Category Archives: Technologies

Did You Hear That? Robots Are Learning The Subtle Sounds Of Mechanical Breakdown

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Fast Company

Expert mechanics can detect what’s wrong with a car without lifting the hood. So can deep learning robots, says startup 3DSignals.

Did You Hear That? Robots Are Learning The Subtle Sounds Of Mechanical Breakdown
[Photo: Flickr user David Hodgson]

Sometimes, machines just don’t sound right.

Brakes squeal, hard drives crunch, air conditioners rattle, and their owners know it’s time for a service call. But some of the most valuable machinery in the world often operates with nobody around to hear the mechanical breakdowns, from the chillers and pumps that drive big-building climate control systems to the massive turbines at hydroelectric power plants.

That’s why a number of startups are working to train computers to pick up on changes in the sounds, vibrations, heat emissions, and other signals that machines give off as they’re working or failing. The hope is that the computers can catch mechanical failures before they happen, saving on repair costs and reducing downtime.

“We’re developing an expert mechanic’s brain that identifies exactly what is happening to a machine by the way that it sounds,” says Amnon Shenfeld, founder and CEO of 3DSignals, a startup based in Kfar Saba, Israel, that is using machine learning to train computers to listen to machinery and diagnose problems at facilities like hydroelectric plants and steel mills

And while most current efforts are currently focused on large-scale machinery, Shenfeld says the same sort of technology might one day help detect failures in home appliances or in devices like self-driving cars or rental vehicles that don’t spend much time in the hands of an owner who’s used to their usual sounds.

“When you have car-as-a-service, the person in the car doesn’t know the car,” he says. “If it sounds strange, you’re losing this link with the human in the car deciding it’s time to take it to the mechanic.”

Initially, 3DSignals’ systems can detect anomalous sounds based on physical modeling of particular types of equipment, notifying an expert mechanic to diagnose the problem. And once the problem is fixed, the mechanic’s diagnosis is added to 3DSignals’ database, which it uses to train its algorithms to not only detect unusual sounds, but also interpret them to understand what kind of repair is needed.

“The next time we hit this signature on the same machine for the same customer or another customer using the same type of machine, it will not just be anomaly detection,” says Shenfeld.

And while 3DSignals focuses entirely on using its machine learning tools to process acoustic data—an area Shenfeld says is surprisingly neglected outside of speech recognition—other companies are using a variety of other types of data to detect and diagnose mechanical problems.

[Photo: Flickr user Sue Clark]

Systems from Augury, a startup with offices in New York and Haifa, Israel, monitor vibrations, temperature ultrasound, and electromagnetic emissions from machinery, typically large-scale heating and cooling systems. The company offers a portable tool a technician can use to capture a reading to an iPhone or Android device, where an app offers a real-time diagnosis as well as a continuous monitoring system, says cofounder and CEO Saar Yoskovitz.

“One of our customers, located in Ohio, had a thunderstorm at 2 a.m., and the whole building went dark,” he says. “The generator was supposed to pick up automatically but it didn’t, and the way that they learned about it was through an alert they got from Augury.”

In more complicated situations, the system uses machine learning-based algorithms and a growing library of signals of different types of errors to suggest what’s wrong and what technicians can do to repair the machine, he says.

“Over time, we’ve collected probably the largest malfunction dictionary in the world for our types of machines,” says Yoskovitz.

For another startup, called Presenso, also based in Haifa, the exact type of data being monitored is less important than how it changes over time and what that signals about the machine’s operation. The company’s systems record data from sensors already installed in industrial equipment as part of existing control processes, streaming readings to Presenso’s cloud servers.

“We don’t care, or we don’t need to know, if the sensor we’re analyzing now measures temperature or voltage or flow,” says CEO and cofounder Eitan Vesely.

Presenso’s software tools use a variety of machine learning techniques to effectively build a model of a machine’s operations based on the sensor data it receives. The tools provide visualizations of unusual readings, and how different they are from normal operations.

“They don’t need any human guidance or [to] know what the physical attributes are that are being measured,” Vesely says. “The goal is for them to learn by themselves how the machine operates.”

And while real-time breakdown detection is obviously useful for companies operating machines in their own facilities, experts say it could also be useful to equipment vendors or insurance companies providing financial coverage in the case of downtime.

“If a company has downtime or business interruption insurance, that’s something where it becomes incredibly relevant and also a money saver,” says Ryan Martin, a senior analyst at ABI Research.

Having more reliable predictions of when machines need maintenance could also spur equipment makers to effectively offer time as a service, charging industrial users either extended warranties or even charging by the hour for the guaranteed use of their equipment without incurring too much risk to themselves, says Augury’s Yoskovitz.

“One of the problems with this business model is they hold all the risk,” he says. “If anything goes wrong, they pay for it, and they’re looking for ways to lower this risk.”

With Mastercard’s Identity Check, your face is the latest form of biometric identification.

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Selfies are no longer just a social media irritant: With Mastercard’s Identity Check, your face is the latest form of biometric identification. In October, Mastercard rolled out the new mobile-payment ID feature, which allows e-commerce customers to verify themselves at checkout simply by capturing an image of their face on their phone. “One of the biggest consumer pain points is when you are prompted for a password,” says Ajay Bhalla, who oversees Mastercard’s security efforts. “That leads to a lot of friction: unhappy consumers, merchants losing sales, banks losing volume.”

Two years in the making, Identity Check works much like the iPhone’s fingerprint ID system. Users set it up in advance by taking a series of photos from several different angles. That info is then turned into an algorithm, which Identity Check can match to a fresh picture at the moment of transaction. “Biometrics is actually safer [than a password] because it is really you,” says Bhalla.

Shoppers weary of passwords might be excited to hear the news, but they should be careful not to get too happy. “The biggest issue we face is that people naturally start smiling when they take a picture,” says Bhalla, “and that messes with the algorithm.”

Mastercard is also exploring other new methods for identifying customers, such as iris scanning, voice recognition, and even heartbeat monitoring. Blood-pumping patterns, it turns out, are as unique as fingerprints. —Nikita Richardson

Milestones: In September, Mastercard launched a program to help developers integrate their services into new technologies such as virtual reality, augmented reality, and the internet of things.

Challenges: Mastercard has been hit with a $19 billion class-action lawsuit over allegations that the company charged illegal fees to U.K. vendors, leading to higher prices for consumers. (“[We] firmly disagree with the basis of this claim and we intend to oppose it,” says a Mastercard spokesperson.)

Mastercard’s new facial recognition tech Identity Check works like the iPhone’s fingerprint system.[Illustration: Laura Breiling]

The age of analytics: Competing in a data-driven world

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McKinsey Global Institute

By Nicolaus Henke, Jacques Bughin, Michael Chui, James Manyika, Tamim Saleh, Bill Wiseman, and Guru Sethupathy

Big data’s potential just keeps growing. Taking full advantage means companies must incorporate analytics into their strategic vision and use it to make better, faster decisions.

