Humans Need Not Apply: Rise of Robot Factories Leading 'Fourth Industrial Revolution'

Humans Need Not Apply

Rise of Robot Factories Leading 'Fourth Industrial Revolution'

 

German Chancellor Angela Merkel visiting the model shop floor. Lenmart Priess/Siemens

By Rose Jacobs

Busy day? A million things to do? Well, here’s depressing news: you’ll probably mess up about 1,000 of them. That’s what the research shows, at least: for every million tasks a human performs, even the best of us inserts mistakes between 500 and 1,000 times. It might seem a lot, but think of the number of emails you send containing a typo, the number of dishes that make it to the drying rack with a fleck of food still on them, the gaffes when talking with colleagues, the mismatched socks you only spot at lunchtime.

None of these slip-ups is likely to prove very costly, whether in terms of time, money or reputation. But the stakes grow higher in certain environments: a pharmacist getting a dosage wrong can take a life; a trader with “fat fingers” can cost his employer millions. It was with this idea in mind, just over 25 years ago, that a team of engineers and scientists at Siemens began to rethink one particular shop floor. The factory in Amberg, a small town near Nuremberg in Germany, made controllers – the boxes stuffed with circuit boards and switches that act as brains for other factories. And it did a pretty good job of it, with customers from across countries and sectors, and a defects per million rate of 550.

But even that number felt too high, particularly given that a broken controller can quickly shut down a factory, costing its owners millions of euros per day in stopped production alone. So the team at Siemens began moving the factory towards greater automation, counting on computers to beat humans in the race for quality. In 1990, 25% of the shop floor was automated; today, it is 75%. And the defect rate has dropped sharply – to 11.5 per million. Output has increased 8.5 times while employee numbers and floor space have stayed steady.

Amberg has become something of a showcase for what automation can achieve; Angela Merkel visited in February and called it an example of Germany’s wealth of “ideas and well-educated workers”. But its real interest for managers, politicians and workers is what it can tell them about the future. The plant is Siemens’s testing ground for a huge development in automation, where factories act less as the setting for a series of sequential steps and more as networks – networks in which assembly lines communicate not just with one another or within the company, but with systems elsewhere and – this is key – with the very products being produced. An automobile bonnet rolls up to the paint machine and tells it, “I should be white”; the next one sends the message to paint it blue.

In Germany, the engineers and academics working to create this “fourth industrial revolution” call it Industrie 4.0; in the US it’s referred to as “the industrial internet”. General Electric describes it thus: “[It is] the tight integration of the physical and digital worlds . . . [enabling] companies to use sensors, software, machine-to-machine learning and other technologies to gather and analyse data from physical objects or other large data streams – and then use those analyses to manage operations.”

It is also, according to Volkmar Koch, a partner at the consultancy Strategy&, a chance for Europe to lead where in the past it has followed: whereas digitisation of the consumer world “is basically owned and shaped by US companies”, no country or region yet dominates the transformation of industry.

You might expect a world built on sensors, software and machines to be devoid of humans. But in Amberg, the 10,000-square-metre shop floor is populated by 1,020 workers over three shifts. And their labour looks relatively physical: a young man lying on his back inches his way under an elegant blue-and-grey machine, as you would under a car needing repair; a woman nearby bends over a circuit board wielding tweezers. Yet other members of staff peer at screens, never touching the products rolling down glassed-in assembly lines.

 

Robot arms weld bodywork at the Porche factory in Leipzig, Germany. Sean Gallup/Getty

“A digital future can frighten people,” says Günter Ziebell, production unit leader in Amberg. “But we complement automated tests with eye checks.” More to the point, this project has created demand for people with experience and creativity, who can improve the processes. So the management structure in Amberg has become very flat, allowing, for example, line workers to speak with the IT department directly rather than go through their bosses. Any employee can initiate a project that requires an investment of less than €10,000, and managers simply check every quarter that their teams are neither spending too much nor too little. Employees also earn bonuses when they suggest changes that are later implemented. The average employee earns an additional €1,000 per year this way, says Ziebell. He stresses the importance of schemes like this: “If a digital factory is being managed top-down, you wouldn’t get many advantages from it.”

