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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.

Interview w/ Wael Elrifai

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Implementing AI in Europe’s Businesses, Beyond the Hype

AI Business set out to find out how AI is transforming business today and how it will evolve in the future. They surveyed the C-Suite executives in the UK & Europe’s 300 largest businesses on how they see AI impacting their organizations, understanding their current and future AI projects, concerns and overall strategy. Georgios Kipouros, Research Director at AI Business, writes about the findings of the survey on techUK.

The majority of the leaders thought AI will transform their industry and saw it essential for their organization. Over 80% compared the impact of AI to that of the Internet. The leaders perceived AI as a way to improve efficiency, reduce overall costs, and also a way to enhance accuracy in their operations. Over 80% of Europe’s leading organizations were investing in machine/deep Learning technologies, expecting to spend an average of 4 million Euros per AI project within the next 2 years.

Read more about implementing AI in Europe’s Businesses at http://www.techuk.org/insights/opinions/item/10724-implementing-ai-in-europe-s-businesses-beyond-the-hype

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Keys to effective IIoT design

How does the IIoT change the way industrial products and services are designed? What types of opportunities should companies seize and which challenges should they expect to tackle? Petri Asikainen, Director of  Core Technology Development at Konecranes, shares his insights.

Machines report on their usage and condition. Maintenance interaction data is combined with equipment engineering information. Remote-controlled cranes. Logistics chains are truly transparent, as each link is programmed to anticipate the next steps, optimizing flow across the entire chain.

As low-cost sensors and powerful software turn the IIoT from buzzword to reality, the opportunities for new product and service design seem endless. But where should companies start?

Focus on customer value

“The starting point for successful design is a deep understanding of the client’s processes and needs.”

According to Petri Asikainen, Director of Product Development at Konecranes, the key to great IIoT design involves taking a step back from all the cool things that technology can do, and focusing on where it can offer clients the most value.

“Buyers in industrial companies are usually highly rational. Most of them do not care for fancy new gadgets or features just for the novelty of it,” says Asikainen. “The starting point for successful design is a deep understanding of the client’s processes and needs. What is essential and useful for them? Can you make significant improvements in efficiency or work safety? How can you make their life easier?”

Sometimes the solution might not be selling new equipment, but rather retrofitting old equipment with sensors that connect it to intelligent networks to extend its lifespan. Or, it may not mean new sensors but developing software to utilize the sensor data already available in a new way.

“When you have a solid understanding of the user environment, there’s quite a lot you can do by developing the software instead of adding new hardware or sensors – and it’s often more cost-effective too,” he continues.

Design products that communicate

It’s important to note that in the IIoT environment, your products are just small pieces of a larger network. To offer real value, they must fit into, communicate with and improve the client’s existing system.

“With any type of new product, you need to ask the basic questions: How will it communicate with its surroundings and other intelligent systems like enterprise resource planning software? What type of data should it gather and for what end?” explains Asikainen.

Not all data is equally useful either. With increasing amounts of automation, and more smart machines and programs being used at industrial sites, the human operators’ ability to handle new information is often already at its limit. As Asikainen points out, “Especially in user interface design, you should be careful to present only the most crucial information at each given moment.”

Another aspect concerns the high demand for data security in the industrial setting.

“Unlike on the consumer side, you cannot monetize customer data by sharing it with third parties,” Asikainen says. “Data security is a huge issue in IIoT design, as the possible risks with data breaches are often severe.”

Use data to deepen understanding of client needs

“With a clear focus and insight into client operations, IIoT solutions can produce tangible gains for most industrial clients.”

While there are challenges in designing for the IIoT environment, there are also huge opportunities. For one, the influx of new information allows designers to form a deeper understanding of clients’ real needs and to offer them optimized solutions.

“Before, we had an incomplete view into how clients actually use our equipment and services. Now, we can start the design process from facts and real-life data. That is a great advantage for both sales forces and product development.”

In practice, this can mean creating new service concepts or equipment models that fit the clients’ use patterns more precisely. Or it could include offering complementary services that improve safety and quality such as staff training sessions on how to use the equipment efficiently.

The good news, says Asikainen, is that with a clear focus and insight into client operations, IIoT solutions can produce tangible gains for most industrial clients.

“In heavy industrial settings where production volumes and material flows are large, optimizing equipment and tweaking processes with the help of new data can bring in significant savings,” concludes Asikainen.

Petri Asikainen works as Director of Core Technology Development at Konecranes.

