Why China won’t own next-generation manufacturing

Why China won’t own next-generation manufacturing

After three decades of dramatic growth, China’s manufacturing engine has largely stalled. With rising salaries, labor unrest, environmental devastation and intellectual property theft, China is no longer an attractive place for Western companies to move their manufacturing. Technology has also eliminated the labor cost advantage, so companies are looking for ways to bring their high-value manufacturing back to the United States and Europe.

China is well aware that it has lost its advantage, and its leaders want to use the same technologies that have leveled the playing field to give the country a new strategic edge. In May 2015, China launched a 10-year plan, called Made in China 2025, to modernize its factories with advanced manufacturing technologies, such as robotics, 3-D printing and the Industrial Internet. And then, in July 2015, it launched another national plan, called Internet Plus, “to integrate mobile Internet, cloud computing, big data and the Internet of Things with modern manufacturing.”

China has made this a national priority and is making massive investments. Just one province, Guangdong, committed to spending $150 billion to equip its factories with industrial robots and create two centers dedicated to advanced automation. But no matter how much money it spends, China simply can’t win with next-generation manufacturing. It built its dominance in manufacturing by offering massive subsidies, cheap labor and lax regulations. With technologies such as robotics and 3-D printing, it has no edge.

After all, American robots work as hard as Chinese robots. And they also don’t complain or join labor unions. They all consume the same electricity and do exactly what they are told. It doesn’t make economic sense for American industry to ship raw materials and electronics components across the globe to have Chinese robots assemble them into finished goods that are then shipped back. That manufacturing could be done locally for almost the same cost. And with shipping eliminated, what once took weeks could be done in days and we could reduce pollution at the same time.

Most Chinese robots are also not made in China. An analysis by Dieter Ernst of the East-West Center showed that 75 percent of all robots used in China are purchased from foreign firms (some with assembly lines in China), and China remains heavily dependent on the import of core components from Japan. By Ernst’s count, there are 107 Chinese companies producing robots but many have low quality and safety and design standards. He anticipates that fewer than half of them will survive.

The bigger problem for China is its workforce. Even though China is graduating far more than 1 million engineers every year, the quality of their education is so poor that they are not employable in technical professions. This was documented by my research teams at Duke and Harvard. Western companies already have great difficulty in recruiting technical talent in China. This will get worse because advanced manufacturing requires management and communication skills and the ability to operate complex information-based factories. Ernst predicts that the increasing scarcity of specialized skills may be the Achilles’ heel of China’s push into advanced manufacturing and services.

Even if China solves its skills problem, builds its own high-quality industrial robots, and develops innovative industrial processes, it won’t be able to maintain its advantage for long. We could simply import the Chinese robots and copy its industrial innovations. I doubt that even Donald Trump’s immigration walls would keep the foreign robots out.

There is little doubt in my mind that over the next five to 10 years, manufacturing will return, en masse, to the United States. It will once again become a local industry. Yes, it won’t employ the numbers of workers that old-line manufacturing did, but advanced manufacturing will create hundreds of thousands of high-skilled, high-paying jobs. With its massive investments, China is only accelerating the demise of its export-oriented manufacturing industry.

Science

Technology continues to pervade our lives.

Michel Royon/Wikimedia Commons

When will I have my sidekick robot?

Swarm engineer Sabine Hauert of the University of Bristol in the United Kingdom is part of a Royal Society working group asking just that. Hauert, a swarm engineer who works with nanoparticles, has spent time speaking with members of the public about fears and hopes for advancing artificial intelligence. Here on Saturday at the annual meeting of AAAS, which publishes Science, she gave a talk titled, "AI and Policy Engagement: Understanding the Public's Views of Social Risk". Hauert sat down with Science to discuss the issue. This interview has been edited for clarity and length. 

 

A: AI is an abstract concept—different people have different definitions. Very often what people think when they say artificial intelligence is humanlike intelligence. Machine learning is a concrete process that is really the science of computers learning from data. We might be looking at one specific task with one specific set of data and be able to come up with a prediction or a solution based on that. And were using that as a starting point so that we don't get lost in all the discussions about what is AI and what does this technology do. 

 

Q: Where do we see machine learning in our lives?

 

A: There are examples of machine learning all around us. We see it in our spam filters, recommendations online—whether it's movies or with things that we'd like to purchase—and we see it in credit card fraud detection. And there are a number of areas where we are going to see more machine learning in the future.

Swarm engineer Sabine Hauert 

Sabine Hauert

Q: What are the goals of your Royal Society working group?

A: They’re creating a report looking at the potential for machine learning in the next 5 to 10 years, and also the barriers to achieving that potential. They're engaging with a number of stakeholders across the U.K. who'd be interested in this technology whether its industry, policymakers, academia, or the public. And they're trying to look at it from a number of perspectives: ethical, legal, scientific, and societal. 

What I love about the project is that actually a big chunk of this working group’s role is to engage with the public. We surveyed people across the U.K. and asked them what they think of machine learning. We've also had focus groups where we spend more time with small groups of people to dig in and understand what they want from this technology. 

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