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Digital twins, event-thinking and continuous adaptive security are among Gartner’s Top Ten Technology Trends for 2018

Gartner recently released its latest list of the strategic technology trends it predicts will have the greatest potential for impact on enterprises over the next five years. The IT research firm also introduces a concept that ties the ten technologies on this latest list together. This is the “intelligent digital mesh” or the intertwining of people, devices, content and services, which according to David Cearley, Vice President and Gartner Fellow, will be the foundation for future digital business and ecosystems.

The first three strategic trends on Gartner’s list relate to the pervasive spread of AI into virtually every technology, and its potential to enable more dynamic and flexible autonomous systems. The next four concern the merging of the digital and physical worlds to form an immersive, digitally enhanced environment. The final three trends revolve around the increasing interconnections between people, businesses, devices, content and services to deliver digital outcomes.

Explore Gartner’s Top 10 Strategic Technology Trends for 2018 here:

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When investing in AI, start where you see potential profit

Traditional industries such as steel manufacturing are not immune to the transformation emerging technologies is bringing about in the world as we know it. Jane Zavalishina, an Artificial Intelligence expert and the CEO of Yandex Data Factory, shares her thoughts on why steel companies should invest in AI solutions. She also outlines the steps industry players can take to make these efforts prosper.

“I believe that smart technology solutions will bring the most economic benefits to any industrial manufacturing company in the next three to five years,” asserts Zavalishina, when discussing the impetus for industrial companies to fully embrace digitalization. “The capabilities are there, the data is there, and the motivation is there. So, I think it’s a no-brainer that you need to use it.”

She points to a critical question executives and decision makers should be asking themselves before adopting AI technologies.  “How much value can it bring to your particular business?” she says, continuing, “You ought to start where you can quickly see a return on your investment.”

Zavalishina argues that with technologies changing so rapidly and so profoundly, the best place to begin is where potential for easy profit can be found:  “The technologies are universal. Strategically, they can function in a rather disruptive manner, but they can also work just for activation purposes –instead of changing current processes, they can simply improve them, thus making businesses more profitable.”

Focus on revenue, experiment, and get everyone on board

Zavalishina re-emphasizes the importance of focusing on revenue, saying that this is the first step to making a successful investment in AI. She also warns decision makers against the danger of getting overwhelmed by the seeming limitlessness of the opportunities that rapidly developing technologies offer.

“If the innovation you are trying isn’t paying for itself, then focus on something else.”

“Think of a particular business case, about specific products and customers, and then decide where to start,” she advises.

According to Zavalishina, the second step in successful investing is experimentation. “It’s highly beneficial to actually try out new technologies because they are just changing so fast. You can’t build a five-year plan, as no one can forecast what the reality will be in five or ten years’ time,” she argues.

That’s why she recommends trying as many new things as possible, but at the same time, maintaining the costs on a moderate level. “If the innovation you are trying isn’t paying for itself, then focus on something else. There’s no need to spend vast amounts of money on every experiment. Instead, be precise and honest in measuring the results against the costs. If it seems wiser to move on to the next thing, then do so,” she underlines. “But you need to be sure you have understood what these technologies mean for your specific business –  and that’s something you only learn from practice.”

She then describes the third step as being closely linked to the second one: “To succeed in all this, you of course need to have your people on board. And that requires a bigger, more comprehensive change of your entire organizational culture. In order to really embrace change, you must accept continuous experimentation as an inseparable part of your business,” Zavalishina sums up.

Taking the industry to the next level

So, what makes advanced technologies such a driving force, especially for the steel business? Zavalishina says that there are a few prerequisites that enable new smart technologies to be applied efficiently, and that many steel makers already happen to have working in their favor.

One is historical data. In general, steel manufacturers operate the same equipment for decades, which one could say is an un-innovative approach, and might therefore be assumed to be a disadvantage. But on the flip side, this also means that those companies might have accumulated up to ten years of data from their equipment and production processes.

“This is where utilizing AI can really be fruitful, because by analyzing this historical data, AI can learn from it in order to make highly precise operational decisions ,” says Zavalishina.

Another factor is an attitude of experimentation. Here the asset is not the equipment, but the people. “When dealing with the steel industry, you inevitably deal with data-driven individuals coming from engineering backgrounds. Testing and measuring usually comes naturally to them, and they understand the importance of comparing different methods. It’s much harder to convince a banker, for example, about the benefits of spending time experimenting, even if it’s a necessary part of the process.”

