Augmented Worker

The history of manufacturing could easily be told as one of automation. From textile looms to automated assembly lines to intelligent cobots, for centuries manufacturers have found ways to replicate human labor at scale.

But thinking strictly in terms of automation doesn’t tell the whole story. More often, new technologies have worked alongside humans to empower them to work more efficiently, safely, and accurately. This is particularly true during Industry 4.0, when new digital technologies are enhancing every aspect of manufacturing work.

This process of enhancing human workers is augmentation. Given that research and experience have shown over and over that the best solutions are those that amplify the capabilities of manufacturing workers, this much is clear:

The future of manufacturing is augmentation.

The guide will introduce you to augmentation in manufacturing–its technologies, its use-cases, and its principles. We’ll define what augmentation means in the context of manufacturing, explain why manufacturing needs augmentation now, and survey the different technologies and applications that are augmenting workers on the shop floor as we speak.

“Worker Augmentation” Defined

In the context of manufacturing, “worker augmentation” refers to the use of technology to improve how workers do their jobs. Augmentative technologies are integrated and assistive. By integrated, we mean that they are a natural, unobtrusive part of a worker’s environment. And by assistive, we mean that they simplify or control some of the variables contributing to poor human performance. Ultimately, augmentative technologies allow workers to perform more specialized tasks with a higher degree of care.

There are a wide variety of types of augmentation, and augmentative technologies can assist with both physical and mental labor. Now boasting smarter, more intuitive interfaces, current augmentative technologies are characterized by seamless integration into the manufacturing environment.

Augmentative technologies can take a number of shapes and forms. The example that most manufacturers associate with augmentation is augmented reality headsets–visual displays that use machine learning, AI, and other forms of context analysis to overlay new information into the wearer’s field of vision.

But these are far from the only augmentative technologies

Other examples include environmental and bioinformatic sensors that monitor ambient conditions and worker health in real-time, alerting workers if there’s a potential for danger. There are computer vision systems that interact with operators as they work. Augmentation can also refer to technologies that relieve a worker’s cognitive load, such as real-time data aggregation and analysis or interactive work instructions. Augmentation can be as simple as IoT connected in-line quality checks, or as complicated as artificial reality.

When we talk about augmentation we’re talking about all of these technologies and more. We’re talking about any external, assistive system that lets manufacturers do their jobs better, more efficiently, and more safely.

Augmentation Evolves Work

Another way to define augmented work is work that integrates digital technologies into the manufacturing process to evolve how that work is done. Here, the use of new digital technologies actually changes the nature of manufacturing work.

Whether digital technologies assist workers or change how they work, manufacturers are already using augmentation to achieve significant competitive advantages. You can measure gains in performance from a human perspective: better attention, more comfortable conditions, more innovative thinking, long-term worker well-being. Or in terms of manufacturing goals and KPIs. Fewer errors. Higher quality. Higher throughput. Faster changeovers. Less downtime.

The guiding philosophy of augmentation is that improvements in human performance will translate into better manufacturing performance.

Why augmentation now?

There are three factors contributing to the need for augmentation at this particular moment.

First, manufacturing is facing a growing labor shortage. Over the next decade, research firms predict 2.2 million jobs will go unfilled in manufacturing. This is largely due to what researchers call the skills gap, or the lack of alignment between the skill set required for modern manufacturing work and the skill sets extant in the labor market.

Second, manufacturing work is changing at an accelerated rate. The tools of the trade, however, haven’t evolved quickly enough to help workers stay abreast. Changes in work have resulted in a situation in which the complexity of tasks increases the chances of poor human performance. This is true from front-line operators, who are tasked with the assemblies and machine maintenance too complex or variable to be automated. It’s equally true for manufacturing engineers, who are increasingly expected to perform tasks previously done by software engineers, IT, or data scientists.

Finally, automation is still not feasible for many manufacturing applications. Automation can be prohibitively expensive. It’s hard to scale. And, ironically, it’s labor-intensive. (Someone has to steward, program, and maintain all of those robotic arms). As Forbes recently noted, “Complexity, volume, and margin all combine in different ways to rule out the use of robots in many applications.”

Attempts at full automation remind us that, for all of their faults, humans are still magnificent machines. They’re intelligent, creative, flexible, adaptable, and able to learn and innovate. Placed side by side with automated solutions, you’d be hard-pressed to find better-articulated grippers, “computer” vision (what are our brains but squishy computers?), and real intelligence.

All of these factors (a growing skills gap, error-prone work systems, and the challenges of automation) have resulted in a situation in which manufacturers will need to bolster their workforce to achieve more.

The solution to these challenges, in other words, is augmentation.

Augmentation acknowledges that humans are central to manufacturing and that they’re going to remain central to manufacturing for the foreseeable future. They will, however, need assistance performing optimally.

