Industrial Internet Now

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/

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.

With hundreds of choices, how can you pick an IoT platform?

Choosing the right IoT platform among the small industry specific platforms isn’t easy. The market is saturated and there’s no market leader creating one industry standard.

ReadWrite interviews Saverio Romeo, the chief research officer of Beecham Research, who were part of a team creating IoT Pilot, a free, completely independent, analyst-driven tool designed to help enterprises navigate and evaluate the IoT platform landscape. Romeo presumes that data privacy and data ownership need to be discussed industry-wide. “The experience of explaining design innovation to engineers has been quite extraordinary over the last 10 years. I think the next step is to make them aware of how the stuff they do has a social impact. We see some of this emerging in the Horizon 2020 research program. There are a number of initiatives in which your organization can go basically and test the device from an ethical point of view, and so I think there is a move towards that.” Romero concludes.

Read more about the IoT platform ecosphere and its future: https://readwrite.com/2017/07/21/with-over-450-iot-platforms-which-one-will-you-choose-il1/

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.

 

7 amazing technologies we’ll see by 2030

The World Economic forum surveyed over 800 experts and executives to find out what the future will actually look like. This Business Insider video shows 7 amazing technologies they think the world will see by 2030.

Watch the video about technology tipping points we will reach by 2030 at: http://www.businessinsider.com/technologies-future-2030-world-economic-forum-tech-video-2017-2?r=US&IR=T&IR=T

How Industrial IoT enables the factory of the future

Trillion-dollar projections on the expanding size of the market are urging companies to capitalize on the Industrial IoT. For many, however, it remains unclear how industries should apply IIoT to begin making the hyper-efficient and agile factory of the future a reality. Fabio Bottacci, founder and CEO of VINCI Digital and Industrial IoT Expert Contributor at the World Economic Forum and at the Brazilian Development Bank (BNDES), shares his insights on how Industrial IoT is already increasing operational efficiency, saving time and reducing cost.

As the Fourth Industrial Revolution transforms manufacturing and material handling, enterprises continue to look for ways to create value from converging technologies. But what are the steps that companies need to take to put together an effective agenda of action? Fabio Bottacci finds it essential that the implementation of industrial internet is incorporated into the company’s strategy and business development. In other words, chief executives must embrace change. “In order to advance decision-making on the correct level, CEOs must be included from the very beginning, possibly as initiative main sponsor. IT officers alone cannot drive real digital transformation,” says Bottacci.

Bottacci advises manufacturers to initiate the transformation by defining a specific set of goals, to be assessed and validated initially on a pilot project, before the implementation at scale of an end-to-end Industrial IoT solution. The next step is to deploy an industrial internet pilot in one facility, or on a specific production line, which will be used as a case study for learning how IoT works in this particular industrial environment. The pilot facility is then reworked and developed according to observations. After the test phase, it is easy for a company to apply the same principles, with proper adjustments, at scale to other facilities.

Bottacci uses the concept of flexible infrastructure to refer to how transformation can be simpler in certain contexts. “It is easier to justify large investments in industrial internet in environments where industrial internet is incorporated into production by transitioning directly to automated, advanced IIoT environments. The transition phase is less complicated when the existing infrastructure is light, because there are fewer things that must be accounted for in applying new solutions,” he explains.

A case in point is Romania, where the internet infrastructure is now top of the class in Europe. The Romanian infrastructure was created rather recently compared to more affluent European countries, and therefore, the entire web is more modern than that in Finland, for example.

Industrial internet in practice

“IIoT coupled with AI or ML turns maintenance into a dynamic, rapid and automated task.”

Bottacci emphasizes that applications of industrial IoT are already a reality. According to him, there are dozens of different use cases of IIoT in enterprises. “Companies are already developing IoT applications that work, and they have started making a difference. For example, transportation and warehousing benefit from automated vehicles and asset tracking. In manufacturing, predictive maintenance and asset performance management are key areas where industrial internet boosts value creation.”

Predictive maintenance keeps assets up and running, decreasing operational costs and saving companies millions of dollars. Data from IIoT-enabled systems – sensors, cameras, and data analytics enabled by powerful artificial intelligence (AI) or machine learning (ML) algorithms – helps to better plan maintenance, allowing manufacturers to service equipment before problems occur. “Data streaming from sensors and devices can be used to quickly assess current conditions, recognize warning signs, deliver alerts and automatically trigger appropriate maintenance processes. IIoT coupled with AI or ML thus turns maintenance into a dynamic, rapid and automated task,” Bottacci explains.

