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Worker augmentation in manufacturing
In the last few months, there’s been a flurry of commentary outlining the rise of the Augmented Worker. Almost always, “augmented worker” is synonymous with a single technology: augmented reality.
But thinking about augmentation strictly in terms of augmented reality doesn’t show the whole picture. There are a number of ways that new, assistive technologies are helping manufacturing workers do their jobs better, faster, and more safely.
Given the recent attention given to this topic, we figured it would be a good time to give a broader perspective on worker augmentation.
Here are some of the other ways that digital technologies are augmenting workers in manufacturing.
Defining Digital Augmentation
Here’s how we define digital augmentation:
Digital Augmentation and augmented work is work that integrates digital technologies into the manufacturing process to evolve how that work is done.
That’s a mouthful. But at its core, the concept is simple.
Augmentation means using digital tech to help workers go beyond what they’d be able to do without the tool.
According to Pattie Maes, head of MIT’s Fluid Interfaces research group, a few features characterize the kinds of technologies enabling the rise of the augmented worker.
- Sensitive Sensors — smaller, more compact sensors interpret how a worker moves and collect information from a worker’s environment.
- Seamless Integration — These sensors and devices are integrated into the worker’s context in a seamless way. Some are integrated into the body, others into clothing or PPE, and still others into workstations and workflows.
- Improved Responsiveness — These devices constantly interact with internal and external stimuli in real-time. They lead to context-aware assistance.
Add to this the technological advances we traditionally think of when we think of Industry 4.0—Industrial IoT, cloud computing, computer vision, digital twin, AI and machine learning, big data—and you have a mix that promises to transform manufacturing work for the better.
To illustrate this, let’s look at a common problem facing discrete manufacturers: high-mix assemblies.
In many factories, workers make their way through complex assemblies using only paper-based work instructions. When products have multiple variations or options for customization, the result is a “choose your own adventure” through bulky documents, slowing workers down and increasing errors.
The solution is to augment workers with digital, interactive work instructions. These work instructions guide workers through assemblies, using IoT devices to respond to their actions in real-time, as well as images and videos to ensure correct assembly.
Further, these work instructions don’t require operators to stop what they’re doing to find the next step. They’re integrated seamlessly into the assembly.
In-Line Quality Checks
Another way to augment operators is with IoT-enabled in-line quality checks.
Some quality issues are too small to detect with the eye. Others are the result of operator fatigue. Irrespective of the cause, many quality issues move downstream because a worker failed to identify them at the source.
The solution is to use IoT-connected devices like cameras, scales, and calipers to help operators perform their quality checks.
All manufacturers have some protocol for checking quality inline. But operators augmented with the right tools catch more non-conformances, leading to less scrap and fewer rework hours later.
So far, both of these examples have focused on the operator. One point I want to make clear is that augmented workers in manufacturing aren’t just operators. They are process engineers, quality engineers, work cell managers, IT specialists–anyone doing physical or intellectual labor on the shop floor.
So how does augmentation help engineers?
Engineers’ work is knowledge work. Therefore they need tools that help advance their thinking and creative problem-solving.
For example, engineers might spend a great deal of their time aggregating data from a variety of processes, assets, and departments. This could involve using a stopwatch to collect data from a legacy machine, pulling CNC performance data, and using excel to try to make sense of operations.
It may seem glib to phrase it this way, but analytics dashboards—which compile and display real-time data representing machine and human performance—are a form of augmentation. Because the engineer’s work requires access to current, accurate information, these dashboards enable them to evolve their analytical work and improve their decision-making.
That is the definition of augmentation.
The greatest augmentation for engineers, however, comes in the form of no-code platforms.
The reality of the modern shop floor is that machines and humans are forced to interact in increasingly complex ways.
Engineers are increasingly expected to do technical work previously performed by IT or software engineers.
With the advent of no-code, engineers can now design solutions to their processes and improvements themselves. Without writing a single line of code. Without writing a ticket to IT.
No code is making new manufacturing improvements possible by placing greater control in the hands of those closest to problems.
I’ve listed a few of the ways new tech can augment workers in manufacturing here. But when it comes down to it, someone needs to create the applications and processes that enable these augmentations in the first place. This is what no-code platforms do. In augmenting the work of engineers, which in turn augments the work done in an entire factory, no-code platforms help manufacturers reach new levels of process and cost-efficiency.
Beneath each of these augmentations is a digital technology that supports and amplifies work a manufacturer is already doing. It connects humans and machines, internal and external, in a way that leads to an evolution of manufacturing work.
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