Worker augmentation is possible because manufacturing has reached an inflection point in technological development.

While the potential of this technology is great, it’s often not flexible enough for manufacturers to adapt to their unique challenges. It’s thus not uncommon to see paper forms and stopwatches on the shop floor, even in an era of fantastic digital potential.

There are a few, basic advances that, working in concert, have made a broad range of augmentations possible. This section will review the enabling technologies behind augmentation, and suggest ways that manufacturers might deploy them in flexible, customizable ways.

IoT

Most augmentative strategies work because they allow humans to work in conjunction with machines in intelligent ways. They respond to machine outputs and conditions in real-time and feed to those signals to operators in ways that allow them to make informed decisions. IoT connectivity has made such communication possible.

You might even have heard the “connected worker” used as a synonym for the “augmented worker.” This IoT connectivity is precisely what it’s referring to.

For manufacturers considering augmenting their workforce, an IoT infrastructure (wifi, cloud, and security) is a good place to build flexibility and communication into the foundation of an augmentation strategy.

Sensors

One characteristic of modern augmentative technologies is seamless integration with the manufacturing environment. This is only possible because sensors have decreased in size while increasing in potential. These flexible, responsive sensors can fit into clothing, on the body, or at dozens of points in a production station to interpret the environment and record events as they unfold. Increasingly, these sensors can compute at the edge. The data collected by sensors is communicated over IoT, resulting in a factory where humans and objects are in constant “dialogue.”

For manufacturers desiring flexibility, these sensors should measure human and machine performance alike.

Big Data

One of the key features of augmentative tech is that it can respond to a worker’s actions, ambient conditions, or machine data in real-time. These systems aggregate the data collected throughout a manufacturing operation and help organize it in a way that improves a human’s ability to produce insights. AI, machine learning, and other predictive and classifying algorithmic technologies can improve operators’ and engineers’ ability to make decisions.

Augmenting human performance requires understanding the root causes of human failures.

According to the Department of Energy, as many as 80% of work errors in industrial contexts are attributable to human error.

This number requires some context.

In reviewing decades of research, the DOE found that, of those errors, only 30% were caused by individual error. The rest were attributable to work structures characterized by “error likely situations,” or, situations where “the demands of the task exceed the capabilities of the individual or when work conditions aggravate the limitations of human nature.”

The DOE concluded that “human error… is not the cause of failure alone, but rather the effect of deeper trouble in the system. Human error is not random; it is systematically connected to features of people’s tools, the tasks they perform, and the operating environment in which they work.”

This is a significant finding. It suggests that many of the traditional causes we attribute to human error–fatigue, slips of concentration, poor training, bad work environments–are not reducible to human performance. Rather, good performance is an effect of good system design.

The report continues, “no matter how efficiently equipment functions; how good the training, supervision, and procedure; and how well the best worker, engineer, or manager performs his or her duties, people cannot perform better than the organization supporting them.”

So how can augmentative technology create conditions for optimal human performance? How can organizations work to support and empower their people? Let’s look at some common causes of manufacturing problems to understand how the right technology can help.

Fatigue

Fatigued workers are prone to make mistakes. And they’re also prone to injury on the job. To combat fatigue, augmentative technologies can detect when a worker starts to show signs of fatigue.

In manufacturing, wearable sensors embedded in protective gear can detect when a worker is using poor posture as they lift a heavy load (a sign of fatigue). For operators engaged in assemblies, digital work instructions foster interaction and help improve engagement. Wearable devices can sense changes in a worker’s bodily functions characteristic of fatigue and alert them to take a break.

Concentration

For all their virtues, human workers are still prone to lapses in concentration. Even the best workers will have bad days. These slips are often due to the nature of manufacturing: long hours, repetitive tasks, and mentally exhausting work.

Augmentative technologies can help improve concentration by keeping workers engaged in their tasks. Some do this by “gamifying” work, or turning aspects of repetitive labor into a game in which one constantly competes with oneself. Others do this by replacing static instructions with interactive devices.

When mistakes do happen, IoT assisted inline quality checks help humans detect nonconformances before they move downstream.

Training

Operators are often required to learn new tasks on short notice. New hires have a brief window to learn new skills and processes before starting on production lines. And the seasoned associates best able to train recruits are retiring at a rapid rate. All of this contributes to training regimes that aren’t as effective as they could be.

New augmentative technologies help train and reskill employees. For example, manufacturing apps for training can walk a new hire through a process step by step. With media-rich and interactive training modules, these apps can be designed to accommodate all learning styles, and help operators learn by doing from day one. Further, IoT connection and embedded sensors let these apps detect whether or not a worker is performing the new skills properly. This allows for early intervention and ensures that operators don’t reinforce incorrect techniques.