Three steps to drive digital innovation
Innovation is key in navigating businesses through digital transformation. Lina Huertas, Head of Technology Strategy for Digital Manufacturing at Manufacturing Technology Centre, talks about the pinch points where companies in traditional industries require support in their digitalization journey.
What are the steps that traditional manufacturing industries should take to foster a culture of innovation and to avoid extinction? In Huertas’ opinion, the steps are the same for any industry. “The first thing is always to be aware and to raise awareness, for example, by visiting events or conferences or by speaking to organizations that can provide more information. If you are not aware of what’s possible, it’s very difficult to get ideas going,” she says.
“Moreover, someone should always be responsible for achieving digitalization. Businesses always have other pressures to deal with, so it’s very important to have someone in charge of the objective,” Huertas continues. “Once you have a view of the landscape and someone has been tasked with the objective, then you need to identify the opportunity, understanding your whole organization. You should recognize your organization’s opportunities for quick wins and for creating the most value,” she explains. “Quick wins create momentum, so you need to understand that the best place to start is where you have the most issues or concerns.”
Start with the problem, not the technology
“Having established what it is that you want to do, you obviously need to build a business case. At the end of the day, however, what is essential is identifying how value will be created. Without awareness, you can’t articulate what the business of a technology solution is going to be,” says Huertas.
She advises companies to start with the problem, not with the technology. “Starting with technology is never a good thing because digital technology is just an enabler. Nobody should try to digitalize for the sake of digitalizing. It’s just a tool kit that will help you achieve your objectives,” she says.
Instead, companies need to define their objectives and the options for creating value. “Once they have these covered, and a strategy laid out, then they can start thinking about how digital technologies can help them achieve the goals,” she explains. “Again, this is where the awareness is important, because unless you understand a little bit about technology, it’s difficult to link it to your own problems.”
Huertas identifies two key areas that are usually taken into consideration afterwards but that should actually be considered from the beginning. “People and process are important in managing change. First, you need change management to make sure the business is managed correctly. It needs to be taken into account from the beginning. Second, people need to be brought on board. They need to understand what the business benefit is going to be and how the change is going to affect their jobs and responsibilities. That way they become part of the process.”
Build a collaboration ecosystem
According to Huertas, collaboration is crucial. “If you collaborate, you can see best practices. You don’t need to possess all the skills, and you can focus on your core competencies instead of trying to learn everything from scratch. Benefits are generated for all the businesses involved. It’s almost as if you are operating as an ecosystem, an environment that is beneficial for everyone,” she continues.
“Nobody should try to digitalize for the sake of digitalizing. It’s just a tool kit that will help you achieve your objectives.”
“It’s really difficult to achieve digitalization on your own. Therefore, you need to understand what it is that you are going to do internally and who you are going to partner with to help you deliver these solutions in the process of transformation. Consequently, it’s important to establish who is playing which role, where the funding is coming from and how time and skills are managed. It’s necessary to form relationships between all the partners, because ultimately you are all going to deliver the solution together,” Huertas says.
Innovate through strategic partnerships
Huertas identifies the areas where businesses require most support in their digital transformation. These include strategy, collaboration, business change and innovation. “Many organizations are very structured, and having gone through similar transformation processes earlier, they know that having a strategy is important in the long run. However, I think strategy is sometimes overlooked by organizations. A company may be tempted to just start straight away with technology, diving into the deep end, so to say. But there is a danger of starting in the wrong place or creating solutions that will cause fragmentation,” she warns. “Strategy is something a company can get external support for, but there also needs to be someone who understands strategy internally.”
Another area with potential barriers is identifying key partners that can meet the organization’s requirements. “Choosing between one candidate or the other without being a technology specialist is extremely difficult, so support in that area is important. Moreover, external consultancy in terms of business change might be required if the organization doesn’t have a lot of experience in undergoing similar processes,” says Huertas.
When it comes to innovation, she feels it is important to point out that in Europe, there are many research organizations that are partly funded by government. “They have the infrastructure that enables them to take the risk of innovation. Collaborating with those organizations – and basically using their infrastructure – allows companies to better manage risks because not all organizations can afford to test the solutions on their own.”
