analyze it and our thinking. This means creating environments where rapid decisions can be quickly adjusted based on results.
Clear Decision Rights: Everyone needs to understand who has the authority to make which decisions quickly. In emergency situations, there's no time for hierarchical approval processes.
Standardized Response Protocols: In chess, an autopilot is a move you make without much thought. For example, your king is in check and there is only one possible move. Similarly, manufacturing teams need "autopilot" responses for common situations that allow for immediate action without extended deliberation.
Continuous Learning: Rapid decision-making improves with experience. Organizations must invest in developing their people's pattern recognition abilities through training, mentoring, and exposure to diverse situations.
The economics of speed
The financial impact of decision speed in manufacturing cannot be overstated. Consider these scenarios:
- A quality breakout that takes 30 minutes to address instead of 5 minutes might result in tons of off-spec product
- A delay in implementing an optimization that could improve yield by 2% costs money every minute it's not active
- Equipment heading toward failure gives warning signs – catching and addressing these quickly prevents costly unplanned downtime
Leading plants are switching to asset optimization solutions like Fero Labs for up to 11% efficiency gains in just 3 months. This kind of rapid improvement is only possible when organizations can make and implement optimization decisions quickly.
Balancing speed with rigor
The principle of "good moves don't come from thinking too long" doesn't mean abandoning careful analysis entirely. Instead, it means understanding when different approaches are appropriate:
Use rapid decision-making for:
- Time-sensitive process adjustments
- Routine operational decisions with well-understood consequences
- Situations where the cost of delay exceeds the risk of imperfection
- Emergency response scenarios
Use extended analysis for:
- Major capital investment decisions
- Safety system modifications
- New process implementations
- Decisions with irreversible consequences
The key is developing the judgment to know which situation you're in and responding appropriately.
The Path Forward: Integrating Fero AI for faster, better decisions
The future of manufacturing excellence lies not in choosing between human intuition and artificial intelligence, but in combining them synergistically. AI systems like Fero Labs provide the rapid processing of vast amounts of data, pattern recognition across multiple variables, and predictive capabilities that enhance human decision-making rather than replace it.
In an industry where margins are often measured in fractions of percentages and efficiency gains of 1-2% can transform profitability, the ability to make good moves quickly isn't just an operational nicety – it's a competitive necessity.
The organizations that will thrive are those that combine the speed of rapid cognition with the power of AI-driven insights, creating environments where good moves happen naturally and quickly. They understand that in manufacturing, as in many other high-stakes domains, the perfect decision that comes too late is often worse than the good decision that comes just in time.
The principle is simple but profound: good moves don't come from thinking too long. The application – supported by the right technology, culture, and systems – can transform manufacturing performance.
Ready to accelerate your manufacturing decision-making? Discover how Fero Labs' process optimization platform can help your team make better moves, faster.
Start a Fero Labs pilot to unlock untapped margin within just 4-weeks.