Is big data all hype? To the contrary: earlier research may have given only a partial view of the ultimate impact. A new report from the McKinsey Global Institute (MGI), The age of analytics: Competing in a data-driven world, suggests that the range of applications and opportunities has grown and will continue to expand. Given rapid technological advances, the question for companies now is how to integrate new capabilities into their operations and strategies—and position themselves in a world where analytics can upend entire industries.

The age of analytics
The age of analytics
Big data continues to grow; if anything, earlier estimates understated its potential.

A 2011 MGI report highlighted the transformational potential of big data. Five years later, we remain convinced that this potential has not been oversold. In fact, the convergence of several technology trends is accelerating progress. The volume of data continues to double every three years as information pours in from digital platforms, wireless sensors, virtual-reality applications, and billions of mobile phones. Data-storage capacity has increased, while its cost has plummeted. Data scientists now have unprecedented computing power at their disposal, and they are devising algorithms that are ever more sophisticated.

Earlier, we estimated the potential for big data and analytics to create value in five specific domains. Revisiting them today shows uneven progress and a great deal of that value still on the table (exhibit). The greatest advances have occurred in location-based services and in US retail, both areas with competitors that are digital natives. In contrast, manufacturing, the EU public sector, and healthcare have captured less than 30 percent of the potential value we highlighted five years ago. And new opportunities have arisen since 2011, further widening the gap between the leaders and laggards.

Progress in capturing value from data and analytics has been uneven.

Leading companies are using their capabilities not only to improve their core operations but also to launch entirely new business models. The network effects of digital platforms are creating a winner-take-most situation in some markets. The leading firms have remarkably deep analytical talent taking on various problems—and they are actively looking for ways to enter other industries. These companies can take advantage of their scale and data insights to add new business lines, and those expansions are increasingly blurring traditional sector boundaries.

Where digital natives were built for analytics, legacy companies have to do the hard work of overhauling or changing existing systems. Adapting to an era of data-driven decision making is not always a simple proposition. Some companies have invested heavily in technology but have not yet changed their organizations so they can make the most of these investments. Many are struggling to develop the talent, business processes, and organizational muscle to capture real value from analytics.

The first challenge is incorporating data and analytics into a core strategic vision. The next step is developing the right business processes and building capabilities, including both data infrastructure and talent. It is not enough simply to layer powerful technology systems on top of existing business operations. All these aspects of transformation need to come together to realize the full potential of data and analytics. The challenges incumbents face in pulling this off are precisely why much of the value we highlighted in 2011 is still unclaimed.

The urgency for incumbents is growing, since leaders are staking out large advantages, and hesitating increases the risk of being disrupted. Disruption is already happening, and it takes multiple forms. Introducing new types of data sets (“orthogonal data”) can confer a competitive advantage, for instance, while massive integration capabilities can break through organizational silos, enabling new insights and models. Hyperscale digital platforms can match buyers and sellers in real time, transforming inefficient markets. Granular data can be used to personalize products and services—including, most intriguingly, healthcare. New analytical techniques can fuel discovery and innovation. Above all, businesses no longer have to go on gut instinct; they can use data and analytics to make faster decisions and more accurate forecasts supported by a mountain of evidence.

The next generation of tools could unleash even bigger changes. New machine-learning and deep-learning capabilities have an enormous variety of applications that stretch into many sectors of the economy. Systems enabled by machine learning can provide customer service, manage logistics, analyze medical records, or even write news stories.

These technologies could generate productivity gains and an improved quality of life, but they carry the risk of causing job losses and dislocations. Previous MGI research found that 45 percent of work activities could be automated using current technologies; some 80 percent of that is attributable to existing machine-learning capabilities. Breakthroughs in natural-language processing could expand that impact.

Data and analytics are already shaking up multiple industries, and the effects will only become more pronounced as adoption reaches critical mass—and as machines gain unprecedented capabilities to solve problems and understand language. Organizations that can harness these capabilities effectively will be able to create significant value and differentiate themselves, while others will find themselves increasingly at a disadvantage.

From Audi To Zoox, 17 Companies That Are Driving Change In The Car Industry

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Along with GM, these innovators are finding ways to improve the basics of modern transportation.

From Audi To Zoox, 17 Companies That Are Driving Change In The Car Industry
Building a Tesla Model S


Audi: In 2015, started test-driving an AI-laden prototype nicknamed “Jack” that lets drivers easily switch to autonomous mode via buttons on the wheel

BMW: Has promised an entirely autonomous car called iNext by 2021; BMW’s ReachNow car-sharing service launched in April in Seattle and expanded to Portland, Oregon, in September

Ford: Announced plans for fully autonomous car with no pedals or steering wheel by 2021; recently invested $75 million in California laser-sensor company Velodyne; bought San Francisco–based private bus service Chariot and plans to expand it

Volvo: Forged partnerships with Microsoft (will incorporate HoloLens augmented-reality technology into its cars) and Uber (which is planning to use Volvos as part of its self-driving test fleet in Pittsburgh); teamed up with safety-system-maker Autoliv to set up a new company focused on autonomous-driving software


Alphabet: Launched self-piloting-car project back in 2009; testing retrofitted Lexus SUVs and its own adorable prototype vehicles in several locations; recently partnered with Fiat Chrysler to build self-driving minivans

Google’s self-driving prototype.[Photo: Brooks Kraft LLC/Getty Images]

Apple: Has invested $1 billion in Chinese ride-share company Didi Chuxing; reportedly rebooting its efforts to develop an Apple car; might also build a system to add autonomous features to preexisting vehicles

Baidu: Chinese search-engine company has teamed up with digital-graphics pioneer Nvidia to create a self-driving-vehicle system that uses 3-D maps in the cloud; is in the testing stage with several different self-driving-car prototypes, including one built with BMW

Tesla: After revolutionizing electric vehicles with the semi-autonomous Model S, will release more-affordable all-electric Model 3, possibly in late 2017; Model S’s involved in a pair of high-profile fatal accidents


Didi Chuxing: Acquired Uber’s Chinese operations in August, ending a fierce rivalry for Chinese market

Lyft: Partnered with GM to start testing autonomous Chevy Bolt taxis within the next year

Uber: In September, began testing autonomous Ford Fusions in Pittsburgh—the first self-driving fleet available to the public in the U.S.

A fleet of Uber’s autonomous cars.[Photo: Angelo Merendino/Getty Images]

STARTUPS Andreessen Horowitz–backed company making an inexpensive kit that turns regular cars into semi-autonomous ones

Mobileye: Israeli software maker that had partnered with Tesla to provide chips and software, but the two companies ended their collaboration in the wake of a fatal accident in May (Tesla cars currently still use Mobileye chips); has teamed up with Delphi Automotive to build a self-driving system by 2019

NextEV: Shanghai-based electric-car innovator headed in the U.S. by former Cisco exec Padmasree Warrior; set to show off a high-performance all-electric sports-car prototype this year

Nutonomy: Born at MIT and backed by Ford, makes self-driving cars, software, and autonomous robots; started testing driverless taxis in Singapore this summer

Quanergy: Silicon Valley–based company developing light- and object-sensing technology for self-driving cars; boasts $1.59 billion valuation thanks to investors Samsung and Delphi

Zoox: Palo Alto startup behind the Boz, a fully autonomous concept vehicle (still in the design phase) with inward-facing seats similar to a train car; company valued at around $1 billion

A version of this article appeared in the November 2016 issue of Fast Company magazine.