Huertas concludes with a piece of advice: “In driving digital transformation, the focus should be on understanding the business opportunity and optimizing the process. In a way, technology should come last because there are people dedicated to thinking about technology. Focus on your core competencies and play with your strengths. Once you have the right process, then you can digitalize.”
Lina Huertas is Head of Technology Strategy for Digital Manufacturing at Manufacturing Technology Centre, an independent research and technology organization with the objective of bridging the gap between academia and industry.
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: http://www.gartner.com/smarterwithgartner/gartner-top-10-strategic-technology-trends-for-2018/
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.
— IndIntNow (@IndIntNow) June 15, 2017
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: http://www.e-mj.com/features/6923-connecting-the-connected-mine.html
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: https://jaxenter.com/netbeans/automation-blast-furnaces-tata-steel-netbeans
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.
Machine Learning Will Help Us Fix What’s Broken Before It Breaks
Digital twins, exact virtual replicas physical devices, are computer models operating identically to the physical versions, able to detect problems before they have the chance to happen in the real world. Combined with predictive machine learning, the digital twins are hoped to reduce downtime resolving problems before they even occur.
However, as Big Think reminds us on their article on machine learning, there are still devices in service predating the notion of digital twins, especially in industrial settings. Luckily there are several companies developing bridge technologies that would bring the benefits of digital twins to devices without one. They are harnessing machine learning for analyzing data to pick up subtle variations from normal operation that may predict imminent malfunctions. Their approaches vary from analyzing sounds machines make to detecting changes in machine-produced vibrations.
Read more about how machine learning and AI can keep machines and industrial plants operating at: http://bigthink.com/robby-berman/machine-learning-will-help-us-fix-whats-broken-before-it-breaks
The Internet of Smart Things – humanizing the IOT
David Grebow, CEO of KnowledgeStar and former co-director of the IBM Institute for Advanced Learning, believes that the Internet of Smart Things (IosT) is the most significant opportunity that has come out of the IoT world, especially for manpower-intensive heavy industries. He spoke with Industrial Internet Now about IosT’s potential to humanize the IoT and realize companies’ returns.
What is the Internet of Smart Things and how does it differ from IoT in its implications on work as we know it?
The IoT was originally designed as an interconnected system of computing devices that could transfer data over a network. The original focus was to enable machine-to-machine transfer and display of data. The primary output was the data that informed a few people about how the interconnected devices were functioning. The emphasis was on managing that data, driving new business value from the investment of the infrastructure supporting the IoT, and finding more effective and efficient ways of doing business made possible by the IoT. It was not focused on how people could more safely and effectively use the machines, since there was no human-to-machine interface.
The Internet of Smart Things™ (IosT) incorporates that human-to-machine interface and uses the interconnected computing devices to alert and inform people about what they need to know and do to safely and effectively do their jobs. Imagine if the equipment you use in the workplace could show you what you need to know about how they operate, tell you how to use them correctly and efficiently in your native language, help you be safer working with or around them, offer you details to complete and submit regulatory forms and checklists. What if they could also show you how to fix them if they are broken, provide you with the schematics and diagrams you need, help you contact a mentor or emergency assistance, and more?
“Imagine if the equipment you use in the workplace could show you what you need to know about how they operate, tell you how to use them correctly and efficiently in your native language. What if they could also show you how to fix them if they are broken, provide you with the schematics and diagrams you need, help you contact a mentor or emergency assistance, and more?”
What if all this information was delivered automatically whenever you were within a short distance of the machine? Imagine if it was instantly and securely viewable from any nearby internet-connected device. Think of the enormous impact that could have: increasing safety, eliminating errors, boosting employee productivity, proving timely compliance, among others. It could dramatically reduce injuries and associated worker’s compensation and insurance costs – all of which would have an immediate and positive effect on the bottom line.
We’ve all heard and read about how the Internet of Things in the home will transform the ways in which we live. We’ve heard for years how your refrigerator is going to send a shopping list to your grocery store, your car will make an appointment for an oil change, and the blinds on your windows will automatically close as dusk falls.
What about the Internet of Things in the workplace? It seems to me that far more people have an immediate need for the machines they work with every day on the job to supply them with specific information.