But even if increasing automation hasn’t sapped jobs in Amberg, fast-growing efficiency means new plants might have been built to meet rising customer demand – and new positions to fill them – are now unnecessary. It is an issue that the Germans, at least, are attempting to address head-on, with plans under way to form a national-level working group for Industrie 4.0 that includes employee representatives as well as private businesses and industry bodies.

Dieter Wegener, Siemens’s coordinator for Industrie 4.0, argues that companies aren’t pushing these developments forward – consumers are. We want customised products, we want them now, and we want them made efficiently, whether to bring down prices or preserve natural resources. This isn’t possible without networked production processes. As Mr Wegener says, “This is coming from you and me.” He also argues that Germany is at least two years ahead of the industrial internet community in the US “but we appear as if we’re following the Americans. The Americans are better at marketing.”

Roman Friedrich, a Strategy& expert on digitisation, is more cautious: “By definition, these changes are happening with such speed that you might not stay dominant for long. There are pockets of excellence and we see shifts in who’s ahead every year.”

And still, there are serious challenges to overcome, beyond what all of this might mean for workers. Standardisation is one; it doesn’t do much good for your soda bottle to signal to a bottling machine if they don’t speak the same language. A survey by the consultancy Accenture found last year that a third of companies eager to embrace the industrial internet cited “consolidation of disparate data” as a grave concern. And that didn’t just mean data from along the supply chain, where different companies need to find similar standards, but also between departments in their own operations.

Security, inevitably, was another top worry. Technicians at Siemens’s headquarters in Munich have recently started trying to hack into the Amberg factory’s systems, as tests to protect against the real deal. To take full advantage of “smart factories”, every link in the supply chain must be secure – a huge challenge, and one with an inherent conundrum in that taking full advantage of “smart factories” also necessitates allowing a wide distribution of information; in Amberg, any employee can see the real-time data about each product on the assembly line. Companies must find a way to find a balance between transparency and security.

For Wegener, a third challenge is remembering the factors, such as efficiency, customisation and speed, that are driving the revolution (or evolution, as he prefers) – and making sure Big Data isn’t tapped simply for the sake of tapping Big Data. It has to add specific value to each operation. “There’s no benefit to making something smart,” he says, “without it making sense”

The Rise of Turing Robots Leads to a Fall in Wages

 

A staff member stands next to robots at a Kuka Robotics plant in Shanghai on August 13, 2014. Pete Sweeney/Reuters

By Dagobert Brito and Robert Curl

From 1970 to 2007, the U.S. economy grew at an average rate of about 3 percent a year (2.8 percent for the period from 1970 to 2012). The distribution of labor income was stable until about 1980.

Then something happened. The economy’s real growth rate remained on the same trend, but the distribution of income started to become less equal.

In 1970, the top 10 percent of the population earned about 32 percent of labor income; by 2012, the share of the top 10 percent increased to 47 percent. We explain these phenomena by noting that computer technology in the form of automation is replacing workers who perform tasks that can be reduced to an algorithm.

In order to think about automation in comparison with people, we refer to an automation that displaces a worker as a Turing robot. The machine age replaced muscle power with machines. However, machines still needed humans to operate them. The total number of jobs increased with growing production.

The second machine age is replacing humans in tasks that can be reduced to an algorithm. It will be difficult to replace the jobs lost to computers. It will be very difficult to replace the jobs lost to computers with high-paying jobs.

We assume that the population is heterogeneous in its initial endowments of talents. We believe that the highly paid group has attributes that cannot generally be taught. This assumption seems elitist and thus very controversial, but data from the legal and economics professions suggest that this assumption is true.

However, if the jobs are to be high paying, it must be the case that the skills cannot be taught to other workers. If the skills of the highly paid group could be taught to the other workers, the market would erase the skill premium.

People will argue that technological development has happened in the past and that the growing economy created new jobs to replace the old. This is possible, but the problem is now much more difficult.

Increasing the number of jobs for humans will mitigate the problem of inequality in the distribution of income only if these new jobs have three properties: (1) they must be jobs that a computer cannot perform; (2) they must require skills that are scarce in the human population; and (3) the new jobs must include a substantial fraction of the population. Increasing the number of jobs, such as supermarket checkers, that do not have a scarce skill requirement will not solve the problem.

