BCG research predicts that by 2025, adoption of advanced robots will boost productivity by up to 30 percent in many industries, and lower total labor costs by 18 percent or more in countries such as South Korea, China, the U.S., Japan, and Germany.

The use of advanced industrial robots is nearing the point of takeoff, a development that could power a new wave of productivity growth in many industries and lead to changes of up to 5 percentage points in the cost competitiveness of major export economies relative to the U.S., according to new research by The Boston Consulting Group (BCG).

The BCG study projects that investment in industrial robots will accelerate markedly over the next decade, from annual growth that now averages 2 to 3 percent to around 10 percent. As a result, the total cost of manufacturing labor in 2025 could be 16 percent lower, on average, in the world’s 25 largest goods-exporting nations than they would be otherwise. Depending on the industry and country, output per worker could rise by an estimated 10 to 30 percent over and above productivity gains that typically come from other measures.

The biggest gains in labor savings will occur in nations that are at the forefront of deploying industrial robots, such as South Korea, China, the U.S., Japan, and Germany. Manufacturing labor costs in 2025, when adjusted for normal inflationary increases and net of other productivity measures, are projected to be 18 to 33 percent lower in these economies when advanced robots are factored in. In China, one of the world’s largest markets for robots, greater use of automation could compensate for a significant part of the loss in cost competitiveness that is expected to result from rapidly rising factory wages and the growing challenge of finding manufacturing workers. Economies where robotics investment is projected to lag—and where low productivity growth is already a problem—are likely to see their manufacturing competitiveness deteriorate further over the next decade. Such nations include France, Italy, Belgium, and Brazil.

“As labor costs rise around the world, it is becoming increasingly critical that manufacturers rapidly take steps to improve their output per worker to stay competitive,” said Harold L. Sirkin, a BCG senior partner and coauthor of the firm’s series on the shifting economics of global manufacturing, introduced in 2011. “Companies are finding that advances in robotics and other manufacturing technologies offer some of the best opportunities to sharply improve productivity.”

The robotics study is part of ongoing research by BCG on the impact of advanced-manufacturing technologies, which also include additive manufacturing (known as 3-D printing), digital manufacturing, and the industrial Internet. In a recent survey of U.S.-based manufacturing executives at companies with sales of least $1 billion, 72 percent of respondents said that their companies will invest in additional automation or other advanced-manufacturing technologies in the next five years.

Although industrial robots have been used in factories for decades, robots currently perform only around 10 percent of manufacturing tasks, on average. By 2025, BCG estimates, the portion of tasks performed by robots will near 25 percent for all manufacturing industries worldwide.

A confluence of forces is likely to power the robotics takeoff in many industries in the near term. One is declining cost. For example, the cost of an advanced robotic spot welder has plunged 27 percent, from an average of $182,000 in 2005 to $133,000 in 2014—and the price is forecast to drop by a further 22 percent by 2025. At the same time, the performance of robotics systems (their speed and flexibility, among other attributes) is likely to continue improving by around 5 percent each year. The combination of price and performance improvements will greatly accelerate the time it takes for robots to become more cost effective than labor in many industries.

Advances in vision sensors, gripping systems, and information technology are driving this improvement in price and performance, making robots smarter, highly networked, and immensely more useful in a wider range of applications.

“For many manufacturers, the biggest reasons for not replacing workers with robots have been pure economics and technical limitations,” said Michael Zinser, a BCG partner who coleads the firm’s Manufacturing practice. “But the price and performance of automation are improving rapidly. Within five to ten years, the business case for robots in most industries will be compelling, even for many small and midsized manufacturers.”

As robots become more affordable and easier to program, smaller manufacturers will use them on a wider scale. This will greatly expand the market for robots and integrate them more deeply into industrial supply chains.

BCG estimates that manufacturers will begin to ramp up investment in robotics when the costs of owning and operating a system reach a 15 percent discount over the cost of employing a worker.

The inflection point for widespread robotics adoption will vary by industry and country, depending on factors such as wages, productivity, labor regulations, and the ease with which tasks can be automated. BCG estimates that manufacturers will begin to ramp up investment in robotics when the costs of owning and operating a system reach a 15 percent discount over the cost of employing a worker. In industries such as automotive manufacturing in the U.S., where it costs around $8 an hour to use a robot for spot welding compared with $25 for a worker, that point has already arrived—and the cost gap will widen considerably in favor of robots. Similarly, in U.S. electronics manufacturing, it costs around $4 an hour to use a UR5 robot for a routine assembly task compared with $24 for an average worker.