While I can appreciate that having an expensive lathe machine tell me that there is a problem with the calibration of one of the lathes, having that same piece of machinery provide me with safety warnings, a way to access operational information I may have forgotten, a name of a person to call to solve an immediate problem, or a checklist of compliance issues that need to be completed before I operate it would be far more useful. That’s the Internet of Smart Things.
In the shift to a learning economy, what role will managers play, particularly in companies in more manpower-intensive heavy industries like ports and container handling, mining, automotive and general manufacturing? Also, with relation to industrial jobs, in what ways is IosT an opportunity?
Managers who are currently responsible for providing on-the-spot reminders and remedial training would be free to perform more important managerial jobs. Learning becomes the responsibility of the workers who can find out what they need to know and do using their smart devices – phones, tablets, or Google Glass EE – connected to the machines. Managers’ role will be to enable workers to use the IosT.
Managers will also be able to look at the analytics the IosT returns and see where training is hitting or missing the mark, find out who is acting as a go-to expert for operations or repairs, check to make sure regulatory guidelines and maintenance are being met on time, and more. Managers responsible for training will be able to see what parts of the training are working and which areas need to be revisited and revised.
In your writings, you’ve said that the IosT humanizes the IoT? In what way?
It adds people back into the equation. It takes machines that can essentially talk to one another and gives them the capability to literally talk to the workers operating and maintaining them.
You’ve also mentioned that the return on investment is easier to see with the IosT. How so?
According to the 2016 Training Industry Report, the manufacturing sector alone spent more than $25 million on training that year. Current research informs us that we forget as much as 50% of that training in a matter of days or weeks. That means that every dollar spent returns only 50 cents in value. The IosT is an antidote to forgetting since it provides not only just-in-time information; it can be designed to provide just-for-me initialized training as well.
Safety direct and indirect costs from injuries and accidents in the workplace have been estimated by the Occupational Safety and Health Administration, or OSHA – an agency of the United States Department of Labor – to amount to almost $1 billion per week. This ranges from medical payments to repairs of damaged equipment. Smart machines, driven by the IosT, would dramatically cut down these costs by reinforcing safety training and providing safety alerts and instructions. By ensuring that machinery was properly operated and maintained the indirect costs would also be reduced.
What, in your opinion, do responsible developers of technology need to consider in developing IoT systems to make the IosT a reality?
“The value of having a smart machine talking to other smart machines has already proven to be valuable. Incorporating the people who work on those smart machines into the equation makes the IosT even more important.”
The human-machine interface. There is an entire ecosystem that needs to be accounted for. Machine-to-machine data sharing is one element of the ecosystem. Human-to-machine interaction and connection is the other. The value of having a smart machine talking to other smart machines has already proven to be valuable. Incorporating the people who work on those smart machines into the equation makes the IosT even more important. It’s a viewpoint that asks a simple question: How can this technology be used to make life better for the people who work with these interconnected machines every day?
David Grebow heads KnowledgeStar, a US-based consulting firm that provides Fortune 500 corporations, start-ups, NGOs and analyst agencies with insight about the intersection of digital technology and education. His latest book “Minds at Work” will be published in December, 2018 by ATD Press.The Internet of Smart Things™ is trademarked by KnowledgeStar, Inc.
Highlights from the Industry of Things World Report 2017
The Industry of Things World Survey Report 2017 sheds light on the state of the IoT market. Based on the views of over 1,100 cross-industry leaders, the focus point is now shifting towards real-world implementation and monetization of industrial IoT. According to Maria Relaki, Portfolio Director at we.CONECT Global Leaders, the organizer of Industry of Things World conference series, industrial IoT has moved from theory to application.
The third annual Industry of Things World Survey investigates the opinions of over 1,100 Internet of Things (IoT) and Industry 4.0 managers working in industries such as manufacturing, information and communication technologies, automotive and transportation, healthcare, chemicals and many more. Conducted online from January to March 2017, the survey covers the current state of the worldwide IoT market.
“The results indicate that IoT is now considered essential to business and not just theory or something that is good to have. Eighty-eight percent of the respondents found industrial IoT critical to their organization’s future success,” comments Maria Relaki, Portfolio Director at we.CONECT Global Leaders. “A key finding is that Industrial IoT has already become mainstream: According to our respondents, adoption of industrial internet is at 91 percent. This means that companies are moving beyond the theoretical planning phase, and they are able to discuss how they are going to roll out their plans or even what they have already achieved with IIoT,” she explains.