Ways Predictive Analytics Improves Innovation

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From drug discovery to price optimization, across virtually every industry, more companies are using predictive analytics to increase revenue, reduce costs, and modernize the way they do business. Here are some examples.

Disrupt An Industry Drug discovery has been done the same way for decades, if not centuries. Researchers have a hypothesis-driven target, screen that target against chemical compounds, and then iteratively take them through clinical trials. As history has shown, a lot of trial and error is involved, perhaps more than is necessary, particularly in this day and age. According to industry association PhRMA, it takes an average of more than 10 years and $2.6 billion to develop a drug. Pharmaceutical company BERG Health aims to change that. It is using predictive analytics and artificial intelligence (AI) to discover and develop lifesaving treatments. 'There's no way a human can process the amount of data necessary to dissect the complexity of biology and disease into form-based discovery,' said Niven Narain, founder and CEO of BERG. 'We use human tissue samples to learn about as many biological components as we can and we include that patient's clinical and demographic data.' Its platform builds a model of healthy individuals and then compares that to individuals with a disease. The AI then builds a model of the genes and proteins that pinpoints the core differences between health and disease. The model helps BERG target its drug discovery process. The company also uses the same process to identify which patients are the best candidates for a certain drug. Using a single tissue sample, its platform can create more than 14 trillion data points that collectively become a 'patient signature.' The patient signature indicates whether or not the individual will likely respond well to a treatment that, for example, is far more precise than first-line pancreatic cancer treatment. First-line pancreatic treatments fail 90% of the time, Narain said. (Image: bykst via Pixabay)

Disrupt An Industry

Drug discovery has been done the same way for decades, if not centuries. Researchers have a hypothesis-driven target, screen that target against chemical compounds, and then iteratively take them through clinical trials. As history has shown, a lot of trial and error is involved, perhaps more than is necessary, particularly in this day and age. According to industry association PhRMA, it takes an average of more than 10 years and $2.6 billion to develop a drug. Pharmaceutical company BERG Health aims to change that. It is using predictive analytics and artificial intelligence (AI) to discover and develop lifesaving treatments.

“There’s no way a human can process the amount of data necessary to dissect the complexity of biology and disease into form-based discovery,” said Niven Narain, founder and CEO of BERG. “We use human tissue samples to learn about as many biological components as we can and we include that patient’s clinical and demographic data.”

Its platform builds a model of healthy individuals and then compares that to individuals with a disease. The AI then builds a model of the genes and proteins that pinpoints the core differences between health and disease. The model helps BERG target its drug discovery process. The company also uses the same process to identify which patients are the best candidates for a certain drug.

Using a single tissue sample, its platform can create more than 14 trillion data points that collectively become a “patient signature.” The patient signature indicates whether or not the individual will likely respond well to a treatment that, for example, is far more precise than first-line pancreatic cancer treatment. First-line pancreatic treatments fail 90% of the time, Narain said.

(Image: bykst via Pixabay)

Meet Customer Demand

Handmade photo product company PhotoBarn has increased its throughput 500% by creating warehouse software and lean manufacturing processes that are built around predictive analytics. Before its transformation in 2015, the company struggled to balance supply and demand.

About halfway through 2015, the company started using predictive analytics to forecast sales, inventory, and raw materials to anticipate what it would need before and during the holiday season. That and its new lean manufacturing process enabled the company to move five times more product using the same number of people.

“The spikes and volumes in the holiday period are hard to handle. In 2015, we reimagined our supply chain from suppliers to customers,” said PhotoBarn’s business analytics and marketing chief Ryan McClurkin. “We were able to handle the order volumes without hiccups [because] we’re anticipating versus reacting, and it pays huge dividends.”

Right-Size Resources

Predictive analytics has helped Alabama’s Birmingham Zoo more accurately forecast attendance. As a result of that, the company can make more informed staffing and marketing decisions.

“The number of people who attend the zoo affects staffing, marketing and events planning. You could look at historical averages, but we pulled historical data and correlated that with weather data, school calendars, national holidays, [and other] variables to predict how many people would show on a given day,” said Joshua Jones, managing partner at data analytics and data science consulting firm StrategyWise.

The information is displayed on a digital dashboard that provides a much more accurate forecast. Instead of guessing that 10,000 people will come to the park based on historical information alone, Birmingham Zoo can now see it is likely that 7,131 visitors (or whatever the number happens to be) will attend on a particular day.

Create The Perfect Game

Success in the lottery industry is all about finding the right payout levels. Two of the most important factors are the sizes of the prizes and the frequency of payouts, which is why prize values and odds vary significantly in a single game. However, some games are more popular than others.

“Lotteries want to maximize their revenues so they can [contribute to] education and whatever social programs the state wants to support,” said Mather Economics director Arvid Tchivzhel. “We’ve measured responses in tickets purchased due to changes in the payout structure. You can almost build the perfect game based on where you set the payout levels and the frequency.”

Sell More Effectively

Jewelry TV (JTV), like many luxury goods retailers, was hit hard by the recession. The company tried a number of tactics to improve sales that didn’t work as well as hoped, so it eventually embraced predictive analytics.

“A regression model helps you understand what’s impacting your revenue. When you start building a predictive analytics model, it can tell you why what you’ve been doing isn’t working — customers don’t care,” said Ryan McClurkin, former director of strategic analytics at Jewelry TV and currently chief of business analytics and marketing at PhotoBarn. “Predictive analytics can tell you your customers care about this [instead].” That’s the power of predictive analytics. It allows you to see the variables you can innovate around.

Sizzling Steaks May Soon Be Lab-Grown

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Feb. 1, 2016

Several startups are racing to be the first to fill U.S. consumers’ plates with laboratory-developed hamburgers and sausages that taste just as good as the kind from cattle and pigs.

Memphis Meats Inc., a San Francisco company founded by three scientists, aims in three to four years to be the first to sell meat grown from animal cells in steel tanks. Rivals including Mosa Meat and Modern Meadow Inc. also aim to bring such “cultured meat” to market in the next several years.

The competition highlights how these efforts have expanded since the 2013 taste test of a burger grown in a lab through a multiyear, $330,000 project funded by Google Inc. co-founder Sergey Brin and spearheaded by physiologist Mark Post . Reviews of the patty were mixed, but encouraged Mr. Post, who co-founded Netherlands-based Mosa Meat, to press on.

