In the last 5 years, automation has jumped to the forefront of public discussion as the service, transportation, and countless other labor-intensive industries are on the brink of wide-spread automation. Still, the industry that remains the greatest flashpoint for automation is ours, manufacturing.
In a lot of ways, commentators treat manufacturing as the canary in the jobs coal mine. So goes manufacturing goes the global economy. It seems a new report comes out every week forecasting how many manufacturing jobs will be gained (a lot!) and lost (even more!) to advanced automation in the coming decades. Going by some of these reports, it’d be reasonable to conclude that full industrial automation is just around the corner.
The problem with these dire predictions is that they’re painting an extremely complex reality in broad strokes. One recent prediction forecasts that robots will replace “20 million factory jobs” worldwide (14 million in China) by 2030. Another argues that, in the same period, 2.5 million manufacturing jobs are expected to go unfilled in the U.S. alone.
If you put a little pressure on it, it isn’t hard to square this discrepancy. The fact is that the nature of work in manufacturing is changing, and that the jobs being created in manufacturing require a very different skill set than those being lost.
This post is an attempt to bring some clarity to the issue of industrial automation. I’ll look a few enduring myths, and suggest reasons why they don’t capture the full picture.
1.) Automation is cheaper than human labor.
The reasons manufacturers turn to industrial automation are the reasons we’re all familiar with. Robots perform repetitive tasks better than humans. Labor is expensive. Robots can work in conditions that would be deadly to humans.
All of these are important, but the greatest driver of the adoption of industrial automation is the bottom line.
But automation doesn’t always lead up and to the right, as it were. As Forbes has noted, “Complexity, volume, and margin all combine in different ways to rule out the use of robots in many applications.”
There are hidden costs to automation lurking below the sticker price. Robots are expensive to maintain. The more complex, the automation, the more difficult it can be to diagnose and repair. And robotics engineers are more expensive to hire and retain than shop floor associates. This can result in an ironic situation where the labor costs of maintaining robots is actually higher than the cost of keeping a larger human labor force.
This was a lesson overeager US automakers learned in the 1980s, and that Tesla learned the hard way a few years ago. As early as 1993, researchers found there was an ideal balance of automation and human labor.
Despite radical improvements in technological capability, the situation hasn’t changed much since.
2.) Everything humans can do well, robots can do better and more reliably
It’s true that robotic solutions are growing more advanced every day.
But irrespective of recent improvements in AI, flexible gripping mechanisms, and mobility, there are a huge number of tasks that are best suited to humans.
This is particularly true in discrete manufacturing, where the demand for customizable production, high-mix assemblies, and the fragility of manufactured items makes automation impractical.
It bears repeating: if a factory is producing millions of a single part in adverse conditions, industrial automation is no doubt the right call. But for operations with variable production, quick new product introduction cycles, or short production lifecycles, it often makes more sense to train human labor than purchase and program new robotic technology.
3.) Robots have taken all of the jobs. The labor market in manufacturing is shrinking.
Depending on which source you consult, between 2 and 3.5 million manufacturing jobs are expected to go unfilled in the next decade. According to the most recent Bureau of Labor Statistics report, the manufacturing sector is still growing at the rate of 8,000 and 17,000 new manufacturing jobs per month.
So it isn’t true that manufacturing work is disappearing. It’s worth outlining why.
In the last 40 years, automation has taken over most repetitive tasks. We know this. But what’s talked about less is that fact that the jobs that remain are more intellectually and physically complex.
At the same time, manufacturing technology has multiplied in sophistication. Engineers are routinely expected to perform tasks previously done by software engineers. The explosion of data from connected machines means that engineers double as citizen data scientists. And when machines do break down, maintenance is far from trivial.
In short, manufacturing work is now knowledge work. The unfilled jobs in manufacturing are often jobs that require skills in software, data science, and robotics.
The question becomes: what can we do to fill them?
4.) Full automation is on the horizon. There’s nothing we can do to stop it.
For all of the critiques I’ve made of automation here, it still serves to confront it honestly. Robotic technology and AI are improving and, in the long run, a greater number of labor-intensive tasks will be automated.
Is this going to happen in the near future? No, but it’s worth approaching the issue with some candor.
Does this mean we should despair? Maybe. But that still doesn’t fill those 2 million manufacturing jobs that will stubbornly resist automation.
Given that most manufacturing work is now knowledge work, what’s needed is a program that will give workers the opportunity to reskill and upskill as the market demands. Right now, data scientists, software engineers, and, yes, robotics engineers are desperately needed in manufacturing.
Instead of hastening the advent of automation, manufacturers should consider how they can help to close the skills gap now. For every job lost to automation, manufacturing loses an invaluable source of domain knowledge. It’s important to consider what’s really lost with a job.
Our answer to this is to augment workers with the technology that they need to evolve their work.
When empowered with the right digital technology, human workers can do more work, better than either robots or humans alone.
Closing the skills gap isn’t something that manufacturers can do alone. It’s going to take collaboration between industry, education, and government. But it’s essential that we don’t believe everything we hear.
Manufacturing needs to separate myth from reality, and define the problem properly so that we can come to a solution that rises to the challenge.
Tulip’s manufacturing app platform give manufacturers the tools they need to thrive in a changing industry. Curious how Tulip can help improve connection and collaboration in your operation? Get in touch here.