Interview w/ Petri Asikainen

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Securing IIoT systems still a contractual no man’s land

The industrial internet is a continuously evolving and layered infrastructure built on connected machinery – a large proportion of which has not previously been linked to the internet. The fact that these machines can now be accessed online brings new challenges for IoT service providers as well as their clients. Furthermore, questions remain regarding responsibilities, says Pasi Vilja, Chief Information Security Officer at Konecranes.

Last year, a massive distributed denial-of-service (DDoS) attack swept through the globe and nearly disrupted the entire internet. Experts called it the largest attack of its kind in history. Afterwards, close investigation revealed that the assault had been orchestrated completely through IoT devices. A huge number of web cameras were left unprotected, and this offered an easy opportunity for hackers to mount a large-scale attack via the internet.

“This is a great example of the vulnerabilities born out of millions of unprotected devices suddenly being connected to the internet. As the number of internet connected devices continues to grow, new vulnerabilities also arise, bringing forth questions about internet safety which we haven’t faced before,” Vilja says.

The need for shared solutions to these questions is growing increasingly dire as more and more machines – many of which were designed before the advent of the IoT era – are connected into the internet, and operated in ways which couldn’t have been considered at the time they were made.

Implementing security measures in the era of IoT

According to Vilja, security in the context of the industrial internet can be implemented mainly through the same types of practices already used in securing computer networks. Keeping up a proper firewall, requiring identification, and constantly surveying and reacting to problems that arise quickly are important, as is updating software.

“The same principles work in both an ordinary IT context and an IoT environment. On the software level, there isn’t that much of a difference in how the systems can be kept safe in either setting. Still, the industrial context adds a layer of complexity to the equation,” Vilja says.

One of the greatest differences in terms of web security in an industrial context is the machinery’s long lifecycle, which brings forth new questions on service providers’ responsibility to offer their clients updates for extended periods.
“Some machinery in industrial use still run on Windows XP or even NT. For the former, support ended in 2014 – ­ and for the latter, in 2004. How are we going to ensure that systems will be kept secure when some of the machines have lifecycles of 50 years? These are still questions to be discussed,” Vilja says.

Another issue comes up with the variety of machines being connected to the web. Industrial companies might have a combination of old, non-connected machinery which is now being connected to the web, point-to-point connected machines, and newer internet connected machines. When they open all these machines gradually to the internet, questions arise on how to make sure that no gaps are left between the different ways to connect.

Discussions about responsibilities still underway

Who has the ultimate responsibility regarding the IoT solutions in use and keeping them up to date? Is it the service providers? And if so, then how long and how actively do they have to ensure that the security is current? According to Vilja, these questions are still open for discussion, and no concrete best practices have surfaced yet.

“This is very much a discussion still to be had. Service providers must take responsibility to ensure that the services they offer are maintained to protect against new security threats. But only the clients know their full set-up and probably don’t want automatic updates from multiple providers. And how knowledgeable are the clients about the relevant security features or risks? This is still a contractual no man’s land,” Vilja says.

Another concern is that in highly specialized systems that have been tweaked or integrated by clients, the updates could cause interruptions – or even shutdowns –  in their operations. On the other hand, refraining or neglecting to update their systems could also end up leaving their entire systems vulnerable.

According to Vilja, in order to form proper guidelines, open discussion and continuous surveillance are essential. Eventually, the best practices will be formed, and they are likely to follow precedents from the computer market.

Ultimately, the same rules apply to web cameras and smart refrigerators as for industrial sensors – basic security measures go a long way, and they must actually be implemented in order to ensure operational safety.

Pasi Vilja is the Chief Information Security Officer at Konecranes.

Interview w/ Pasi Vilja

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Looking for the human-machine touch

Digital technology is fast changing the way vehicles are built, but the pace of change varies according to different manufacturers and production processes. Above all, the importance of human workers has been central to the decision process for new technology – and looks set to remain so in the future.

According to Automotive Logistics, experts who spoke at automotiveIT Forum – Production and Logistics, which took place during the recent Hannover Messe, stressed that digitalization starts on the shop floor. Implementing logistics automation and support technology needs to be done with workers in mind – including their safety and comfort, but also their skills. For instance, Dr. Sabine Pfeiffer, professor of sociology at the University of Hohenheim, noted that the industry tends to focus on university graduates or consultancies, “but if you work with the experience and skills on the shop floor, you will get great results.”

Read more on how to begin disruption at the shop floor level: http://automotivelogistics.media/intelligence/looking-human-machine-touch

Image credit: Zapp2Photo / Shutterstock.com

Via Automotive Logistics

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