The startups’ lofty goal is to remake modern animal agriculture, which the United Nations estimates consumes one-third of the world’s grains, with about a quarter of all land used for grazing. The companies say that growing meat with cells and bioreactors—similar to fermentors used to brew beer—consumes a fraction of the nutrients, creates far less waste and avoids the need for antibiotics and additives commonly used in meat production.

“The meat industry knows their products aren’t sustainable,” said Memphis Meats Chief Executive Uma Valeti, a cardiologist and medical professor at the University of Minnesota. “We believe that in 20 years, a majority of meat sold in stores will be cultured.”

A meatball from another startup, Memphis Meats. PHOTO: MEMPHIS MEATS

The potential payoff could be enormous—American spent $186 billion on meat and poultry in 2014—and this month, Memphis Meats plans to announce its strategy and about $2 million in funding from venture-capital firms including SOSV LLC and New Crop Capital.

Some in the meat industry are skeptical that consumers, many of whom are demanding “natural” or organic food made without additives or genetically modified ingredients, will embrace meat grown from animal cells. Representatives for major meat suppliers Tyson Foods Inc., Hormel Foods Corp. and Perdue Farms Inc. declined to comment, saying the technology was still too new.

But enthusiasm for new technology to satisfy consumers’ hunger for meat is high among venture-capital firms and Silicon Valley investors. Microsoft Corp. co-founder Bill Gates and Twitter co-founders Biz Stone and Evan Williams have invested in plant-based protein companies Beyond Meat and Impossible Foods Inc.

Memphis Meats grows meat by isolating cow and pig cells that have the capacity to renew themselves, and providing the cells with oxygen and nutrients such as sugars and minerals. These cells develop inside bioreactor tanks into skeletal muscle that can be harvested in between nine and 21 days, Mr. Valeti said.

While the source cells can be collected from animals without slaughtering them, Memphis Meats and others have relied on fetal bovine serum, drawn from unborn calves’ blood, to help start the process. Mr. Valeti said Memphis Meats will be able to replace the serum with a plant-based alternative in the near future, and Mr. Post says he also expects to be able to eliminate its use. Without the serum, there will be no need for antibiotics, according to the researchers.
Mosa Meat, which Mr. Post started with Maastricht University food technician Peter Verstrate, aims to sell cultured ground beef to high-end restaurants and specialty stores in four to five years, and is fielding interest from potential investors, Mr. Post said. Though the method’s efficiency and environmental aspects strike a chord with some consumers, “it will take time and early adopters” to catch on, he said.

Modern Meadow is working on cultured leather, which could be for sale in two to three years, according to Sarah Sclarsic, business director for the Brooklyn, N.Y., company. Meat, she said, “is a longer-term mission for us.”

The meat startups say their main challenge will be scaling up production while keeping costs low enough that cultured meat costs—and tastes—about the same as meat sliced from animals. Currently it costs about $18,000 to produce a pound of Memphis Meats’ ground beef, compared with about $4 a pound in U.S. grocery stores, according to the U.S. Department of Agriculture. Eventually Memphis Meats and Mosa Meat aspire to sell more-complex products like steak, and make meat healthier by growing cells that contain less saturated fat.

Memphis Meats officials say they have had discussions with the U.S. Department of Agriculture and the Food and Drug Administration on how their food will be regulated. The FDA would likely review the cultured meat before the USDA Food Safety & Inspection Service would begin regulating the product and how it is processed, a USDA spokesman said.

Memphis Meats plans to eventually unveil its meat at restaurants and retailers, including several Memphis-area barbecue restaurants that are co-owned by William Clem, a tissue scientist who teamed up with Mr. Valeti and Nick Genovese, a stem cell biologist, to start the company.

Mr. Clem said he has been pitching the cultured meat idea to regular guests of his chain, Baby Jack’s BBQ, some of whom are skeptical and others interested.

‘We’ve got a road map to start small and introduce it to people…’
—William Clem, Baby Jack’s BBQ and Memphis Meats
“This is probably the toughest market you can imagine for something like this. It’s Memphis, Tenn., it’s all about tradition,” Mr. Clem said. “We’ve got a road map to start small and introduce it to people and get some feedback.” Memphis Meats has discussed its product with food service distributors U.S. Foods Inc. and Sysco Corp., he added.

Steve Lieber, global brand head of BurgerFi, a Florida-based chain that serves burgers from grass-fed beef on tables made from recycled milk jugs, said his company would consider using cultured beef for a seasonal special if it tasted as good as BurgerFi’s current meat.

“We do want to be a cutting-edge company in everything we do,” he said. But “right now for millennials, the tendency toward natural is ingrained.”

The Robotics Revolution: The Next Great Leap in Manufacturing

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SEPTEMBER 23, 2015
Boston Consulting Group. By Harold L. Sirkin, Michael Zinser, and Justin Rose
  • By 2025, the share of tasks performed by robots will rise from a global average of around 10 percent to about 25 percent across all manufacturing industries.
  • Wider robotics adoption will boost manufacturing productivity by up to 30 percent.
  • As a result of higher robotics use, average manufacturing labor costs are projected to be 33 percent lower in South Korea and 18 to 25 percent lower in China, Germany, the US, and Japan than they otherwise would have been.

It has been roughly four decades since industrial robots—with mechanical arms that can be programmed to weld, paint, and pick up and place objects with monotonous regularity—first began to transform assembly lines in Europe, Japan, and the U.S. Yet walk the floor of any manufacturer, from metal shops to electronics factories, and you might be surprised by how many tasks are still performed by human hands—even some that could be done by machines. The reasons are simple: economics and capabilities. It is still less expensive to use manual labor than it is to own, operate, and maintain a robotics system, given the tasks that robots can perform. But this is about to change.

The real robotics revolution is ready to begin. Many industries are reaching an inflection point at which, for the first time, an attractive return on investment is possible for replacing manual labor with machines on a wide scale. We project that growth in the global installed base of advanced robotics will accelerate from around 2 to 3 percent annually today to around 10 percent annually during the next decade as companies begin to see the economic benefits of robotics. In some industries, more than 40 percent of manufacturing tasks will be done by robots. This development will power dramatic gains in labor productivity in many industries around the world and lead to shifts in competitiveness among manufacturing economies as fast adopters reap significant gains.

A confluence of forces will power the robotics takeoff. The prices of hardware and enabling software are projected to drop by more than 20 percent over the next decade. At the same time, the performance of robotics systems will improve by around 5 percent each year. As robots become more affordable and easier to program, a greater number of small manufacturers will be able to deploy them and integrate them more deeply into industrial supply chains. Advances in vision sensors, gripping systems, and information technology, meanwhile, are making robots smarter, more highly networked, and immensely more useful for a wider range of applications. All of these trends are occurring at a time when manufacturers in developed and developing nations alike are under mounting pressure to improve productivity in the face of rising labor costs and aging workforces.

