Why Industry 4.0 is not only about IoT devices
Commentary on Industry 4.0 that only focuses on the Internet of Things (IoT) entirely misses the point, argues Ben Merton in his article in IoT Tech News. More than 80% of Computer Numerically Controlled machinery is owned by small and medium-sized businesses, who are unlikely to have enough resources to make full use of the IoT functionality of their machines.
The opportunity exists for centralized, technology-driven solutions that use IoT to connect and manage all the entities in a given supply chain. An end-to-end, IoT enabled manufacturing solution will not only reduce the cost and complexity of managing an outsourced supply chain, but also lead to a whole host of environmental and social benefits like reducing the amount of waste, lowering overall costs and localizing.
Read more about the true power and impact of IoT and Industry 4.0: https://www.iottechnews.com/news/2017/apr/18/why-industry-40-not-only-about-iot-devices/
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The Operations Technology (OT) vs. Information Technology (IT) debate turns to better security
While OT managers may see the benefits of IoT-enabled asset monitoring, IT leadership can see IoT connectivity as a security threat. IoT-connected machinery offers uptime rewards at minimal risk but when done wrong, that connectivity into OT systems can pose big threats.
Material Handling Product News has interviewed several security experts on the ways to avoid security vulnerabilities when moving from closed-off OT systems to wireless networks and IoT connectivity.
“Integrating these systems can provide a lot of efficiency and help with goals like uptime, but at the same time, as things become more connected, they become more vulnerable.” says Keith Blodorn, director of program management at ProSoft Technology, which specializes in industrial communications and remote access solutions.
Read more about how companies are solving IoT connectivity data security issues: http://www.mhpn.com/article/the_operations_technology_ot_vs._information_technology_it_debate_turns_to
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How can Industry 4.0 help the global steel industry achieve greater efficiencies?
Taking place in Warsaw, Poland, the Future Steel Forum assembles speakers from academia and the steel industry to examine how technological innovations can revolutionize steel production. Matthew Moggridge, Editor of Steel Times International, talks about the themes and perspectives steelmakers must consider as they shift to a digital manufacturing platform.
According to the 2016 Global Industry 4.0 Survey conducted by the consulting firm PwC, the buzz surrounding Industry 4.0 has moved on from what some had earlier considered as hype to actual investment and real results. This investment, in turn, is translating into increasingly advanced levels of digitization and integration. 67% of respondents from the metals sector, among them companies in the steel industry, say they expect to reach advanced levels of digitization in their vertical value chains by 2020.
Matthew Moggridge, Editor of Steel Times International, shares a similar view. “The steel industry is well prepared for Industry 4.0 and has, for a long time, been at the forefront of industrial technological development,” he says.
“There are companies, such as Primetals Technologies, SMS group, Danieli Automation, Fives, among others, who have been pushing the boundaries of digital manufacturing and partnering with leading steelmakers such as ArcelorMittal, Tata Steel, Voestalpine and many others to develop the concept of Industry 4.0.”
Moggridge adds that in the US, Big River Steel is arguably the first smart steel plant. The company recently partnered with Noodle.ai, a San Francisco-based Enterprise Artificial Intelligence company, to implement Enterprise AI to optimize operations at the former’s scrap metal recycling and steel production facility in Osceola, Arkansas.
“On the one hand, digitization has moved from being an augmenting capability for steel companies to something that is now becoming a disruptive force. On the other hand, it is delivering supply chain agility, deeper process understanding and higher production utilization.”
Efficiencies and challenges
Broadly speaking, Industry 4.0 assists the global steel industry in its quest for greater efficiencies while raising new concerns. On the one hand, as digitization has moved from being an augmenting capability for steel companies to something that is now becoming a disruptive force, the PwC report says that it is delivering supply chain agility, deeper process understanding and higher production utilization.
The report states: “Automation is combining with data analytics to enable much higher flexibility as well as more efficiency in production. Algorithms are linking the physical properties of the materials with production costs and plant constraints to improve efficiency. Processes that were previously separated are now being integrated, leading to reductions of heat loss, energy consumption, throughput time, inventory as well as better price optimization.”