A large fraction of the population may have skills that a computer cannot perform, but these skills may be common to a large fraction of the population. Wages are set at the margin, and if at the margin humans are competing with robots, the cost of robot labor will be determining the human wage rate.

The argument is slightly more complicated. Computers are replacing humans with skill sets that were relatively well paid, so the competition between human workers and automation is not at bottom-wage levels. These displaced workers are forced to find lower-paying jobs, thereby lowering wages for all workers with whom they compete.

For high-paying jobs to return, enough human jobs must be created to employ the entire labor force so that at the margin humans are not in competition with robots. The Catch-22 of this proposition is that if the jobs are high paying, there are incentives to develop the technology so they can be performed by Turing robots.

The limits of computer technology or human ingenuity have not been found.

The seminal book The Second Machine Age by Erik Brynjolfsson and Andrew McAfee (2014) addresses the implications for the economy and society of the latest developments in computers, connectivity and robots. Their work makes it clear that the full impact of the computer revolution lies ahead.

This second machine age will result in even greater production of material goods. We may well be entering an era where scarcity of material goods ceases to be an issue in the developed world.

The second machine age may eliminate the scarcity of material goods, but it will also increase unemployment and inequality. We believe this, in turn, may result in increased redistribution and a substantial increase in the role of government.

It is not difficult to make the argument for redistribution without calling for altruism from the rich. In an economy where incomes are very unequal, social stability may require either the use of force or substantial redistribution. If goods are not scarce, the elites should favor redistribution because it is cheaper to maintain social order through the transfer of non-scarce goods than by resorting to force to maintain order.

In a democracy, transfer payments may be the cost of maintaining political power. If a substantial fraction of the labor force is unemployed, the result is a “bread and circuses” society, or in the modern context, a “food stamps and television” society, according to Bloomberg View columnist Alice Schroeder.

Objections to such transfer payments will be raised. Most Americans believe in the free market, and factor endowments determine the “legitimate” distribution of income. In a market economy, the marginal product of a factor determines its value.

The intuition behind this belief is powerful. In an economy where factors are paid their marginal product, workers’ wages reflect the value of their labor, and the capitalists’ income reflects the value of time and the return for delayed gratification.

The neoclassical paradigm, shared to some degree by a majority of American economists, gives theoretical support to this argument. It takes a very small leap in logic to argue that this implies that the free market endows individuals with the just fruits of their labor.

Despite these objections, we believe that transfer payments will be necessary for social stability. Although we can find no alternative to transfer payments for avoiding a humanitarian crisis, we believe that a “bread and circuses” society is unhealthy.

We recognize that our judgment of a “bread and circuses” society reflects our background and personal set of values. Saying that learning to read and play the piano, and eating home-cooked meals, is better than watching television and eating junk food is a value judgment.

A motivation for our work is to develop arguments against a “bread and circuses” society that do not depend on value judgments. We summarize them here:

1. The need to maintain competent elites. Successful societies need competent elites for governance, innovation, creativity, the transmission of values and as protection for unforeseen external threats. We believe that such necessary class mobility will be impaired if there exists an underclass that is not part of the recruiting pool for elites. If talent is scarce, it is necessary to recruit from as wide a pool as possible. This requires the education and employment of as large a fraction of the population as possible.

2. The need to avoid loss of social capital. Without the possibility of work, there are few incentives to acquire an education and the other forms of human and social capital needed for employment. These are the same attributes needed to be a functioning member of a civil society and for creating functional families. There are positive externalities to work and education, and without education many elements of a dysfunctional society follow.

3. The need to maintain a civil society. There is evidence that employment is an important factor in the maintenance of a civil society. Employment has positive externalities.

4. Unhealthy shifts in the economic system. In a democracy with a large unemployed population, governmental redistribution is inevitable and the role of government increases. Income is concentrated in the productive elite and the mandarin class. The role of the free market as an institution of decentralized power is diminished.

