Industrial Internet Now

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.

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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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From Reactive to Predictive Maintenance – An IoT-enabled Manufacturing Leap

Predictive maintenance is taking the place of calendar-based and reactive maintenance practices. More than a quarter of manufacturers are now tracking product performance through predictive maintenance applications, and the global spend on IoT solutions is expected to increase from $29 billion in 2015 to $70 billion in 2020, Data Scientist and Statistical Modeller Ravishankar Kandallu writes in his blog post for Industrial Internet Consortium.

IoT enables companies to solve problems before customers realize they exist, resulting in reduced downtime and maintenance costs, hence better customer experience. Kandallu reminds us that “[w]ith the IoT emerging as a strong transformational force, it is very quickly becoming indispensable for manufacturing companies to choose the right predictive maintenance model for plant equipment maintenance and customer experience enhancement purposes.”

Read more about how to take your predictive maintenance efforts to new level:

Via Industrial Internet Consortium

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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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