Manufacturers in some countries are currently installing robots much faster than in others. China, the U.S., Japan, Germany, and South Korea account for nearly 80 percent of robot purchases, a share that is likely to hold steady for the next decade. The competitive position of South Korea, which is the most aggressive adopter of robots given its labor costs, is expected to improve most dramatically relative to other leading export economies. Productivity-adjusted labor costs in South Korea are projected to be 33 percent lower in 2025, relative to what they would be otherwise, owing to advanced robotics—double the average cost reduction estimated across all 25 leading export nations. In Japan, Canada, the U.S., Taiwan, the UK, and Germany, the savings could amount to at least 20 percent based on expected investment and industry mix.

A number of countries, however, have been relatively slow to adopt robots. Some slow adopters, such as Mexico and India, are unlikely to lose much competitive ground, because their costs are forecast to remain low for the next decade. But there are also a number of high-cost economies that are lagging in robot use, largely because of inflexible labor laws that make it difficult to replace workers through automation. What’s more, several of these economies—such as Belgium, France, Italy, and Brazil—have already seen their competitiveness erode over the past decade relative to other export economies owing to rising costs and low productivity growth.

“It’s ironic that in some countries where economic logic suggests that manufacturers should urgently convert to automation, they have instead been among the slowest to do so,” noted Justin Rose, a BCG partner and coauthor, along with Sirkin and Zinser, of The U.S. Manufacturing Renaissance: How Shifting Global Economics Are Creating an American Comeback (Knowledge@Wharton, 2012). The BCG projections indicate that these economies are likely to see their cost competitiveness decline even further if nothing is done to address this trend.

Adoption will also vary by industry. The transportation equipment, computers and electronics, electrical equipment, and machinery industries are expected to account for around 75 percent of advanced robotics installations through 2025. By that time, robots should be able to handle 40 to 45 percent of manufacturing tasks in these industries. Adoption will be slower in industries such as food products, plastics, fabricated metal, and wood products, where many tasks will remain difficult to automate and wages are relatively low. Thanks to technological advances, however, robots are making greater inroads in these industries as well.

“Regardless of whether it’s time to invest in next-generation robots, manufacturers everywhere should start preparing,” added Sirkin. “They need to understand how costs and automation technologies are changing in their industries and what their competitors are up to. They also need to start training their workforces for new skills. The coming robotics revolution could significantly reshape the global manufacturing landscape.”

A formal BCG report containing the findings will be published later this year on the firm’s publishing portal, www.bcgperspectives.com.

If you liked this article, you may also be interested in:

• Raffaello D’Andrea on the future of robotics
• ShanghAI Lectures: Alan Spreckley “Robotic Automation in Manufacturing”
• € 7M EuRoC competition opens call to ignite EU manufacturing industry
• Jobs by the numbers: Economic reports on the robotics and manufacturing industries
•  Factory-in-a-Day: FP7 invests €7.9M to make robotics affordable for SMEs

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Raffaello D’Andrea’s robots can do amazing things: play soccer and ping-pong, perform complex choreographed acrobatics in the air, build towers brick by brick, and move pallets around warehouses. (See the video at his Web site, and check out his TED Talk.)

He also cofounded Kiva Systems, which was acquired by Amazon.com in May 2012 for $775 million with the hope of using the company’s orange robots to move items around warehouses, get products to consumers faster, and lower costs. Robots have reportedly been deployed in Amazon warehouses around the country.

Massimo Russo, a senior partner and managing director in the Boston office of The Boston Consulting Group and the regional leader of the Technology Advantage practice in North America, recently spoke with D’Andrea about the future of robotics. 

Edited excerpts from their conversation follow.

You’ve developed some amazing robots that play soccer and ping-pong. What novel potential uses for business and society do you see for robots in the future, and where are we liable to see robots next?

In the short term, the biggest impact is going to be from how we make things—how we extract things from the ground (mining, agriculture), how we make things (manufacturing), and how we distribute them. We’re going to see a lot of that innovation find its way into these sorts of realms in the next three to five years.

I still think we’re a ways away from the general-purpose robot in your home that’s going to do your bed, do the dishes, and so on. But I think we will also see in the medium-to-long term an infiltration of automation and robotics in the home.

A lot of your research has been around learning systems and learning robots. Do you see applications of learning algorithms for products such as the driverless car?

One of the biggest advantages of learning and adaptation is that it allows you to create powerful systems that are inexpensive to make. If you try to make a very precise robot, it costs a lot of money. If, however, you relax that requirement to make a machine that right off the bat is high performance, and let it learn and adapt tobecome high performance, it becomes economically viable to deploy such a machine. I think learning and adaptation will have a huge impact on how we make machines.

What has changed over the past few decades, either in terms of the underlying technology or in the economics of robotics, and how do you see robotics changing going forward?