“Other potential advantages include increased equipment lifetime, increased plant safety and fewer accidents with negative impact on environment,” he adds.

The importance of edge analytics

“Companies have been proactive in moving the processing of IIoT to cloud services,” Bottacci notes. However, in his opinion, it is not necessarily a wise move to have everything in the cloud. During critical stages of the manufacturing process it is crucial that decisions can be made instantaneously. Here, manufacturers can benefit from edge analytics.

“Edge computing enables real-time analytics. Edge analytics is an approach to data collection and analysis where automated analytical computation is performed on data at a sensor, network switch or other device instead of waiting for the data to be sent back to a centralized data store. IIoT can be supplemented with Arduino-based, open-source computer hardware and software applications that allow some of the processing to take place on site, at the edge of the network and near the source of the data. Edge computing helps ensure that the right processing takes place at the right time, in the right place,” Bottacci explains. “Edge computing is a preferable option for the cloud in terms of security, as proprietary data is kept within the company firewall. Moreover, edge computing becomes vital when you need real-time analysis and automated action to save critical-mission production lines or facilities from potential heavy damages.”

Creating value with Industrial IoT

“There’s no value in the data without advanced algorithms of machine learning.”

Bottacci says that value can be created in surprisingly simple ways by putting data to work. As an example of enhancing safety and efficiency in material handling, he refers to a fleet management system in Silicon Valley. “Peloton Tech’s truck platooning system is a case study that illustrates how IIoT is already creating value. The system uses vehicle-to-vehicle communication to connect the braking and acceleration between two trucks. The lead truck controls the simultaneous acceleration and braking of the whole fleet, reacting faster than a human or even a sensor system could. What follows is a reduction in aerodynamic drag, which leads to companies saving around seven per cent in fuel cost. In terms of annual savings, this is a remarkable number,” says Bottacci.

In Europe, trucking companies such as Scania and Volvo Trucks have adopted IIoT fleet thinking. “It still takes courage to adopt innovations like these,” Bottacci admits. However, he recommends getting started quickly by building a case study of industrial internet and then working towards expanding IIoT to cover more and more of the industrial realm. “Companies should start seeing emerging technology like Industrial IoT not as a threat but as the only way to survive in a matter of a few years. That’s two or three years if you are an optimist, five to ten if you are more conservative,” estimates Bottacci.

In Bottacci’s view, the simple capacity of devices to seize data is not what the Industrial Internet of Things is essentially about. “Even if you have all the infrastructure and the technology to get the data – sensors, WiFi, the gateway, the cloud – and the capacity of analyzing the data, there’s no value in it without AI, more specifically advanced algorithms of machine learning.”

“IIoT is about AI or ML analyzing data in real time so as to make decisions and act, most of the times several days or even weeks before a potential issue. This process results in actual business outcomes,” Bottacci states. “Prescriptive analytics react autonomously, real-time: In a mission-critical situation, a prescriptive system will autonomously decide what to do. This is where edge analytics is imperative,” he explains. “My point is: You can’t consider industrial internet standalone. The real value comes from how companies use AI and ML-enabled IIoT solutions in analyzing and processing data.”

Fabio Bottacci works as an independent advisor. He is founder and CEO of VINCI Digital and an Industrial IoT Expert Contributor at the World Economic Forum and at BNDES, the Brazilian Development Bank.

The need to connect legacy devices to the IIoT

Pre-internet assets lack the connectivity of newer pieces of equipment. These legacy devices, however, still have years of remaining value if they can be linked to the cloud, enabling their data to be analyzed and revealing actionable insights that could perhaps potentially transform business. Wael Elrifai, Sr. Director of Enterprise Solutions at Pentaho, offers insights on how older systems can be made to work with current ones, and talks about the human side of machine learning.

Businesses that have operated for a considerable amount of time will have accumulated several legacy systems over that period. While they have long life-spans, few of these machines will be immediately compatible with one another. The cost of replacing these pre-internet assets to facilitate communication could easily outweigh foreseeable production benefits. What steps must plant managers then take to combine AI capabilities with legacy infrastructure?

