This post is designed to help you maximize human performance in your operations by introducing some simple, but essential, concepts for understanding when and why humans make mistakes.
In the end, I’ll tie all of these concepts together with a graph that will help you isolate the causes of human error in your operations, allowing you to augment operators for increased efficiency in your operation.
The Three Modes of Human Performance
Recent research in psychology and management studies has expanded upon Rasmussen's model for three modes of human performance. Each of these modes describes a set of behaviors and responses underlying how humans perform work.
Understanding these performance modes is the key to understanding human error.
Skills-Based Performance
Skills-based performance (SBP) describes situations in which workers perform a task with little conscious thought. SBP is usually the result of extensive experience with a given operation.
When operating in a skills-based mode, individuals rely on “pre-programmed sequences of behavior” with “little or no allocation of attention resources.”
You can think of SBP as things we do automatically, like riding a bike, typing, or writing by hand.
Knowledge-Based Performance
From its name, knowledge-based performance can easily be misinterpreted.
According to the Department of Energy (DOE) Human Performance Standard, “the situation described as ‘knowledge based mode’ might better be called ‘lack of knowledge’ mode.” This is because we rely on knowledge-based performance when we don’t know what we’re doing, such as when faced with wholly unfamiliar situations.
In these cases, we rely on our existing knowledge to help us. We look for patterns, and apply schema we’ve learned from other tasks to the situation before us.
Rules-Based Performance
Rules-based performance (RBP) applies when changes in context prevent an individual from relying on skills. In this performance mode, a worker applies written or memorized rules to navigate an unfamiliar situation. If aspects of a situation match a learned skill, the worker will fall-back on skill-based behaviors. If not, they will consult external sources.
Another way of thinking of rules-based performance is as sequences of “if-then” decision. If the situation is one way, Then the prescribed behavior follows.
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The Three Modes of Error
For each mode of performance, there’s an associated mode of error. This section will explain how different types of performance lead to different types of error.
Skills-Based Error: Inattention
When operating in a skills-based performance mode, most mistakes are due to inattention. This is because it’s easy to fall into “autopilot,” and miss changes in conditions or tasks.
Examples of skills-based error include pouring orange juice over your cereal, or driving straight home instead of to the grocery store while running errands after work.
In manufacturing, operators are particularly prone to skills-based errors when they perform repetitive tasks, or during transitions to new processes and product lines.
Knowledge-Based Error: Inaccurate Mental Picture
Because knowledge-based performance relies on an individual’s understanding of a task, many errors result from flaws in that understanding. When forced to respond to novel circumstances, an individual will resort to what they know instead of surveying the situation and responding to facts on the ground. We apply known patterns to unknown situations.
This kind of mistake is common in manufacturing. During unexpected downtime, for example, an engineer might reach for a solution that has worked in the past without first evaluating all extant data on machine performance.
Rules-Based Error: Bad Choices
Rules-based errors involve choices.
Because individuals are responding to if-then decision sequences, misinterpretations of rules or deviations from prescribed procedures lead to mistakes. As the DOE writes,
“People may not fully understand or detect the equipment or facility conditions calling for a particular response. Errors involve deviating from an approved procedure, applying the wrong response to a work situation, or applying the correct procedure to the wrong situation.”
Human Performance Explained in One Graph
This graph will help you visualize the different performance modes and their associated errors.
The two axes on this graph are familiarity and attention, with each increasing as they move further from zero.
Where familiarity is highest and attention is lowest, you see skills-based performance. In other words, the better we know something, the less we have to put our attention on it. The less we put our attention on tasks, the more likely errors will slip through.
On the other end, you have knowledge-based performance. Here, attention is high precisely because familiarity is lowest. When mistakes occur here, it’s in spite of that attention. It’s often because we don’t have a strong mental picture of the task, or our existing models aren’t appropriate for the situation in front of us.
Sitting in the middle is rules-based performance. In this case, there’s an equal amount of attention and familiarity. Here a misinterpretation of rules or action sequences leads to errors.
Key Drivers of Human Performance
People make the plant run. Machines help, systems help, but it’s the people who notice problems, fix things on the fly, and keep production moving. When performance dips, it’s rarely about effort, it’s usually about what’s getting in their way.
