Industrial robots and the new China Syndrome

Last year, China bought 66,000 industrial robots. This might not seem like an extraordinary figure for a country of over a billion people, but when you consider that each of these machines can do the jobs of 15 or more full-time employees, the impact begins to look more significant: it means that 66,000 robots could potentially do the work of one million people.

China purchased one-quarter of all the industrial robots manufactured in 2016. So, using these figures, we could make a reasonable claim that manufacturing jobs alone may already be falling to the machines at a rate of four million a year worldwide. By 2019, 1.4 million new industrial robots will be installed in factories around the world, according to the International Federation of Robotics.

On the face of it, that’s 21 million human jobs being replaced by machines in just two years. But is the reality so simplistic?

One thing is certain: the adoption curve can only get steeper. Industrial robots are no longer dumb, single-purpose machines; they’re becoming programmable platforms that can carry out a range of increasingly complex tasks. In this way specialist human processes that have evolved over years -even centuries, such as farming – can be replicated with an app and an industrial robot.

In China, the human population is falling, even as the machine population grows. China is industrialising fast to further its ability to manufacture goods faster and cheaper than the West, but it is also automating faster than any other economy to retain its cost proposition. Costs are rising as a new urban middle class booms within its borders, just as it did in the UK during the industrial revolution. Robots promise to reverse that process, or so China believes.

At the moment, China is a long way behind countries such as Germany and Sweden in terms of its robot density – the number of robots per 10,000 human workers. Sweden has a robot density of 200 and China’s target is 150. In China’s case, however, that means a strategic goal of up to 650,000 robots, each doing the jobs of 15 or more people. That’s nearly 10 million jobs.

But even this is not the full scale of the challenge to human society – or the business opportunity, depending on your perspective. Thanks to Moore’s Law and similar effects, robots can only get smarter, faster, more efficient, and cheaper, and will eventually begin building and upgrading themselves.

Industrial robots may become 20% cheaper over the next five years and 20-30% more efficient, according to Boston Consulting, although there are already examples of a single platform upgrade reducing an automated task’s duration from minutes to seconds.

Inevitably, China itself is starting to manufacture industrial robots, too, so within a decade these machines may be available at half the cost. By then, each robot may be a fully customisable platform that’s able to do the work of 20 or more people – without falling sick, taking a holiday, or starting a family (yet!).

Human jobs

All of this poses a real challenge to other developing economies that are seeking to create new human jobs and wealth, and lift millions out of rural poverty. What if there are no new human jobs to make – at least in manufacturing? And what happens when the robots start farming?

The rise of the machines, then, can be boiled down to simple maths – if you strip away the potential impacts on human society, as it is modelled today. And this process will happen across a huge range of other industries, too. Robotics, drones, smart IoT devices, and autonomous vehicles are already beginning to transform social and health care, critical infrastructure maintenance, transport, law enforcement, the military, and more.

And, of course, robots can be software, too: back- and front-office applications that automate business functions in sectors as diverse as the media, or financial and legal services, often enhanced by a mix of AI, big data, analytics, and enterprise asset management.

It’s not just blue-collar jobs that are falling to the machines, but also white-collar careers; not to mention post-industrial jobs, such as call centre work. What happens to local employment opportunities if the UK’s one million call centre workers are replaced by chatbots?

Yet despite all the above, robotics, AI, and automation are not the zero sum game that apocalyptic thinkers and tabloid newspapers like to imagine: they also create human jobs and open up opportunities for new types of lean, smart business. As long as organisations focus on deploying the technologies strategically to enhance and complement human society, rather than tactically to sweep workers aside and slash costs.

This ‘threat or promise?’ aspect of robotics was the subject of a packed summit in London on 4 September: Rise of the Machines, hosted by The Crowd – a kind of TED for sustainability specialists. Delegates were a Who’s Who of blue-chip businesses and government organisations, and they heard presentations by robotics, AI, and business transformation specialists before breaking out to debate questions such as: How will robotics and AI impact on sustainable development goals (SDGs)?