Wael Elrifai, Sr. Director of Enterprise Solutions at Pentaho, begins with this premise. “I usually like to remind people that we talk as though data was not generated in the past on these systems. Remember, however, that there’s a lot of robotics involved already, and these systems have sensors that have been producing data for decades. The truth of the matter is that systems such as PLCs (programmable logic controllers) and SCADA (Supervisory Control and Data Acquisition) have already been capturing that data. What you need to do now is to pull the data off those systems. Things like data integration tools are built for that,” he says.

According to Elrifai, predictive maintenance – a technology that drives value in modern manufacturing – isn’t new either. “The difference today is, because the cost of computing and the cost of storage have dramatically reduced, you can do more with it. It’s been a nice positive feedback cycle: Where you can capture more data, you can do more computing work – applied mathematics, machine learning and AI, among others. This then makes capturing data more valuable.”

Conversely, in situations where it’s entirely mechanical and no data or robotics are involved, he suggests looking for proxies for that data. Elrifai adds that while some retrofitting may be required, from his experience a lot of data is already there and is not being used, so it would be best to begin with that.

IIoT implementation across industries

Some industries are more advanced when it comes to IIoT implementation, while others lag. To convince traditional manufacturing companies of the economic benefits of AI investments, Elrifai offers the following examples.  

“The ports industry is already using complex machine learning techniques. The most common one for logistics companies is simulated annealing, a method for schedule optimization that sees to it that cranes are doing the right thing at the right time, and containers are moved according to the right schedule.” Elrifai believes that for some container terminals, it’s mostly about the integration of the larger supply chain.

On the other hand, he recalls a visit to a steel factory that wanted to improve its efficiency. “A couple of times a day, they experienced a very specific kind of failure. This cost about 10 percent of their productivity, and in the steel industry that figure is enormous,” Elrifai explains. Furthermore, the way they knew there was a problem was rather unusual: The control room would start shaking.

While the company wanted to reduce this through predictive maintenance techniques, what they didn’t know was that they were capturing all this data already. Elrifai says like many other companies, this particular steel factory would only look at five, ten or 20 variables, the ones that were in their SCADA system.

“What they didn’t do is integrate this with thousands of other sources. The statistical techniques that factories are doing today are low-dimensionality ones,” he continues, adding that trying to convince groups to do more is a matter of explaining to them that it’s just an evolution of what they are already doing.

Where humans fit in

Another dimension to machine learning and AI is the human factor. As far as the supply chain is concerned, Elrifai is of this opinion. “If you are talking about supervised learning – just prediction, basically – oftentimes the baseline data that you use to train these systems is from humans. And you want these systems to evolve, because systems evolve, factories evolve,” he says. “I think humans are always going to be there, helping to state what the ground truth is. Or, at least for the foreseeable future, they will be doing that.”

In addition, Elrifai points out that in certain cases in factories, several different algorithms are voting whether something is going to fail or not, and a human expert is doing that as well.

“With these kinds of methods – ensembles, if you will – you end up with better outcomes. For instance, the machine by itself might produce 75% accuracy and the human on his or her own might produce 68% accuracy.  When you put them together, you end up with greater performance, say 80% or 85%,” he states. “I think there’s still a lot of room for cooperation. I don’t think the algorithms are taking over just yet.”

How to solve new problems

“The common problem people have with technology is that they search for problems. That makes no sense. Solution? Start with use cases.”

Finally, Elrifai – with his background in data science – offers this essential piece of advice to companies that plan to connect legacy equipment to the IIoT. “The common problem people have with technology is that they search for problems. That makes no sense,” he emphasizes. His solution? Start with use cases.

“I think there’s a sense that this is extremely expensive to do. However, all you really are doing is putting up a basic data engineering or basic machine learning infrastructure – this is low-cost. There’s a lot of automation available now around machine learning,” states Elrifai. “In the data world, when you try to build models, about 80-90% of the effort that is put in is made up of data engineering, feature engineering, preparing data, filtering – all the easy stuff.”

Elrifai believes that a lot of the data prep for data engineering can be done in an automated fashion. “I don’t think people recognize that. They are trying to use old tools to solve new problems,” he concludes.

Wael Elrifai is an author and speaker. He works as Sr. Director of Enterprise Solutions at Pentaho, a data integration and business analytics company with an enterprise-class, open source-based platform for diverse big data deployments.