A lot of it is probably best thought of as reaching tipping points. Let’s say you make a robot that could do something and that it costs $1.25 for it to do that, and a person could do the same thing for $1. You’re not going to use that robot. But next year, let’s say it takes 5 cents less to do it, so it becomes $1.20, and then $1.15, and then $1.10. From an outside perspective, you may say there’s no progress being made in this technology. Yet when that tipping point comes, when this robot can do the job for $1 or less, all of a sudden it becomes economically viable and everybody jumps in to adopt it. I think that there has been steady progress in the development of robotics and automation, but I think we’ve just reached various tipping points that will make them, all of a sudden, economically viable to deploy.

What kinds of systems innovations are you seeing? And if you think about this from a company perspective, are there interesting startups or maybe lessons even for the more established robotics players?

One of the neat things that big companies are doing is using robots in a cooperative fashion. A lot of people think that’s been going on for a long time, but actually that’s not true. For example, having multiple robot arms work together to manipulate objects for manufacturing, for installations, and so on—that’s a pretty new development. A lot of the big robotics companies are exploring these ideas—also integrating different types of automation technologies together. I think that’s an exciting development that big companies are mainly undertaking.

With startups, what really gets me excited are the folks who are looking at new ways to think about location and navigation. If we really want robots or autonomous systems to be pervasive, they need to know where they are—not just outdoors but also indoors. A lot of startups are trying to solve this problem by using advances in computer vision—and advances in inexpensive sensors, accelerometers, gyros, magnetometers, you name it—and in the algorithms to fuse all that information together and provide a local awareness for autonomous systems that just doesn’t exist right now.

Your startup, Kiva, was acquired by Amazon. What’s the long-term potential you see for warehouse logistics and management, distribution, and operations going forward?

What’s interesting about Kiva was that in the original business model, the key value that we thought Kiva was bringing to the space was cost. We thought, “Here’s a system that will allow us to reduce the cost of filling orders.” But it turns out that it was many of our secondary value-adds that were really exciting to customers: flexibility, adaptability, and a reduction in human errors. There was the fact that you could set up a warehouse in six weeks instead of two years, and the fact that if you wanted to reconfigure the warehouse, it was easy to do—which you couldn’t do with existing automation. The lesson was that businesses really want flexibility and adaptability.

Do you see applications of Kiva’s technology outside of warehouses, perhaps in more of a manufacturing environment?

Absolutely. We always thought about manufacturing when we were growing the company, but we were really focused on distribution. There was just so much opportunity in distribution that we said, well, manufacturing will come later—maybe we will spin off another company, maybe we will start another division. That still may happen. The underlying ideas can certainly be brought to manufacturing.

There’s been a lot of discussion about big data and companies hiring decision scientists. Are the underlying skill sets very similar, or should companies be thinking about hiring control engineers as well?

The right answer is somewhere in between. You can try to model absolutely everything and never look at data, and try to predict. This is what the Greek philosophers used to do—sit around and try to think of how the world works and forget about observations: “There should be a way to think things through.” That’s one extreme. The other extreme is, “Forget about trying to have any understanding of the physical world, we’re just going to look at data, and the data is going to tell us what causal relationship should take place and how the world works.”

We like to do things in between. We like to have physical models. We like to sit on the shoulders of the giants who have developed all of this understanding about how the world works. But we also like to use real physical data to augment our models, to refine our models, to tell us where our models have deficiencies. The more data you have, the better you can do that. I think there’s a lot to be gained by combining those two approaches. Folks who are only focused on big data—I think that’s limiting. Folks who can only do modeling—I think that’s limiting. You need people who can do modeling and who can work with big data.

Systems are inherently unpredictable and complex when you start connecting them. Do you have any cautionary tales for business leaders about how behaviors could be unpredictable around business systems?

There are many examples of when feedback or interactions with the environments can cause things to go wrong. One example that comes to mind is the Tacoma Narrows Bridge. When this bridge was designed, people didn’t really take into account the interaction of the bridge with a constant flow across the bridge. That caused the bridge to become unstable, and it collapsed. There was Chernobyl, which was basically a system that ran wild and became unstable because of various feedbacks that caused a positive runaway cycle.

Feedback is very powerful, but it’s also very dangerous. You have to be very careful when you start feeding things back without some sort of supervision, because you get unpredictability. It’s a two-edged sword: you want to harness the power, but at the same time you don’t want things to go wrong.

How do you get around that? One, try to understand things before you connect them together and try to predict what could go wrong. You can do that by simply trying to understand the system and trying to reason through it, but also you can make simulation models, agent-based models, you name it. I think that’s the first point. Second, just be cautious. Try things out, and see how it works. For really complicated things, it’s very difficult to prove that something is going to work when interconnected. A lot of times you just have to try it out.