1. Safety
Nothing works right if people don’t feel safe. When the floor’s cluttered or a job feels risky, attention goes to self-protection, not the work. The basics matter: clear walkways, working guards, quick near-miss reports, and a way to fix things fast. When folks trust the environment, they relax and do the job right.
2. Efficiency
Half the waste in a plant comes from people not having what they need when they need it. Missing tools. Wrong specs. Poor hand-offs. The more friction you remove, the better the output. Digital instructions or prompts can help, but the real win comes from making sure the work itself makes sense to the people doing it.
3. Culture
You can tell what kind of culture you have by how people act when something goes wrong. If they hide it, you’ve got a problem. If they talk about it and help fix it, you’re in good shape. Recognition helps like a simple, honest thanks when someone spots a hazard or prevents downtime. That’s what builds buy-in over time.
4. Learning
The floor changes constantly. New batches, new specs, new machines. If the only time people learn is in a classroom, you’re always behind. It’s better when learning happens on the job with quick videos, prompts, checklists right at the station. It sticks better that way because it’s tied to real work.
5. Leadership
Good leaders stay close to the work. They listen, clear obstacles, and back their people when things go sideways. They don’t just count output, they ask what’s slowing it down. When operators see that, they start flagging issues early instead of working around them. That’s when performance really starts to build.
How This Applies to Manufacturing
To drive the point home, think about how you might map different manufacturing processes to this graph.
Under skills-based performance, we can put manual assemblies, routine maintenance, machine changeovers, and all of the other tasks that operators and engineers perform every day without a lot of thought. How often will even the most experienced worker make mistakes due to lack of attention?
Human error is a fact of manufacturing, but it’s easy to prevent if you outfit your operations with tools that keep operators engaged and include checks against common errors.
Knowledge-based tasks, on the other hand, might be complex, variable discrete assemblies, products that require customization, or new product introductions when associates are relatively new to a process or product. Here, the lack of familiarity leads to mistakes as workers attempt to make sense of the new task through their understanding of previous processes.
With the right tools, all of these errors are avoidable. The trick is identifying where errors are likely and outfitting your lines with solutions that will help your workers perform at their best.
Measuring Success: KPIs and Frameworks
Human performance can be measured if you collect the right information. The data is already there, it just has to be tied to the people and systems that shape the work. The goal isn’t to track everything; it’s to focus on the few measures that show how conditions and habits drive consistent results.
The Right Metrics for Human-Centered Operations
Most plants already track throughput, downtime, or scrap. Those are useful, but they don’t tell the full story. A solid human performance view mixes efficiency, quality, and learning.
Overall Equipment Effectiveness (OEE): Shows how well people and machines run together. A strong OEE means uptime, skill, and process clarity are all working in sync.
Error Rate / First-Pass Yield: Tells you how often the job gets done right the first time. Lower rework usually means instructions and checks are clear.
Training Completion and Retention: Tracks how quickly new operators reach steady performance, and how long they keep it. Short, on-the-job learning helps speed this up.
Productivity per Operator: Measures how much value each person produces per hour. It points out where process design or support systems may be limiting output.
Safety and Near-Miss Frequency: Reflects awareness and behavior on the floor. Consistent reporting and follow-up show whether safety is lived or just tracked.
A Framework for Continuous Improvement
Metrics alone don’t change anything. What matters is how the information gets used. The best systems treat measurement as part of a daily cycle:
Measure: Capture information automatically, from machines, apps, or operator inputs.
Analyze: Look for trends, patterns, and weak spots that repeat.
Act: Adjust workflows, coach teams, or redesign problem steps.
Reinforce: Use quick feedback to make the right habits stick.
Done this way, improvement stops being a quarterly project and turns into steady, daily work.
Traditional vs. Digital Human Performance Approaches
Most plants try to get better at how people work. The question is how that improvement actually happens.
Old-school methods rely on paper checklists, binders, and meetings after the fact. The newer digital setups bring information into the job itself and so problems show up sooner, and fixes happen faster.