To assess the potential impact of the coming robotics revolution on industries and national competitiveness, The Boston Consulting Group conducted an extensive analysis of 21 industries in the world’s 25 leading manufacturing export economies, which account for more than 90 percent of global trade in goods. We analyzed five common robot setups to understand the investment, cost, and performance of each. We examined every task in each of those industries to determine whether it could be replaced or augmented by advanced robotics or whether it would likely remain unchanged. After accounting for differences in labor costs, productivity, and mix by industry in each country, we developed a robust view of more than 2,600 robot-
industry-country combinations and the likely rate of adoption in each.

The Global Impact of the Robotics Takeoff

The following are some of the key findings of this research:

  • Robotics use is reaching the takeoff point in many sectors. The share of tasks that are performed by robots will rise from a global average of around 10 percent across all manufacturing industries today to around 25 percent by 2025. Big improvements in the cost and performance of robotics systems will be the catalysts. In several industries, the cost and capabilities of advanced robots have already launched rapid adoption.
  • Adoption will vary by industry and economy. Among high-cost nations, Canada, Japan, South Korea, the UK, and the U.S. currently are in the vanguard of those deploying robots; Austria, Belgium, France, Italy, and Spain are among the laggards. Some economies, such as Thailand and China, are adopting robots more aggressively than one might expect given their labor costs. Four industrial groupings—computers and electronic products; electrical equipment, appliances, and components; transportation equipment; and machinery—will account for around 75 percent of robotics installations during the next decade.
  • Manufacturing productivity will surge. Wider adoption of robots, in part driven by a newfound accessibility by smaller manufacturers, will boost output per worker up to 30 percent over the medium term. These gains will be in addition to improvement from other productivity-enhancing measures, such as the implementation of lean practices.
  • Savings in labor costs will be substantial. As a result of higher robotics use, the average manufacturing labor costs in 2025—when adjusted for inflation and other costs and productivity-enhancing measures—are expected to be 33 percent lower in South Korea and 18 to 25 percent lower in, for example, China, Germany, the U.S., and Japan than they otherwise would have been.
  • Robots will influence national cost competitiveness. Countries that lead in the adoption of robotics will see their manufacturing cost competitiveness improve when compared with the rest of the world. South Korea, for example, is projected to improve its manufacturing cost competitiveness by 6 percentage points relative to the U.S. by 2025, assuming that all other cost factors remain unchanged. High-cost nations—such as Austria, Brazil, Russia, and Spain—that lag behind will see their relative cost competitiveness erode.
  • Advanced manufacturing skills will be in very high demand. As robots become more widespread, the manufacturing tasks performed by humans will become more complex. The capacity of local workers to master new skills and the availability of programming and automation talent will replace low-cost labor as key drivers of manufacturing competitiveness in more industries. There will be a fundamental shift in the skills that workers will need in order to succeed in advanced-manufacturing plants.

Few manufacturing companies will be left untouched by the new robotics revolution. But getting the timing, cost, and location right will be critical. Investing in expensive robotics systems too early, too late, or in the wrong location could put manufacturers at a serious cost disadvantage against global competitors.

Preparing for the Robotics Revolution

The right time for making the transition to advanced robotics will vary by industry and location. But even if that time is several years away, companies need to prepare now.

To gain competitive advantage, companies need to adopt a holistic approach to the robotics transition. We recommend that companies take the following actions:

  • Understand the global landscape. First, companies need a clear picture of the trends in robot adoption around the world and in their industries. They need to know how the price and performance of robots are likely to change in comparison with the total cost of labor in each economy where they manufacture—and how this comparison is likely to change in the years ahead. They must also factor in other considerations, such as the flexibility of labor rules and the future availability of workers, that support or hinder wider robotics adoption in a given economy. It is important to keep in mind that these are moving targets.
  • Benchmark the competition. Companies need to be well aware of what their competitors are currently doing and understand what they will do in the future. If robotics adoption is expected to rapidly increase in their industry, they should assume that the total cost of systems will fall. This knowledge will help companies more accurately estimate the cost and timing of investments as well as make decisions about where to locate new capacity.
  • Stay technologically current. Rapid advances in technology mean that companies must stay abreast of the evolving capabilities of advanced robotics systems. They should have a clear view of whether and how quickly innovation is resolving technical barriers that so far have inhibited the use of robots, such as the ability to manipulate flexible or oddly shaped materials or to operate safely alongside workers. Just as important, when will these new applications be cost-effective? As they take stock of the new capabilities and the improvements in price and performance, many companies—even small and midsize manufacturers—may discover that installing robotics is more cost-effective than they once thought. In some cases, having a view on the evolution of robotics and automation can help a company determine whether it is better to wait for a better technology to emerge or to implement a new process that allows them to upgrade technology without having to duplicate what they have already done. In many ways, timing is crucial.
  • Prepare the workforce. As more factories convert to robotics, the availability of skilled labor will become a more important factor in the decision about where to locate production. Tasks that still require manual labor will become more complex, and the ability of local workforces to master new skills will become more critical. The availability of programming and automation talent will also grow in importance. Companies and economies must prepare their workforces for the robotics revolution and should work with schools and governments to expand training in such high-compensation professions as mechanical engineering and computer programming.
  • Prepare the organization. Even if the economics don’t yet favor major capital investment, companies should start preparing their global manufacturing operations now for the age of robotics. They should make sure that their networks are flexible enough to realize the benefits of robotics as installations become economically justified in different economies and as suppliers automate. They should get themselves up to speed on new advanced-manufacturing technologies and think about how they will transform their current production processes so that these technologies can achieve their potential. For many manufacturers, adapting to the age of robotics will require a transformation of their operations.

Manufacturers do not have the luxury of waiting to act until the economic conditions for robotics adoption are ripe. Our projections show that when the cost inflection point arrives, robotics installation rates are likely to accelerate rapidly. This will provide the opportunity to create a substantial competitive advantage. Companies and economies that are ready to capitalize on the opportunity will be in a position to seize global advantage in manufacturing.


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in Fast Company

January 11, 2016


On a hot, swampy November afternoon at IMG Academy in Bradenton, Florida, the private school’s boys’ lacrosse team is huddling around the bench draining water bottles after a round of drills. It’s a scene like any other on high school fields around the country . . . except here there’s a small crew of men sitting off to the side with laptops, running diagnostics on the team’s fluid intake, tracking each player individually through smart chip–enabled refillable water bottles.

Each “smart cap” bottle is digitally linked to a specific player. It works with an app to calculate how much he sweats in an average practice, how much sodium he loses, and how much he needs to drink to maintain optimal performance. Each bottle is filled with a drink formula that corresponds to an individual player’s sweat type. A microchip and a small turbine in the spout measure how much he takes with every sip. LED lights on the cap help him pace his drinking, showing whether he’s ahead of or behind his target. This is the future of athletic hydration. It’s also the future of Gatorade.

