Manufacturing Jobs, Automation and Future Assumptions
Anyone who is interested in how manufacturing jobs are evolving (and disappearing) should be sure to read Adam Davidson’s excellent article in the current issue of The Atlantic: “Making it in America.” The article is based on interviews with workers and executives at Standard Motor Products, a manufacturer and distributor of auto parts with a factory in Greenville, South Carolina.
Here’s a a quote from the article, focusing on the future prospects for an unskilled worker named Maddie:
Tony [the factory manager] points out that Maddie has a job for two reasons. First, when it comes to making fuel injectors, the company saves money and minimizes product damage by having both the precision and non-precision work done in the same place. Even if Mexican or Chinese workers could do Maddie’s job more cheaply, shipping fragile, half-finished parts to another country for processing would make no sense. Second, Maddie is cheaper than a machine. It would be easy to buy a robotic arm that could take injector bodies and caps from a tray and place them precisely in a laser welder. Yet Standard would have to invest about $100,000 on the arm and a conveyance machine to bring parts to the welder and send them on to the next station. As is common in factories, Standard invests only in machinery that will earn back its cost within two years. For Tony, it’s simple: Maddie makes less in two years than the machine would cost, so her job is safe—for now. If the robotic machines become a little cheaper, or if demand for fuel injectors goes up and Standard starts running three shifts, then investing in those robots might make sense.
“What worries people in factories is electronics, robots,” she tells me. “If you don’t know jack about computers and electronics, then you don’t have anything in this life anymore. One day, they’re not going to need people; the machines will take over. People like me, we’re not going to be around forever.”
As the article makes clear, Maddie has a job largely because she is still cheaper than installing automation equipment — assuming a two-year payback period for the equipment. But now consider that just last year the Boston Globe reported that start-up company Heartland Robotics expects to introduce a manufacturing robot that will sell for around $5,000. The cost of automating jobs like Maddie’s seems likely to fall quite dramatically in the relatively near future. At a cost of $5,000 — or even $10,000 — a robot could easily pay for itself within a matter of months.
Now here’s another quote from the article focusing on a skilled operator named Luke. Luke runs computer-controlled “Gildemeister” machines, which cut precision parts used in fuel injectors:
After six semesters studying machine tooling, including endless hours cutting metal in the school workshop, Luke, like almost everyone who graduates, got a job at a nearby factory, where he ran machines similar to the Gildemeisters. When Luke got hired at Standard, he had two years of technical schoolwork and five years of on-the-job experience, and it took one more month of training before he could be trusted alone with the Gildemeisters. All of which is to say that running an advanced, computer-controlled machine is extremely hard. Luke now works the weekend night shift, 6 p.m. to 6 a.m., Friday, Saturday, and Sunday.
When things are going well, the Gildemeisters largely run themselves, but things don’t always go well. Every five minutes or so, Luke takes a finished part to the testing station—a small table with a dozen sets of calipers and other precision testing tools—to make sure the machine is cutting “on spec,” or matching the requirements of the run. Standard’s rules call for a random part check at least once an hour. “I don’t wait the whole hour before I check another part,” Luke says. “That’s stupid. You could be running scrap for the whole hour.”
The conventional wisdom, of course, is that while Maddie’s job may well be in danger at some point, Luke has little to worry about. Demand for skilled machine operators is strong and is likely to increase in the future. But is that really the way things will play out?
Any time workers are highly paid and in short supply, there is a clear incentive for innovation that will either eliminate those workers or “dumb down” the job so it can be done by less skilled people. Currently, Luke spends a lot of effort testing parts to make sure they remain in spec, and then recalibrating the machine as necessary. Is it possible that machines like the Gildemeisters will someday be able to automatically test parts and then make the required adjustments? Perhaps it might be possible to use precise computer imaging technology to analyze parts and then auto-adjust the machine to produce consistent results. Since this hasn’t happened yet, we can assume the technology probably isn’t there. But what about five or ten years from now?
In a previous post, I wrote about the new “cloud robotics” strategy being pursued by Google, Willow Garage and other companies. Cloud robotics involves migrating much of the software that controls robots or other machines into centralized servers. Could something similar be done with computer-controlled machine tools — so that perhaps dozens of machines are controlled by advanced software that incorporates artificial intelligence, eliminating the need for individual operators?
Yet another possibility involves offshoring. Jobs in areas like customer service can, of course, easily be outsourced to low-wage countries. Could skilled machine operator jobs also be offshored? If the parts are moved around robotically so that the entire job consists of analysis, programming and control, then those tasks could potentially be done from anywhere. China currently has a huge surplus of college graduates — a high percentage of whom have studied science and engineering — and for jobs of this type, English language skills wouldn’t necessarily be critical. Moving an entire factory to China results in major transportation costs and logistics issues. Moving specific jobs offshore electronically, as is currently done in the service sector, might someday prove more cost effective.
In other words, our conventional assumptions about the jobs of the future are not necessarily all that reliable. While Maddie’s job is certainly at risk, even Luke may not turn out to have the level of job security we expect over the longer run.
A final quote is from a discussion with the CEO of the company:
To keep the business of the giant auto-parts retailers, Standard has to constantly lower costs while maintaining quality. High quality is impossible without good raw materials, which Standard has to buy at market rates. The massive global conglomerates, like Bosch, might be able to command discounts when buying, say, specially formulated metals; but Standard has to pay the prevailing price, and for years now, that price has been rising. That places an even higher imperative on reducing the cost of labor. If Standard paid unskilled workers like Maddie more or hired more of them, Larry says, the company would have to charge its customers more or accept lower profits. Either way, Standard would collapse fairly soon.
The main point here is that when a company is squeezed between rising costs for commodity inputs and an inability to raise its own prices, the only significant variable it really has to work with is labor. This dynamic is not limited to manufacturing companies like Standard Parts; previously, I made a similar point about the fast food industry.
Fast Food (or beverage) workers are classified as service workers by the government, but from a technical point of view, fast food is really a kind of just-in-time manufacturing. As automation technology gets better and cheaper, and as the price of food commodities continues to rise in response to ever increasing global demand, it seems likely that the rate of job creation in fast food as well as in a variety of other service areas may be in danger of falling short of expectations.
When it comes to the jobs of the future, beware the conventional wisdom. A great many widely-held assumptions seem to be based primarily on simply projecting the continuation of existing trends, rather than on any meaningful analysis of what the industries of the future might really look like.