Cognitive Load in Manufacturing: How Augmentation Can Improve Decision Making

There are many factors that can hurt manufacturing performance. Among the long list of usual suspects, however, we’ve noticed one conspicuous omission: cognitive load.

While cognitive load has a long history of use in education and ergonomics, it’s a concept that manufacturing is just starting to play around with.

In this post we’ll provide reasons why manufacturers should care about cognitive load, and outline some of the ways augmentative technologies can help minimize it for better performance.

Cognitive load is a concept for explaining how demands on a person’s working memory can inhibit learning and performance. According to Cognitive Load Theory, tasks that exceed inherent limitations to memory and attention lead to precipitous decreases in information retention and success.

In the Cognitive Load Theory, there are three types of cognitive load.

1. Intrinsic – This is the cognitive load associated with a topic or task. You can think of intrinsic cognitive load as the objective difficulty of a task.
2. Extraneous – This is the way information or tasks are presented. How we encounter information determines the resources we have available to interpret it.
3. Germane – According to Psychologists World, germane load “is produced by the construction of schemas and is considered to be desirable, as it assists in learning new skills and other information.”

For our purposes, intrinsic and extraneous load are the most relevant.

Manufacturing work has a high intrinsic difficulty — With automation responsible for most repetitive tasks, shop floor associates now perform complex, variable work. Engineers are responsible for understanding and improving intricate systems.

Put simply, manufacturing work is hard.

Manufacturing work has a high extraneous load — The structure and form of manufacturing work are not always conducive to accurate execution. Further, many manufacturers do their work without adequate technological support. Operators working on high-mix assemblies with options for customization often do so following paperwork instructions. And despite the advances of Industry 4.0, many engineers still collect data with a stopwatch and clipboard.

In other words, traditional workflows and tools increase the cognitive load on workers.

It’s no secret that human error is a problem in manufacturing.

Even so, human error is badly misunderstood.

Recent research has shown that most mistakes in industrial contexts are not the fault of individual workers. Instead, these mistakes are a product of “latent organizational weaknesses,” or aspects of work systems that become “error traps.”

This research echoed years of organizational and psychological studies in finding that:

• Humans tend to perform poorly under high stress and time pressure
• Human “fallibility” is often the result of conditions that ask workers to exceed the limitations of human nature
• Error is more likely when people work within complex systems
• People overestimate their ability to maintain control under difficult working conditions

So is it really any surprise that manufacturing has a problem with human error? Workers are placed in high-stress situations with hourly and shift quotas; they’re asked to do enormously complex work with limited resources, and their work is embedded in complex systems.

All of this is a recipe for cognitive overload, and ultimately for reduced performance quality.

Reducing the cognitive load of workers is an easy way to improve performance across lines, from more efficient production to a higher percentage of quality throughput.

The trick to reducing cognitive load in manufacturing is to create work systems that enable workers to maximize their mental and physical resources.

To do this, manufacturers should consider outfitting their lines with tools that let workers focus their attention on the task at hand. New technologies have the ability to minimize the effects of stress and time pressure while bringing the many variables outside of workers’ control under management.

The best way to reduce cognitive load is through augmentative technologies. Augmentative technologies assist operators and engineers as they work. They integrate into the manufacturing environment to enhance a worker’s abilities.

Here are some examples of augmentative technologies that can improve manufacturing performance by reducing cognitive load.

Paper-based work instructions are difficult to follow. Finding the next step can divert an operator’s attention away from their work. All of the mental energy spent interpreting work instructions is energy that isn’t spent on value-add work.

Interactive digital work instructions can help reduce cognitive load by guiding workers through complex processes. These work instructions progress with workers, presenting them the information they need, when they need it. Rich media like photos and videos show workers exactly how to complete the next step. And IoT connectivity can poka-yoke lines, reducing the stress of workers and removing common sources of error from the equation.

Cognitive Load Theory emerged from the study of education. Therefore, it’s particularly applicable to training applications.

Augmentative technologies like digital training apps help streamline the learning process by presenting information to learners in targeted, interactive modules. These apps can be configured specifically for the task in question, so trainees can practice on precisely the tasks they’ll execute. Further, training apps can break down difficult, multi-part processes into easily digestible subtasks. This reduces the cognitive load by simplifying the scope of the work, and ensures that they learn each step accurately.

Making accurate, informed recommendations for improvement is one of an engineer’s most important responsibilities. However, time spent collecting and aggregating data is time and energy that could be better spent doing the creative work of analysis.

Real-time analytics dashboards can help reduce the attention and energy devoted to pre-analysis data work, and free up the resources for important critical thinking. Analytics dashboards display up-to-the-minute information on human and machine performance, arming engineers with the best possible information. By simplifying the way data is collected and presented, it enables engineers to put their full attention into improving their value stream.

Tulip’s Frontline Operations Platform streamlines work stations, helping workers devote all of their resources to the task at hand. Curious how Tulip can help reduce cognitive load in your processes? Schedule a free demo today.