The brand’s new high-tech focus can be traced back to senior vice president and general manager Brett O’Brien’s decision in 2014 to build an internal innovation unit to look beyond bottle shapes and new flavors and toward a higher mission. After all, something had to be done. Gatorade sales in the first half of 2009 had fallen 18% year over year; new competitors such as VitaminWater, Red Bull, and Monster had gained influence and market share; and the product, born 50 years ago in a University of Florida lab as the original sports specialty drink, had become known more as a hangover helper than a high-performance elixir. O’Brien and the innovation group set about getting Gatorade back into shape, transforming it into an elite sports brand on par with Nike and Under Armour. “It’s not just about capturing a bigger part of a marketplace,” O’Brien says. “It’s about filling a void for your consumer, and ours are athletes.”

Gatorade started by looking at how athletes were already using its products. For instance, pro athletes had long been mixing sodium powder packs and other additives into their Gatorade (the brand even makes its own, called Gatorlytes). Now the company is looking to cut out the extra steps by creating 12 different formulas served in small egg-shaped pods that mix with water—to be used in new bottles featuring the smart-cap design. The formulas will range in carb, calorie, and electrolyte levels to optimize fluid recovery. To know which formula is right for which athlete, the brand has developed a suite of products and technologies that work together to measure and track individuals’ data. A smart scale linked to a tablet and software, for example, catalogs a player’s weight, along with training time and intensity, to make fluid and carb-intake recommendations. A patch, like a near-field communication chip-enabled Band-Aid, will analyze a player’s sweat and communicate with the digital platform to identify his sweat type—which will determine sodium, electrolyte, and additional fluid-intake needs.

All of this technology was developed in conjunction with the brand’s nutrition-minded Gatorade Sports Science Institute, fueled by PepsiCo’s 40% increase in R&D spending between 2011 and 2014. Now Gatorade is testing it on the field with top high school, college, and pro athletes, including the Brazilian national soccer team, the Kansas City Chiefs, the Boston Celtics, FC Barcelona, and the University of Florida. For Kansas City Chiefs wide receiver Jeremy Maclin, the idea of customized hydration is intriguing. “I don’t think there’s any athlete on this earth who wouldn’t take an advantage if proven to work,” he says.

Xavi Cortadellas, Gatorade’s global innovation and design senior director (who joined the brand in 2011 after 11 years at Nike), believes the move into customization and personalization was inevitable, given the rise of data-driven insight in sports in general. “It’s about whether you want to be a premium brand or not,” he says. “If you want to play in the premium space, then you have to be delivering personalization.” As part of Cortadellas’s plan to scale these new ideas, a simple version of the bottle will be available to the public by mid-2016—it can be customized with digitally printed cap graphics including team and player names, numbers, and logos. “Here at IMG, one of the things the kids were really excited about was having the ability to customize the bottle ‘just like NikeID personalized my shoes,’ ” says Cortadellas. “That’s what you want to hear.”

Ultimately, Gatorade wants to move beyond the field. The brand is developing and testing a new line of food products expected to hit shelves over the next several years—protein bars, vegetable-based nitrate boosts, protein-enriched yogurt—that will embody what nutritionists have long been preaching to athletes: that they are athletes all day, even at rest. “In all these spaces where we have no or minimal share, the potential for growth is gigantic,” Cortadellas says.

Not long ago, a Gatorade ad tagline asked, “Is it in you?” If Cortadellas and his colleagues are right, the question will soon become moot. They’ll already know.

The road ahead for 3-D printers

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PWC: Technology Forecast

As 3-D printers become faster, easier to use, handle multiple materials, and print active components or systems, they will find use beyond rapid prototyping.

The technology for 3-D printing, also known as additive manufacturing, has existed in some form since the 1980s. However, the technology has not been capable enough or cost-effective for most end-product or high-volume commercial manufacturing. Expectations are running high that these shortcomings are about to change.

Several technology trends are feeding these expectations. An emerging class of mid-level 3-D printers is starting to offer many highend system features in a desktop form factor at lower price points. Printer speeds are increasing across the product spectrum; at least one high-end system under development could print up to 500 times faster than today’s top machines. And key patents are about to expire, a development likely to hasten the pace of innovation.

In a recent PwC survey of more than 100 industrial manufacturers, two-thirds were already using 3-D printing. (See Figure 1.)

Figure 1: Prototyping has driven the adoption of 3-D printing so far. Future opportunities 3D printers chiefly used for prototyping

Figure 1: Prototyping has driven the adoption of 3-D printing so far. Future opportunities 3D printers chiefly used for prototyping

Most were just experimenting or using it only for rapid prototyping, which has been 3-D printing’s center of gravity for most of its history. Canalys, a market research firm, anticipates changes ahead and predicts the global market for 3-D printers and services will grow from $2.5 billion in 2013 to $16.2 billion in 2018, a CAGR of 45.7 percent.1

Despite these trends, the 3-D printing industry faces challenges. Rapid prototyping will remain important but is not the game-changer that will expand the technology into high-volume use cases. The industry should pivot to printing more fully functional and finished products or components in volumes that greatly outnumber the volumes of prototypes produced. For example, some makers of hearing aids and dental braces have adopted the technology for finished products. In addition, 3-D printing should supplement or supplant products and components manufactured traditionally and create items that can be manufactured in no other way.

Technology for 3-D printing will advance through loosely coordinated development in three areas: printers and printing methods, software to design and print, and materials used in printing.

To evolve their design and manufacturing strategies, many industry sectors are using 3-D printing solutions already in the market. (See Table 1.) Technology for 3-D printing will advance through loosely coordinated development in three areas: printers and printing methods, software to design and print, and materials used in printing. This issue of the PwC Technology Forecast examines each of these areas. This article assesses 3-D printer and printing method trends in performance, the management of multiple materials, and capabilities for producing finished products. Future articles will examine the software and the materials themselves.

Table 1: Emerging uses of 3-D printing in the different industry sectors.

Industry sector Some emerging and near-term future uses of 3-D printing
and industrial
  • Consolidate many components into a single complex part
  • Create production tooling
  • Produce spare parts and components
  • Faster product development cycle with rapid prototyping, form and fit testing
  • Create complex geometry parts not possible with traditional manufacturing • Control density, stiffness, and other material properties of a part; also grade such properties over a part • Create lighter parts
  • Plan surgery using precise anatomical models based on CT scan or MRI
  • Develop custom orthopedic implants and prosthetics
  • Use 3-D printed cadavers for medical training
  • Bioprint live tissues for testing during drug development
  • Create custom toys, jewelry, games, home decorations, and other products
  • Print spare or replacement parts for auto or home repair, for example
  • Create complex geometry and shape not possible with traditional manufacturing
  • Create custom protective gear for better fit and safety
  • Create custom spike plates for soccer shoes based on biomechanical data
  • Create multi-color and multi-material prototypes for product testing

The emerging shape of the 3-D printer industry

In 3-D printing, hundreds or thousands of layers of material are “printed” layer upon layer using various materials, or “inks,”2 most commonly plastic polymers and metals. The many different printing technologies are generally material dependent. (See the sidebar “3-D printing technologies.”) For instance, fused filament fabrication (FFF) is used with plastics, stereolithography with photosensitive polymers, laser sintering with metals, and so on.

