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.
At 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.
The Three Modes of Human Performance
Recent research in psychology and management studies has identified 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 error.
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.
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 (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.
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.
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.
Tulip helps manufacturers augment employee production rather than automating it away. The company’s app platform connects people, machines, and sensors to optimize manufacturing. Curious how Tulip can help you improve human performance? Get in touch for a free demo.