The aim of the present work is to search for new options that would keep our society healthy and to explore the consequences of choosing among these options.

Conclusions

The first machine age, ushered in by the steam engine, freed mankind’s dependence on muscle power. It started around 1776 when the population of the world was around 800 million people, according to the (U.S. Census Bureau in 2013. By the year 2000, the population of the Earth was over 6 billion people.

The rate of growth of world gross domestic product, if such a concept is meaningful over such a long period, increased by a factor of 10, from .22 percent to 2.2 percent, according to WorldEconomy.org. Per capita consumption went up by a factor of 20. Brynjolfsson and McAfee argue that the second machine age, ushered in by the computer, will do the same thing for mental power. They date the start of the new age at around 1958, according to the U.S. Census Bureau in 2013.

We start in 1978 when the distribution of income starts to diverge. We do not know the ultimate implications of the second machine age. Machines are rapidly progressing in pattern recognition, which was once a human forte. This suggests to us that there is no clear demarcation limiting the abilities of machines.

Here we assume the ability of machines (Turing robots) to be limited to tasks that can be described by algorithms. However, we are not at all sure what the limits are to such tasks.

We use data from Thomas Piketty’s book (Capital in the 21st Century, 2014) and a relatively simple economic model to calculate robot penetration. We find that the implications of robots replacing humans in routine work require serious attention.

Since 1978, the ratio of labor income of the top 10 percent to the bottom 90 percent has increased from .48 to .89. Extrapolating our results in a linear fashion, this ratio will increase to around 1.3 in 2040, which is an income disparity larger than in any previous era. Substantial redistribution will be required.

On a happy note, the demographic problem of too few young workers to support the elderly will be solved by increasing productivity. This brings up the central question of the paper: What is wrong with “bread and circuses”? Our answer is twofold.

First, as we have already discussed, work and education are necessary to maintain social capital. Second, work and education are necessary for the sorting process required to maintain an elite. Institutions will have to be developed to motivate education and employ people in a fashion that may not be economically necessary.

Education may become an important sector because it employs people and is an excellent sorting mechanism. Immigration issues will become even more important.

There are two immigration issues: one at the bottom end of the labor market, the other at the top. A society with substantial transfers will attract immigrants at the bottom end of the labor market where there is already a labor surplus. At the other extreme, the educational sector attracts students from other countries.

Top talent, so far, has tended to stay in the United States, replenishing our supply of elites. This may be good for the United States, but robs other countries of their sorely needed supply of elites. International trade may be reduced.

Comparative advantage is one of the most powerful concepts that have come from economics, but the logic behind the concept of comparative advantage requires scarcity of factors. As robots replace workers, manufacturing is returning to the United States. This is already happening, and if it continues, it could lead to political instability in countries that depend on exports produced by relatively cheap labor.

Political instability in either China or Mexico would be a serious problem for the United States. Is democracy viable without a large middle class? Francis Fukuyama argues that a substantial middle class is essential to democracy.

Our key assumption is that the workforce is divided into two segments: (1) a segment whose skills are scarce and who perform tasks that cannot be performed by automation and (2) a segment of the population whose skills are not scarce. There is strong evidence that this true.

We realize that predicting the future is always risky, and that there might be high-impact developments that we cannot imagine that somehow change the picture. While we have no idea what such high-impact developments might be, we are confident that work and education are essential to maintaining a healthy society, and this will not change.

Dagobert L. Brito, Ph.D., is a Baker Institute Rice faculty scholar and the Peterkin Professor of Political Economy at Rice University. Robert F. Curl, Ph.D., is a Baker Institute Rice faculty scholar, as well as the Kenneth S. Pitzer-Schlumberger Professor of Natural Sciences Emeritus, university professor emeritus at Rice University and professor of chemistry emeritus at Rice University.

This article first appeared on the Baker Institute for Public Policy website. The full report can be found here. 

From Newsweek @ http://www.newsweek.com/2015/03/13/rise-robot-factories-leading-fourth-industrial-revolution-311497.html and http://www.newsweek.com/rise-turing-robots-leads-fall-wages-309498

For more information about automation see http://nexusilluminati.blogspot.com/search/label/automation
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