On the other hand, the people aspect also needs to be addressed. PwC states that companies will need to make sure staff members understand how the company is evolving and how they can be a part of the change. From PwC’s interviews with metals companies, the biggest challenges involve issues such as culture, leadership and the economic case for change.
In addition, Moggridge cites Dirk Schaefer, assistant professor of design engineering at the University of Bath, UK, who argues that the development of a new work force will also prove challenging within the context of Industry 4.0. Schaefer believes that investing in workforce education is essential. “Each of the previous industrial revolutions resulted in a surge of unemployment. There is no reason to believe that this will be any different this time around, unless preventive action is taken today,” Schaefer asserts.
— IndIntNow (@IndIntNow) June 14, 2017
These topics will be addressed by experts at the Future Steel Forum in Warsaw, which takes place today and tomorrow. Other discussion points include the impact of smart manufacturing on the steel industry, Industry 4.0 and its implications for plant safety, the future of cooperation between automation and steel manufacturing, and the role of human beings in the factory of the future.
Taking part are speakers from academia, the steel industry and the world of steel production technology such as Dr. Rizwan A Janjua, Head of Technology, World Steel Association; Jose Favilla, Director, Industry Solutions for Industrial Products, IBM; and Professor Chris Hankin, Imperial College London, among others.
Connecting the dots
As for Industry 4.0-related themes that are set to gain ever greater prominence in the coming years, Moggridge, who will deliver the welcoming and closing remarks at the event, has this to share. “Cyber security will always be a big issue that will constantly need to be addressed, but also the role of the human being in an increasingly automated environment, not only in steel but in other areas of industry as well,” he says.
“What people tend to forget about the steel industry is that it is already a very automated environment. In many ways, it’s just a case of connecting the dots before steelmakers can claim to be true advocates of Industry 4.0.”
Matthew Moggridge is the Editor of Steel Times International. The Future Steel Forum takes place in Warsaw, Poland, on June 14–15, 2017. futuresteelforum.com
How the Internet of Things impacts supply chains
Enterprise resource planning and supply chain management (SCM) have gone hand-in-hand for quite some time, but the IoT revolution will allow those solutions to be enhanced by intelligently connecting people, processes, data, and things via devices and sensors.
“Think of it as SCM 2.0,” writes Udaya Shankar, Vice President and Head of Internet of Things for Xchanging, a business process service provider for the global insurance industry. According to Shankar’s article in Inbound Logistics, this deeper intelligence can come to life in many different ways when it comes to supply chain data and intelligence – from the automation of the manufacturing process to improved visibility within the warehouse.
One area that Shankar believes will play a prominent role in the future supply chain, as it’s impacted by IoT, is in-transit visibility. “The logistics ecosystem has many players, and thus, many moving parts. Products are handled and transferred between the manufacturer, suppliers, the distribution center, retailer, and customer.”
Read more about how IoT can help supply chain professionals at:
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The deciding factor – how to utilize IoT data analytics for business intelligence
To make the most of data, it has to be transformed into information, which then has to be transformed into intelligence. As companies seek to leverage data – whether it’s internal, external, structured, or unstructured – to improve profitability or boost operational efficiency, analytics makes it possible to gain insights on business areas that were previously out of reach. Alun Jones, Data Scientist at Konecranes, talks about how organizations can best use IoT data analytics to arrive at more impactful business decisions.
According to McKinsey, the potential economic impact of the IoT could reach $11 trillion per year in 2025. That figure is equivalent to around 11 percent of the global economy. Turning that possibility into reality depends on how effectively IoT data analytics is used to drive better decision-making. The technology research firm Gartner identified IoT data analytics as one of the key IoT-related technologies that should be on every organization’s radar in 2017 and 2018, second only to security.
To identify target-rich, high-value data that can be used to generate business intelligence, the following steps should be taken.
1. Be aware of what you already have. It makes sense to know if some of that information is already available or accessible even if it isn’t immediately apparent. If you don’t know, then find out: Build a data map for your enterprise.
2. Think like a custodian, not an owner. The term “data owner” can be misleading as it appears to imply not only ultimate responsibility, but also the ability to utilize data for one’s own purposes. Both are not necessarily true of data use within a business. A data custodian, meanwhile, is responsible for the technical environment and controls around data.
