Industrial Internet Now

Millions of things will soon have digital twins

As a concept, digital twins are not new, and their application can be traced back many decades, at least to the early days of space travel. Nevertheless, digital twins are being billed as one of the most strategic technology trends of the moment, with the potential to massively enhance enterprises’ decision making.

This Economist article delves into the far-reaching possibilities that twinning opens up. It also presents recent examples of companies putting digital twins into use and their rationale for doing so. One case is that of Siemens, with its plant in Amberg, Germany, where pairing a virtual version of the facility with the physical one has made a definite impact on the efficiency of day to day operations.

Read the full story here:

For more on the topic, read Matti Kemppainen’s recent take on digital twins here:

Via The Economist

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The new KPIs of efficiency

Companies regularly measure downtime, but what other indices should industrial internet-enabled factories look out for? Dr. Dirk Lange, Technical Director at ARTIS GmbH, talks about the new key performance indicators of manufacturing.

In his opinion, the new KPIs (key performance indicators) of efficiency will include numbers denoting overall equipment effectiveness as well as energy consumption. Lange finds data analysis to be a crucial part in determining these numbers.

“One KPI that I think will continue to be relevant in the future is Overall Equipment Effectiveness, or OEE. This refers to the quality and speed of production. If you have several machines running in a line, performing the same task, the smart factory allows you to compare between their individual performance rates,” says Lange. “Sensors enable the measurement and comparison of productivity between machines, and the resulting data helps to figure out the reason for differences in performance,” he continues.

In addition, the smart factory makes it easier to measure energy consumption and detect opportunities for improvement. According to Lange, energy efficiency is becoming increasingly important to manufacturers. Data from smart machines can help reduce consumption by pointing out which part of the process is eating up energy, for instance.

The right quantity matters as well

In Lange’s view, companies in industries such as steel, pulp and paper, waste-to-energy, ports and shipping, and automotive should focus on measurements that are important to the manufacturing process and that help to detect issues in the quality of work. “Again, the overall equipment effectiveness is very important, but that is only one aspect. Another critical point is that the right quantity is being measured,” Lange says.

“For example, consider the machinery of motor blocks or those in the automotive industry. If you can detect early that something is wrong with the machine—for instance, the surface quality is not like it should be—you can react and stop the machine, which is the easiest solution, or you can adapt the machine to do something else. However, early detection requires having the right sensors measuring the right quantity,” Lange explains.

“Data mining and data analysis will be crucial in the future.”

“It makes no sense to merely integrate sensors in the machine for the sake of doing so. While we have sensors, we must go intelligent and see what kind will really help us. We must use sensors to detect specific conditions such as, for example, damage in spindles and drives, unbalancing, increasing cycle times, decreasing tool life, and worsening quality,” he says.

Measuring predictive maintenance

Lange has been working in his area of expertise for 20 years. According to him, the discussion around the need for predictive maintenance has been going on for about the same time. He shares what has surprised him about the application of measurement to this technique.

“There is not yet a real, reliable solution for predictive maintenance in the machine tools industry,” he reveals. “In high-speed machining, for example, damage is difficult to predict. Because of the high speed of cutting and the variety of tools being used, damage can occur very quickly. But if you look in contrast to aggregates with stable conditions, there are solutions available.”

The role of data analysis tomorrow

Having access to big data also yields new challenges, Lange notes. “To give you an example, in our projects in the automotive industry, process data and condition monitoring data are collected from over a hundred machines. The growing number of sensors results in a vast amount of data, which needs to be stored somewhere. This is a big challenge.”

“Therefore, data mining and data analysis will be crucial in the future. Raw data will have to be collected and compressed into an accessible, intelligible form. To this end, we will have algorithms and strategies which give the user an easy-to-use tool and will, on the other hand, act as the base for a higher grade of automation. This can lead to intelligent factories with increased performance and economy,” Lange concludes.


As Technical Director of ARTIS GmbH, the German tool monitoring company, Dr. Dirk Lange is responsible for product development and application technique.

Interview w/ Dr. Dirk Lange

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Predictive maintenance: the brainpower behind smart factories

In previous decades, there was no reliable way for factory operators to prevent equipment from breaking down unexpectedly and leaving their operations at a standstill. But with the increasing presence of IIOT sensors that monitor and process data at production plants in real time, this uncertainty is becoming a thing of the past.

Preventive maintenance, and especially edge computing, is transforming manufacturing in today’s smart factories. By enabling the close monitoring of equipment, workers can be alerted well before failures happen. Jason Ng, business development director at Adlink, outlines this and some of the other advantages of edge computing, while pointing out a complex hurdle that manufacturers must first overcome before reaping its full benefits.

Read the article here:

Via DigiTimes

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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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