3-D printing technologies

The printers must be improved in three areas to seize the opportunities that exist beyond today’s predominant use case of rapid prototyping:

Performance: Improve key performance characteristics, such as speed, resolution, autonomous operation, ease of use, reliability, and repeatability.

Multi-material capability and diversity: Incorporate multiple types of materials, including the ability to mix materials while printing a single object.

Finished products: Provide the ability to print fully functional and active systems that incorporate many modules, such as embedded sensors, batteries, electronics, microelectromechanical systems (MEMS), and others.

Today’s 3-D printers are concentrated at two ends of a spectrum: high cost–high capability and low cost–low capability. (See Figure 2.) High-end printers are generally targeted at enterprises and 3-D printing service bureaus; low-end printers, which are often derivatives of open source RepRap3 printers, are targeted at consumers and hobbyists.

Figure 2: The emerging market for printers is defining a new category that has high capability at lower cost.

Figure 2: The emerging market for printers is defining a new category that has high capability at lower cost

During the past year, a new class of printers in the middle has emerged. These printers from new entrants and established vendors have many of the higher-end capabilities at lower prices. For example, printers from FSL3D and Formlabs deliver higher resolution and smaller size using stereolithography technology and are priced at a few thousand dollars. Printers from MarkForged offer the ability to print using carbon fiber composites in a desktop form factor for less than $5,000. CubeJet from 3D Systems is priced under $5,000, can print in multiple colors, and brings professional features to a lower price point.4

Gartner predicts that 3D printers with the value (capabilities and performance) that is demanded by businesses and other organizations will be available for less than $1,000 by 2016.5 It is fair to expect that printer improvements will accelerate in the next few years, although the degree and nature of these changes will vary considerably across printing technologies and vendors.

Emerging trends in 3-D printer performance

Technology for 3-D printing will advance through loosely coordinated development in three areas: printers and printing methods, software to design and print, and materials used in printing.

While many characteristics define a printer’s performance, the key challenges are speed and ease of use.

Printers can be expected to get faster

Even for simple products, 3-D printing still takes too long—usually hours and sometimes days. Incremental improvements as well as new methods that have the potential for an order of magnitude change will help printers meet the challenge for greater speed. “There are lots of ways to improve speed by using higher-quality components and by optimizing the designs and movement of the lasers,” says Andrew Boggeri, lead engineer at FSL3D, a provider of desktop stereolithography printers. For instance, Form 1+, a stereolithography printer from Formlabs, uses lasers that are four times more powerful to print up to 50 percent faster than the previous generation printer Form 1.6

Most of today’s printers use a single printhead to deposit material. Adding more printheads that print at the same time can increase speed by depositing material faster while incorporating multiple materials or multiple colors of the same material. Multiple heads can also make many copies of the same design in the time it takes to print one. With such innovation, print speed can increase more or less linearly as the number of heads increases. At the hobbyist end, Robox sells a dual nozzle printer that the company says can print three times faster than single nozzle printers.

Speed is especially a challenge when printing larger objects. Large objects require more material to be pushed through the printer nozzle, which generally has a set rate for processing material. A partnership between Oak Ridge National Laboratory and Cincinnati Incorporated, a machine tool manufacturer, is addressing this challenge.7 The organizations are developing a large-scale additive manufacturing system. Their design will combine larger nozzles for faster polymer deposition, high-speed laser cutters that handle work areas in feet rather than inches, and high-speed motors to accelerate the pace at which printer heads are moved into position. The result will be a system capable of printing polymer components as much as 10 times larger, and at speeds 200 to 500 times faster than existing additive machines.

To control the movement of the printer head, 3-D printers use different approaches or architectures. Cartesian printers, which move a printhead in two dimensions on a plane, are the popular configuration today. Deltabot printers, also called Delta robot printers, use parallelograms in the arms like a robot. (See Figure 3.) “The Delta printers are going to basically take over all the Cartesian printers, because they have some significant benefits, one of which is speed,” predicts Joshua Pearce, associate professor at Michigan Technological University (MTU) and an active developer of open source 3-D printers. Delta configuration allows for higher speed, because the printheads are lighter and they use shorter paths from one point to another.

Figure 3: Cartesian and Delta configuration in printers.

Figure 3: Cartesian and Delta configuration in printers

Printers will become more automated and easier to use

“These [hobbyist 3D] printers all need considerably more personal upkeep than people are accustomed to with appliances.” —Prof. Joshua Pearce, Michigan Tech University

Existing 3-D printers perform many tasks autonomously. However, some printers at the hobbyist end require that printheads be cleaned periodically, that beds be properly leveled, and that a human tinker and supervise to minimize errors. “These printers all need considerably more personal upkeep than people are accustomed to with appliances,” Pearce says. The potential to reduce or eliminate this human element is real and will be a key area of innovation over the next few years.

Automating the features that cause many of the common errors and reliability concerns, such as support structure generation, part orientation, and others, will likely advance the ease of use in hobbyist printers. For instance, a print run can be wasted if the build platform is not level. Many printers, such as those from Robox, XYZprinting, and MakerBot, include autoleveling where the printer calibrates itself to the platform. Expected in the future is a feedback system that provides real-time monitoring of the printing process, that detects defects or deviation from the design (as specified in a 3-D model generated by a CAD [computer-aided design] tool), and that allows appropriate intervention. Together, such features will likely improve the reliability and repeatability of the printing process.

Emerging trends in how 3-D printers deal with materials

Most printers work with only one type of material—plastic, metal, ceramic, wood, or a biological material. To create more useful products and expand the market, 3-D printers will need to process multiple material types within a single build cycle. Various factors, mostly related to materials themselves, make this requirement challenging. For example, most processes are built around an ideal material that responds to a narrow range of temperature inputs or light frequency. Using heat or light, printers often liquefy or solidify substances to manipulate the material into specific forms. The characteristics that make this manipulation work exclude many other potential materials—at least at the current level of sophistication.

The pursuit of multi-material capability will favor certain printing methods over others. FFF printing has high potential to accommodate multiple materials without greatly extending the existing technology, because printing heads can be added to handle other polymers. Multihead printers are available from Hyrel 3D, XYZprinting, and MakerBot for less than a few thousand dollars.

“For multi-material printing, inkjetlike technology such as Voxeljet is the present and the future.” —Andrew Boggeri, FSL3D

“For multi-material printing, inkjet-like technology such as Voxeljet is the present and the future,” Boggeri predicts. Methods such as selective laser sintering and others use inkjet technology. This technology can handle multiple materials within a range that can be delivered as a powdered “base,” because it already uses multiple printheads. As a result, parts or assemblies made from different materials can be printed in a single print run. Today this technology is accessible at the high end from Voxeljet, Stratasys, 3D Systems, and others.