3. Every action is part of the value chain. The siloed approach to data access makes unifying the analytics layer a challenge. To generate scalability and real-time performance, however, all types of analytics – descriptive, diagnostics, predictive and prescriptive – must be brought together into a single engine.
The role of cloud analytics platforms
In terms of using cloud analytics platforms to derive value from IoT data, it’s important to remember that not all data is created equal. Companies should think of ways to get data from a device into a position where it can be analyzed; the priorities of that data need to be determined as well.
Next, it’s also essential to gather data from numerous sources as interoperability is key in a heterogeneous environment. Last, it is advisable to have distributed data sources so that the cloud is there by default. Cloud simply means off premises; there will be distance between individual data sources and the computer power performing the processing. If you are uncomfortable with the cloud then find out why, and work to alleviate those anxieties. Processing IoT data close to the source results in less network delay than transferring it to the cloud, processing it there, and sending back the actionable result, such as computing and analysis at the edge.
“Harnessing IoT data analytics for business intelligence is not a one-time exercise, but a continuous process.”
As far as the barriers to widespread IoT value delivery are concerned, these could be overcome in two ways. First is technical. This covers everything from data gathering and low power or no power devices. (At the moment, for example, sensors and devices need power to drive them or the transmission of data, and in the future there will be a need to have devices that have lower power requirements.) Data architecture and cost of hardware should likewise be considered.
Second is the people aspect. Gatekeepers need to change. Management must improve its ability to understand and interpret the output from analytics. Individuals need to collaborate, even with those outside their respective enterprises. Normal business practices mean that things are driven on short-term departmental measures – this must be reconsidered as well. Do you design your plant to be cheaper to build, or more efficient and flexible to run?
Overall, harnessing IoT data analytics for business intelligence is not a one-time exercise. Rather, it’s a continuous process. Bear in mind that not everything is going to work. Optimizing what you do today is not enough either.
In addition, look at how to change the business model in a way that fits the market. For instance, GE builds airplane engines. They innovate by fitting sensors to gather that data and transfer it back to the factory. This is adopted over time so all engines send data about themselves. Over time, this is optimized so that GE knows the state of each asset and is able to predict when parts are likely to fail. This reduces downtime, making maintenance more efficient. Once the asset behavior is understood and de-risked, the business is transformed from building engines to offering engines as a service. GE’s software platform is now the key element in their business model. Cranes are a little behind but are catching up fast as the platforms needed to support such devices are already being built.
Alun Jones works as a Data Scientist at Konecranes. He is participating in several panel discussions at the IoT Tech Expo Europe event in Berlin on June 1-2, 2017.
— IndIntNow (@IndIntNow) June 7, 2017
Learning by doing and the value of pivoting in an IIoT environment
Data-driven technologies and business models are becoming increasingly linked. For many companies, this means that the skills and assets they are today investing in bear little relation to those they sought five years ago, and neither do the services they offer. Mika Parikka, Managing Consultant at LINK Design and Development Oy, holds that the Industrial Internet of Things plays a central role in this redefinition process, as it is fundamentally affecting how businesses are being run and what kind of talent is deemed necessary.
According to Mika Parikka, the Industrial Internet has introduced a distinct shift in business models from times past, when often what companies sold was technology or machinery itself. Parikka is the former CEO of TreLab, an electrical manufacturing company whose activities include retrofitting wireless smart sensor solutions for industrial equipment.
“The question of IIoT must be understood as not being primarily about technology, but instead about positioning and differentiation. After you understand how the machinery is being used, you can begin to see how the business is, or should be, run,” Parikka says.
He doesn’t see the Industrial Internet as being completely defined by technology, although it determines the types of data companies will be able to offer their customers. Herein lies the crux to his argument; customers will essentially come to define the businesses which serve them. Parikka maintains that for companies to reap the benefits of IIoT and IoT, mentalities need to shift form ‘we are only selling hardware,’ to ‘we are selling services that benefit our customers.’
