Industrial Internet Now

To benefit from Industry 4.0, start somewhere and start now

It’s a common pitfall for companies to delay piloting Industry 4.0 projects until proven applications for specific problems have been developed. Prof. Dr. Harald Peters, General Manager at VDEh-Betriebsforschungsinstitut, Germany’s central research institute for the steel industry, urges companies to start by first adopting a wider mindset.

According to Peters, companies shouldn’t wait for disruptive technologies to emerge before starting to build their own solutions. Forward-thinking organizations will gain a competitive edge by trying things out early on.

“When companies started applying new digital technologies to their operations some five to ten years ago, there wasn’t any talk of big data or deep learning,” recounts Peters.

According to him, what’s changed since the last decade is that due to the hype around Industry 4.0, organizations have now become more cautious.

The focus in the German Industry 4.0 approach has been on building smart factories in which computer-driven systems monitor physical processes, create a virtual copy of the physical world, and make decentralized decisions based on self-organization mechanisms. It seeks to build on Germany’s existing strengths, augmenting them with new IIoT possibilities such as strong customization of products through flexible production.

“The main factor in starting is having the right mindset and the right amount of imagination”

“Ten years ago, we focused on technological problems. These could have concerned, say, scheduling or quality, and solutions for these were sought after. Now, after all the buzz surrounding the IIoT, decision-making goes through the higher management level, and every idea is deliberated on thoroughly before any action is taken.” This move, he says, slows the process down.

“The main factor in starting is having the right mindset and the right amount of imagination to be able to realize ideas leading into Industry 4.0.”

Advice from an expert

Peters’ vast experience includes chairing the Integrated Intelligent Manufacturing working group of the European Steel Technology Platform (ESTEP), which brings together the European steel industry’s major stakeholders. From this privileged perspective, he is able to offer some key points that traditional steel manufacturers should bear in mind when considering investments in digital technology.

The first involves adopting a wider view – one that looks beyond today’s requirements. Companies, particularly those in the steel sector, need a strategy for the future on how to digitalize their production processes, says Peters. Having one will help them implement the right technologies. “Industry 4.0 is much more than digitalization,” he argues. “An essential part of the development process is that of selecting the most suitable examples to realize Industry 4.0 applications based on digitalized plants.”

Second, Peters emphasizes the importance of forming partnerships and pursuing collaborations. Knowledge of what manufacturers in other industries are doing as they move towards the factory of the future can be extremely beneficial in overcoming hurdles and finding the right solutions. In addition, he suggests discussing ideas with research institutes and universities. “Collaboration is really beneficial, as partners can exchange their ideas and best practices. You can learn and improve your thinking, and gain new ideas,” he continues.

The steel factory of the future and the circular economy

Looking beyond Industry 4.0, perhaps to the Digital Ecosystem of the 2030s, Peters offers a glimpse of what steel factories of the future might look like and how they might function. He believes that changes in the steel industry will be the result not only of technological innovations, but will also be brought about by policymakers’ decisions.

“What the steel industry will look like in 20 to 25 years depends completely on political decisions,” Peters points out. “But in any case, there will be a more fully formed circular economy in place by then, and companies will have to adjust to that.”

“Efforts have to be made, for example, to develop new technologies to produce high quality steels from scrap metals”

He says that the new economy will require companies to think of new ways to deal with scrap, as getting rid of surplus and waste will help the steel industry achieve a more circular economy. Peters notes that efforts have to be made, for example, to develop new technologies to produce high quality steels from scrap metals.

“And this isn’t enough. We also have to find ways to reduce energy consumption, or to recover energy used in high temperature processes and to use this recovered energy in other areas of steel production. Here Industry 4.0 can help to develop solutions to detect cause-and-effect relationships between energy consumption and process conditions, or to coordinate different activities of energy management in real time. This requires, for instance, the capacity to handle huge amounts of data and to extract the right information from this data at the right time,” Peters says.

Ultimately, with the added level of intelligence in the production process provided by Industry 4.0 technologies, all these efforts combined will have a huge effect on the industry, however it may look in the future. This demands that companies start somewhere, and start soon.

Prof. Dr. Harald Peters works as General Manager at VDEh-Betriebsforschungsinstitut GmbH. He is member of the European Steel Technology Platform (ESTEP) and is currently the chairman of the ESTEP Working Group, “Integrated Intelligent Manufacturing (I2M),” which deals with Industry 4.0 applications in the steel industry.

Interview w/ Prof. Dr. Harald Peters

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Millions of things will soon have digital twins

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

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

Read the full story here:

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

Via The Economist

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The new KPIs of efficiency

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

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

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

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

The right quantity matters as well

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

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

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

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

Measuring predictive maintenance

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

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

The role of data analysis tomorrow

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

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


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

Interview w/ Dr. Dirk Lange

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Predictive maintenance: the brainpower behind smart factories

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

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

Read the article here:

Via DigiTimes

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Digital twins – 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.

Interview w/ Matti Kemppainen

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