Inkjet printing for 2-D printers has been around since the 1970s, but was adopted for 3-D printing only about seven years ago by Objet (now part of Stratasys) in a process the company calls PolyJet. By jetting two or more base materials in varying combinations, this technology allows the creation of new material properties that span from rigid plastic to rubber-like and from opaque to transparent. More recently, the technology also allows the printing of multiple colors. For example, the Stratasys Objet500 Connex3 printer supports multi-material and multi-color 3-D printing. A printed part can have as many as 14 distinct material properties and 10 color palettes.8

Today, multi-material printers work for a single family of materials—polymers, for instance—and are largely used for prototyping so designers can check form, function, fit, and feel. Figure 4 shows multi-material prototypes of a handspring and headphones made by the Connex3 printer.

Figure 4: The prototype handspring in this picture combines soft and hard polymer material of different colors. The prototype set of headphones combines multiple materials in multiple colors.

Figure 4: The prototype handspring in this picture combines soft and hard polymer material of different colors. The prototype set of headphones combines multiple materials in multiple colors.

Advances are still needed to combine different families of materials, such as metals and plastics, in a single print cycle.

Advances are still needed to combine different families of materials, such as metals and plastics, in a single print cycle. Developments on this front are in very early stages in research labs,9 and it will likely be more than five years before products are offered.

Emerging trends in printing complete systems

Farther out is the ability to print complete systems or subsystems. Emerging multimaterial capabilities will help, since most finished products are made from more than one material. However, challenges extend to the ability to embed components such as sensors, electronics, and batteries, so everything can be printed in one build. R&D efforts are under way in a number of areas, including materials, printing methods, and combining additive and traditional methods of manufacturing.

The key materials science challenge is to develop inks that can be the basis for printing different types of products, be they sensors, electronics, or batteries. For example, Xerox PARC is developing inks so circuits, antennas, and RFID tags can be printed and applied directly to a product.10 Similarly, Professor Jennifer A. Lewis at the Harvard School of Engineering and Applied Sciences has developed the basic building block of tiny lithium-ion batteries as inks that can be printed.11

The future of additive manufacturing is not limited to inanimate objects. Lewis’s team has developed bio-inks to make living tissues. The team uses multiple printheads and the customized inks to create complex living tissues, complete with tiny blood vessels.12 Some pharmaceutical companies are already using 3-D printed tissue for testing drugs.

Bio-printing typically uses two inks. One is the biological material and the other is hydrogel that provides the environment where the tissue and cells grow. The breakthrough to add blood vessels was the development of a third ink that has an unusual property: it melts as it cools, not as it warms. This property allowed scientists to print an interconnected network of filaments and then melt them by chilling the material. The liquid is siphoned out to create a network of hollow tubes, or vessels, inside the tissue. Such creations are possible only with 3-D printing, generating new possibilities beyond traditional manufacturing.

Can CIOs be a catalyst for taking advantage of 3-D printing?

The printing of complete systems is not limited to a nano or microscopic scale. Working with Aurora Flight Sciences and Stratasys, Optomec has printed complete airplane wings, including electronics and sensors, for small drones.13 Each wing was printed with a Stratasys FFF printer. The sensors and circuitry were printed directly onto the wing using Optomec’s aerosol jet system. Whereas the inkjet process prints on a flat surface, depositing tiny drops of ink from a printhead less than a millimeter away, the aerosol jet process atomizes the nanoparticlebased print material into tiny droplets and focuses them via a nozzle on a print surface that can be curved or an irregular shape. The print surface can be kept 5 millimeters or more away. This capability enables the printing of electronic features smaller than a hundredth of a millimeter.

Some approaches may combine 3-D printing with other manufacturing methods. For example, iRobot has filed a patent for a fully automated robotic 3-D printer, including multiple manipulators and milling, drilling, and other processes to make final products.14

Pace of innovation suggests high expectations will be met

The 3-D printer market is transforming rapidly. Robust innovation at established vendors and among entrepreneurs and hobbyists is providing a test ground for filling the market with more midrange systems that bring enterprise-class capabilities at much lower prices.

Another key factor that will likely change soon is the control that patent holders have had over specific techniques. When key patents for FFF expired five years ago, the open source community rapidly incorporated the techniques in low-cost printers, triggering improvements in speed, quality, resolution, and ease of use.

Likewise, many laser-sintering patents are set to expire in 2014. “I would expect rapid innovation to occur in 3-D printers that use laser sintering, sort of what happened with the RepRap and FFF method,” Pearce says. Communities such as Metalbot and OpenSLS already have open source efforts to create desktop-based laser-sintering printers. If the pace of innovation is as rapid as it was with FFF printers, then less-expensive desktop metal printers may appear within a few years.

Today’s market for 3-D printers and services is still largely bifurcated—at the low end are limited-function offerings of interest to hobbyists. At the high end are expensive printers that have a limited total available market. The key for market growth is the continuing development of printers in the middle price range to achieve advances in performance, in multi-material capability, and in printing complete systems. PwC expects these continuing developments to open the door to a disruptive expansion in the market.

1 “Canalys, “3D printing market to grow to US$16.2 billion in 2018,” news release, March 31, 2014,

2 The term inks refers to all material in 3-D printing that is either extruded or jetted out of a nozzle. The term is not limited to inkjet-based printing methods.

3 RepRap was one of the first desktop 3-D printers. The RepRap concept applies to any machine that can replicate itself, which the RepRap 3-D printer can do. For more details, see

4 Brian Heater, “The CubeJet promises pro-level 3D printing in a consumer form factor for under $5,000,”Engadget, January 7, 2014,

5 Pete Basiliere, How 3-D Printing Disrupts Business and Creates New Opportunities, Gartner G00249922, April 2014.

6 Signe Brewster, “Formlabs reveals the Form 1+, a faster and more reliable SLA 3D printer” Gigaom, June 10, 2014,

7 Oak Ridge National Laboratory, “ORNL, CINCINNATI partner to develop commercial large-scale additive manufacturing system,” news release, February 17, 2014,–.

8 Stratasys, Objet500 Connex3, How to Maximize Multi-Material and Color Possibilities, 2013.

9 Michael Molitch-Hou, “Metal-Plastic Voxel 3D Printing Pursued by Arizona State University,” 3D Printing Industry, April 2, 2014,

10 “Print me a phone,” The Economist (US), July 28, 2012,

11 Mike Orcutt, “Printing Batteries,” MIT Technology Review, November 25, 2013,

12 The Wyss Institute for Biologically Inspired Engineering at Harvard University, “An essential step toward printing living tissues,” news release, February 19, 2014,

13 “Revolutionary ‘Smart Wing’ Created for UAV Model Demonstrates Groundbreaking Technology,” Optomec, 2006,

14 Cabe Atwell, “iRobot Takes Humans out of 3-D Printing Equation,” Design News, March 13, 2013.