Trends and phenomena
Relaki points out that the increasing percentage of use of digital technology—up from last year’s 82% to 91% this year—supports the notion that digital transformation really is the way to go. According to her, moving to the implementation phase means that there is a lot going on in the world of IoT because this stage has so many steps. In addition to the overarching theme of digital transformation, among the hot topics discovered in this year’s survey results are monetization strategies, data analytics, platforms, and improved operating performance. “People have had enough of ‘This is the next big thing’. Now, businesses are expecting results,” says Relaki.
However, some themes remain equally important from year to year. “Forecasting demand, cybersecurity, and interoperability have not lost importance,” Relaki notes. For example, over half of the respondents (55%) think regulation, governance, and security play a very important role in digital transformation, while almost two thirds (62%) found cybersecurity and privacy a hurdle that must be overcome in pursuing digital transformation. The lack of industry standards for interoperability and interconnectivity was the second most significant hurdle (39%) from the respondents’ perspective.
Challenges and opportunities
“Businesses have identified the opportunities of IIoT technologies, and they are now looking for specific solutions to answer their needs.”
The survey identified some of the challenges and opportunities of industrial IoT. “Based on the responses to our open-ended questions, businesses have identified the opportunities of IIoT technologies, and they are now looking for specific solutions to answer their needs,” she says. For example, the majority of respondents expected IIoT to yield new revenue streams and business models (66%) as well as new products and services (66%). The biggest potential IIoT improvement areas were found in plant operating performance through improved maintenance and asset uptime (58%) or through improved execution (48%).
The challenge lies in recognizing innovation and integrating it into business. For instance, when it comes to digital transformation, only four percent of respondents found their company as having both vision and execution in place.
Industry of Things World 2017
Industry of Things World 2017 is a strategic conference that brings together stakeholders from a variety of industries, all with the aim of defining the future of the fourth industrial revolution. Organized by we.CONECT Global Leaders, the event is scheduled to take place in Berlin, Germany, from September 18 to 19, 2017. According to Relaki, approximately 1,000 participants representing over 40 different nationalities are expected to attend.
Key themes of the two-day program include, among others, overcoming integration challenges of industry 4.0 in running businesses, monetizing the IIoT in an industrial setting, the impact of AI, machine learning, and robotics on productivity, and the implications of the convergence of IT and OT in terms of security. “This year, the emphasis is shifting from ideas and intentions to implementation. There will be presentations of actual projects demonstrating real-world applications of IIoT technologies and sessions dedicated to the integration of innovation in companies,” shares Relaki.
Among the conference’s 80-plus speakers are Kevin Ashton, a renowned expert in digital transformation and the one who coined the term “the Internet of Things;” Nigel Upton, Worldwide Director and General Manager IoT and Global Connectivity Platforms at Hewlett Packard Enterprise; Eric Schaeffer, Senior Managing Director at Accenture; and Tanja Rueckert, President IoT and Digital Supply Chain at SAP.
To find out more about the agenda and speakers of Industry of Things World 2017, visit www.industryofthingsworld.com/en/ .
Download the full survey report here.
Maria Relaki works as Portfolio Director at we.CONECT Global Leaders and is responsible for the Industry of Things World global event series.
Image credit: Industry of Things World
Update: Kevin Ashton, who first coined the term ”Internet of Things” talks about the next phase of the Industrial Internet. Video was filmed at the event venue in Berlin.
— IndIntNow (@IndIntNow) September 22, 2017
Unifying industrial IoT technologies with monitoring
While the majority of industrial companies have multiple systems and technologies managing various components of their operations, only a few have been able to aggregate the data from all of these systems together to drive business prosperity. When brought together, the data from these disparate systems can produce insights across all aspects of the business.
“The teams and upper management of these industrial organisations could – with a unified monitoring tool – see every link in the supply chain and their products’ evolution from early stages right through to delivery,” Paessler AG’s APAC sales director Andrew Timms told IoT Hub. Besides operations-wide oversight, monitoring tools can also be used for the management of particular components of the value chain.
Read more about aggregating data from RFID, SCADA, IT and other sources at: https://www.iothub.com.au/news/unifying-industrial-iot-technologies-with-monitoring-458258