Finally, Zavalishina points out the advantage of the industry’s long history: “The industry’s processes are pretty stable, and there haven’t really been any fundamental changes to them in the past decades. You can almost say that the industry has explored practically all the ways to optimize their processes with the current tools – and that motivates them to employ new technologies like AI to achieve the next level.”

When resources are limited

What about the companies with limited financial resources? How can smaller players navigate the world of AI and succeed against the competition?

“Well, if you are a giant, industry-leading player, you actually have less choice. You simply must invest in R&D and try as many things as possible because it’s the only way to maintain your top position,” says Zavalishina. “On the other hand, as a smaller company, you don’t want to stay in the background forever either. In this case, a step-by-step approach is the smartest route.”

To conclude, Zavalishina returns to her advice on the importance of profit. “Don’t invest much. Make sure that every step you take helps your business generate returns. Give a specific experiment three to six months, and if it doesn’t deliver any measurable value in that time, then move on to the next thing.”

“And yes, some of your experiments will fail. That’s what innovation is about, and that’s just fine. But if you stick to this paradigm where you keep going, then you’re very likely to succeed at some point. You might spend 50,000 dollars and lose all of it. Then again, you could spend another 50,000 and win half a million,” she ventures.

Jane Zavalishina is the CEO of Yandex Data Factory, an industrial AI company belonging to Yandex, one of Europe’s largest internet companies. She was recently named in Silicon Republic’s Top 40 Women in Tech as an Inspiring Leader.

Interview w/ Jane Zavalishina

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Connecting the Connected Mine

Mining companies today are looking for ways to benefit from greater data access, real-time analytics, autonomous systems and services such as remote monitoring. In order to do that, they are going to need a network infrastructure that will tie all of those technologies and capabilities together.

The challenges are unique: mining operations can span hundreds of miles above and below ground, and are usually set in far-off areas with minimal or no communications infrastructure. Douglas Bellin and Paul McRoberts propose in their article in Engineering and Mining Journal that “[t]he first step for mining companies is to converge their information technology (IT) and operations technology (OT) systems into a single, unified network infrastructure. This eliminates silos of information and, as result, enables seamless information sharing across an entire mining operation.”

Read more about how wireless communications can help improve efficiencies, enhance safety and reduce costs:

Via Engineering and Mining Journal

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Automation of Blast Furnaces at Tata Steel with NetBeans

JAXenter reports that the Automation Division of Tata Steel Ltd has developed a Level2 system Blast Furnace and implemented a H–Blast Furnace at Tata Steel Jamshedpur.

Blast Furnace Level2 system is a collection of mathematical & mass-energy balance models which, based on first principles, mathematical equations and numerical methods, simulate the blast furnace process in segments on real time basis. The models extract plant data like flow, temperature, pressure, distance, velocity etc from the field devices and convert them into trends using fundamental principles of physical laws. The Level2 system helps operators to visualize the process of the blast furnace and in turn assists them in operation with better control facilities.

Read more about Blast Furnace Level2 system at:

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Digital twins – a new standard in industrial production

The digital twin is a burning topic within manufacturing industries. While it is often included in lists of today’s most strategic technologies, it has yet to be widely adopted in practice. Matti Kemppainen, Director of Research and Innovation at Konecranes, discusses the implications for manufacturers of the rolling out of digital twins. According to Kemppainen, digital twins are set to be a new standard for industry.

A digital twin refers to a virtual representation or model of a physical entity or system, or even an entire factory. The real world and the digital world are brought together via sensors attached to the physical asset, generating real-time data, which is analyzed in the cloud and presented to users in a way that helps them to better understand it and to make decisions based on data.

The uses of a digital twin include analysis, simulation and control of real-world conditions as well as potential changes and improvements in the manufacturing process. Matti Kemppainen, Director of Research and Innovation at Konecranes, recognizes a strong hype around digital twins. According to him, however, there are not yet many functioning examples of them.

Kemppainen’s unit is working towards discovering the best way to create a digital twin of a new product. “The creation of a digital twin ought to start from the very beginning of the chain, and therefore it should cover the design phase of the new product. The digital twin’s heart starts to beat when the completed product is equipped with sensors and connected to the digital world. Traditionally, the design or model of a product is ‘dead’ in the sense that after the product is built and completed, the model remains as it is. In contrast, the digital twin ‘lives’ with the product throughout the product’s lifespan,” Kemppainen explains.