Aspect | Traditional Approach | Digital (Tulip-Enabled) Approach |
Work Instructions | Printed binders or PDFs that go out of date fast. Operators make their own notes to keep up. | Digital steps that stay current and walk people through the job in real time. |
Performance Tracking | Data written down at the end of the shift, then entered into a spreadsheet later. | Numbers update automatically from apps, sensors, or machines—everyone sees them right away. |
Feedback and Problem Solving | Problems found after an audit or inspection, usually when it’s too late to do much about it. | Live dashboards and alerts show what’s off while the job’s still running, so the team can act right away. |
Training and Skill Building | Off-line classes or slide decks that don’t match what’s really happening on the floor. | Short learning moments built into the job, reinforced by data from real work. |
Improvement Cycles | Reviews led by managers every few months. Changes roll out slowly. | Operators and leads make small adjustments daily because they can see the data themselves. |
Data Integration | Separate systems that don’t talk to each other. Someone has to copy data by hand. | One platform that connects people, processes, and machines—nothing lost between steps. |
Ownership of Results | Information flows up the ladder; operators hear about it later. | Everyone sees the same data at the same time, which builds shared ownership. |
Traditional systems tell you what went wrong after it’s already happened. Digital tools help teams see what’s happening right now and react before it spreads.
How Digital Tools Improve Human Performance
People can only work as well as the systems backing them up. In most plants, that means getting the right info at the right time without hunting for it. Digital tools help with that. They clear away the noise, show what matters, and give feedback while the work’s still happening.
From Data to Decisions
Most factories already collect more data than anyone can use. The trouble is, it often sits in reports no one sees until later. When that data shows up right at the station i.e. machine status, targets, quality checks, operators can spot trouble early and adjust before it spreads. That’s what turns numbers into action.
Real-Time Feedback Loops
People do better when they can see the effect of what they’re doing. If cycle times slow or scrap rises, a live display lets them respond right away. Supervisors get the same view, so problems are handled on the floor instead of after the fact. Quick feedback keeps teams sharp and cuts the guessing.
Connected Workflows
Juggling separate systems slows everything down. Instructions in one place, data entry in another, machines talking through a third. When all of it lives in one platform, the job flows smoother. Operators can follow the work, record results, and move on, no bouncing between screens.
Visibility Builds Ownership
When people can see their own results, they care about them. A prompt for a quality check or a small alert when something drifts from spec is enough to keep things on track. Over time, that steady visibility builds skill and confidence because everyone sees how their actions affect the line.
Closing Thoughts
Human performance isn’t abstract. You can see it in how the floor runs day to day. It shows how people solve problems, how they communicate, and how steady the work feels.
When safety is solid and the process makes sense, people do better work. Leadership matters too which means clear direction, quick support, fair feedback. Those things set the tone.
Digital systems just help make that easier. They give people the info they need without the wait, keep records straight, and make it simpler to spot what’s drifting. Tracking simple measures like uptime, first-pass yield, or training progress keeps everyone focused on the right things.
The plants that take this seriously don’t treat it like an initiative. They build it into the job. Over time, that’s what creates a stable, skilled, dependable team.
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It’s usually not the tools, it’s the lack of visibility. Most plants can see machine data in detail but have little insight into how people interact with the process. Without that view, you end up fixing symptoms instead of causes. Once teams can see what’s actually happening in real time, improvement becomes faster and a lot more focused.
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It’s a bigger factor than most realize. Training styles, supervision, even communication habits can shift results from one crew to the next. Those differences distort KPIs and make it hard to know what “good” really looks like. Digital tracking helps expose the gaps so teams can compare shifts honestly and standardize what works best.
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It can, if it’s used with the right context. AI on its own just sees numbers; paired with process data, it can flag early signs of skill gaps, slowdowns, or bottlenecks before they show up as lost output. The real value is giving supervisors a heads-up early enough to act.
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Training alone doesn’t hold. What works is steady feedback and reminders built into the job. When prompts, checklists, or coaching cues show up during the shift, people keep doing the right things naturally. Consistency, not campaigns, keeps performance high.
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They go hand in hand. When people have the tools, data, and support they need, they take pride in their work and stick around. Plants that invest in clear processes and fair feedback usually see less turnover. A stable team performs better, it’s that simple.
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