Sean Culey, consultant and executive VP of Manucore, gave the keynote, in which he described the traditional concepts of manufacturing, manual labour, and even ownership being swept aside by a wave of “creative destruction”. According to Culey, we’re in the trough before the big wave hits, the disruptive crunch-point before future opportunities fully emerge on the crest of new ideas.

The future it brings will be about ‘PAL’ he said: Culey’s acronym to describe businesses that are personalised, automated, and local, in which a single prototype is as simple to produce as a million finished products, enabled by big data, robotics, the IoT, and 3D printing. He used the example of Carbon, a Silicon Valley startup* that has been developing new lines of shoes for Adidas. Watch this video for more.

Outside the event, Culey set out his vision, summed up as PAL, to diginomica:

Personalized through mass customization and on-demand manufacturing, combined with devices that remember personal preferences and AI systems that predict what you want before you want it.

Automated using autonomous vehicles and smart robotics that pick, pack, and produce goods, while Blockchain technology, robotic process automation, and chatbots handle the administration and customer queries.

Localized due the re-shoring of production to where consumers are located, as the cost advantages of making, storing, and shipping goods locally and in smaller quantities using smart machines and on-demand manufacturing become increasingly pervasive. A micro-logistics network, using warehouse robotics and autonomous delivery methods – from drones to mothership vans that contain delivery robots.

In Culey’s PAL model, goods will increasingly be made on demand, reducing excess production, transportation, storage, and waste. The sustainability benefits alone will be massive, he said:

Currently we ship products halfway round the world, then drive them to large warehouses, store them, then drive them to smaller warehouses or outlets, and finally they’re either picked up by people who drive to that location, or the goods are transported to the consumer.

Meanwhile, factory managers are often measured on line utilization and throughput, with production running all day, making large quantities of product that often finds its way into landfill. The carbon emissions and waste from this global supply chain are enormous.

Additive manufacturing greatly reduces the by-products and waste from the production process, and machines operate for the cost of electricity, the cost of which continues to fall due to increased efficiencies from solar and other renewable sources.

More, the industrial internet will see machines that monitor and optimise their own performance, reducing quality defects and highlighting any excessive energy consumption. Transportation will be greatly optimised through increasingly autonomous electric vehicles, directed by intelligent AI routing systems that direct them to move what’s needed to where it’s needed.

Finally, the rise of digitalization and ‘servitization’ business models will greatly increase the utilization rate of logistics assets, while dramatically reducing the costs.

My take

In Culey’s future, the China described at the beginning of this report seems less like a massive object attracting global business through sheer force of gravity, and more like the apotheosis of an ageing industrial strategy: big manufacturing on a massive scale, with global supply chains, waste, and a relentless focus on cost, rather than diffused, personalized, localized innovation.

That’s an opportunity for other countries to exploit.

But the mistake that many futurists make is imagining that the old world stops when the new one starts; the reality is messy, unpredictable, complex, full of legacy as much as promise – the 19th Century landmarks among the Shards and creative hubs. Yet Culey is a pragmatist, not a futurist: he is outlining a new ideal for the smart, sustainable, industrialised world that’s emerging both on the fringes and in the centre.

The wider challenge is to ensure that we don’t try to remodel human society after the fact. We need to think today about directing this wave of creative destruction towards human societal advantage and sustainable, ethical development, and not just towards narrow, short-term cost-cuts. That demands a buy-side shift of focus, and a change of tack from the analysts and think tanks.

So, how to ensure that robots enhance human society, rather than consign it to the history books (if such things are still being written then)?

A simple idea emerged from one of the breakout groups: why not use the UN’s Sustainable Development Goals to underpin a legal framework to regulate robotics and AI? That was the suggestion of delegate Sherah Beckley, sustainability specialist at Thomson Reuters.

A brilliant idea. Let’s make that happen.

Image credit – Disclosure – Chris Middleton hosted a panel at the Rise of the Machines summit, which will form the basis of a separate exclusive report for diginomica.


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