Multiple benefits for businesses

The business benefits of digital twins are clear: The digital twin grants control over the whole production chain, which increases productivity. Maintenance and interruptions can be predicted more accurately, and it is possible to experiment with simulation. “Simulation allows for planning improvements in the process, such as the replacement of components, without interrupting manufacturing, and enables the preparing of alternative plans in case of malfunctions or disturbances,” says Kemppainen. Moreover, safety is improved when processes are simulated continuously. “The device and the products are under continuous control, and there should be no more surprises,” he says.

Operator training is one use case of the digital twin. Kemppainen gives an example: “A crane operator can wear augment reality (AR) glasses and operate a digital version of the crane that behaves exactly like the real crane. Moreover, with AR glasses, machinery can be virtually disassembled into its components in front of the trainee’s eyes. It then becomes easier for a learner to understand how it functions than by looking at the real unit, the insides of which are normally covered by a hood when the machine is up and running.”

“A digital representation of a physical asset is particularly useful in conditions where they are difficult to reach, for instance in wind parks or in ships sailing in the middle of the sea.”

The combination of a digital twin and augmented reality has another advantage. “A digital representation of a physical asset is particularly useful in conditions where they are difficult to reach, for instance in wind parks or in ships sailing in the middle of the sea. It may not be efficient to have an expert technician onboard all the time. With a digital twin and AR glasses, technicians can solve occurring problems remotely,” Kemppainen explains. “In such environments, well-executed digital twins help to predict maintenance, and building them is worth the cost,” Kemppainen states.

Making the most out of a digital twin

In terms of individual products, data gathered throughout the lifespan of a product is useful, but in Kemppainen’s view, comparable data is what creates the most value. According to Kemppainen, the most benefit can be gained when there are digital twins of an entire series of products. “Data from multiple sets of twins can be compared to one another to find out whether a problem occurs frequently in products that are used in similar conditions. Hundreds, even thousands of variables can be compared to find clusters of products that are used similarly and that are in different stages of their lifespan,” he says.

“Devices connected to AI can order maintenance independently, based on observations of the device’s performance. However, sometimes comparison against data on other devices’ performance reveals that there is in fact no need to do anything, because the performance observed is normal under prevailing conditions. When there is a reference list comprising a million devices and all their parameters, it is possible to find a parallel that helps to predict use or assess condition,” says Kemppainen. He illustrates: “For instance, if there is a reference list of hundreds of thousands of cranes at hand containing all data on each individual crane throughout its lifespan, it is possible to match and compare the performance of a group or batch of cranes and find a pattern in how the environment and surrounding conditions impact performance. Consequently, an individual crane’s maintenance and use can be predicted more realistically. Without real use data, all we have are estimates.”

The challenge of getting started

From Kemppainen’s perspective, the reality is that there is still plenty of work to do in order to keep a set of digital twins in good condition throughout the product’s lifespan. Obviously, setting up a digital twin requires a heavy IT system. As the lifespan of industrial products can range from 30 to 40 years, the price tag of a digital twin may turn out to be sizeable. Products and components are repaired and replaced, IT systems are updated, and converting data to new formats is not without cost. Human interference also causes trouble: “Mechanical devices such as hoists cannot be covered entirely with sensors, so if a digital twin of a hoist is in use, the system is going to require manual updates whenever maintenance or other changes take place. Humans are not as accurate as computers, and therefore manual updating always entails a risk of error,” notes Kemppainen.

Accordingly, many companies speculate whether they will need all the sensors that a digital twin would require. “Investing in a digital twin may feel pointless if other components in the system are incompatible. It is easy to end up in a chicken-or-egg situation, where it is difficult to decide when to kick off the digitalization of processes,” Kemppainen says. Therefore, he would rather emphasize the gains of digital twins in new products, systems and facilities. “In an old factory, it is not too realistic to expect everything to be digitalized, especially if there are components of different ages included. But in the future, when a new factory is built, basically all of it will be represented digitally. This can constitute a technological leap that makes the difference and really sets the factory in the position to beat the older competitors.”

The biggest advantages from digital twins are currently seen in critical processes and in very limited contexts, such as aircraft turbines. Kemppainen, however, maintains that manufacturers in all industries should keep a close eye on new developments and get ready to make the leap into the digital world at the right moment. “We should bear in mind that even smaller scale digitalization benefits companies. It’s a matter of getting started and moving forward area by area. Soon it will be standard procedure that a digital twin is included in all new acquisitions, as manuals currently are.”

Matti Kemppainen works as Director of Research and Innovation at Konecranes.

Interview w/ Matti Kemppainen

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