“This affects who the products and services are being marketed to, as well as how contracts between parties are being drawn up,” Parikka says. “At TreLab we provided our customers with technology which they could utilize in IoT and IIoT scenarios. We began seeing the kinds of problems that customers were having in selling and conceptualizing their services as they began telling us that their businesses were fundamentally changing, and that they are no longer billing their customers for what they used to.”
This is not a surprise when considering the types of customer relations that the Industrial Internet is spawning. “Previous business models were largely about selling machinery and technology to customers, whereas in the future, they will largely be defined by customers wanting total solutions. Thus, the most significant change that will occur is that sales, customer services and most notably contracts, will need to be done from the perspective of customer value, as opposed to selling things in the traditional sense.”
“The question of IIoT must be understood as not being primarily about technology, but instead about positioning and differentiation. After you understand how the machinery is being used, you can begin to see how the business is, or should be, run.”
Trial and error yields the best results
Parikka cites Kati Hagros, the Chief Digital Officer at Aalto University and former CIO of KONE, as someone who often speaks about the need for companies to learn from their customers.
“She often urges companies to go with the customer and try things out. Then after these tryouts, once the business model begins to take shape, to fund it lavishly so that is has the ability to overcome corporate inertia, which tends to stifle ideas that appear radical at first,” Parikka relates.
The willingness to engage in these tryouts necessitates a significant internal philosophical shift in companies, most of all from the people at top management level. However, Parikka feels that a mentality shift shouldn’t be seen as totally restructuring what a company has been doing or selling up until that point. This is particularly true for larger organizations who have the resources for testing and experimentation, as well as for setting up smaller units that have more autonomy in terms of serving the customer.
“I believe that those at top management level must start acting more like venture capitalists. Potential solutions to customer needs exist, particularly among those at the frontlines of organizations who understand and see both problems and solutions. The role of top management is to then recognize and understand which of the possible solutions to fund to put the company in the best possible position, and to allow the frontline people lead the change,” Parikka says
He asserts that this is particularly true for industrial organizations who manufacture and produce material products. “Especially for organizations that are now moving into providing anything as a service, the leadership need to clearly lead the way, saying: ‘this is important in our business, we need to do this and above all else try that.’ It is important to prioritize what seems to be the best option, going for it, and changing direction – or pivoting as they say in the start-up world – if it doesn’t work. Learning by doing is definitely the way to go.”
The birth of the knowledge worker
After successful learning and experimentation has taken place, the emerging business models will set new demands upon the workforce, breathing new life into them. Drawing on the topic of companies requiring new expertise and new talent, Parikka highlights how knowledge workers will play an essential role from an operational standpoint.
“Most of the work will be about defining the procedures and algorithms, when there are a lot of sensors and subsequent data coming in from different sources. A knowledge worker will need to be able to analyze vast quantities of data, and really understand where all the data comes from. On top of this, the worker will need to create ways to deduce action-oriented alerts from the data. In most cases this is not what we would call programming, but instead, the type of work we would be doing every day in Excel – creating rules and procedures to act on.”
Parikka, who is an engineer by trade, holds that this workforce will be increasingly made up of people with a technological prowess, who will then take the lead when it comes to business initiatives. He concurs with Bill Gates’ statement that ‘it’s very difficult to teach technology to business people, and thus much easier to teach business to technology people.’ Approaching business from a technological mindset will mean that issues such as IIoT data ownership will be prioritized and dealt with early on in contracts signed between service or data providers and their customers. For tech people to spearhead business model realization processes, they ultimately need the license, capacity and funding to experiment to get the best results.
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The future of IoT and machine learning – what role will humans play?
Despite having been around for over two decades, machine learning and its integration into business models is yet to become commonplace. Jari Salminen, Managing Director of Cumulocity, has witnessed the unfolding of machine learning and noted the progress made in its adoption into a wide range of industries over recent years. He says that rather than spend time in building grand strategies then assume that value can immediately be realized, companies should take pragmatic steps to connect their assets and start collecting data.
“What we are seeing today is that there typically exists a bit of a delay when companies start connecting assets and collecting information to be able to rely on machine learning algorithms and their accuracy,” says Salminen. “The training of these algorithms requires large amounts of data and thus time. It takes time for any individual company to move through the cycle of starting with very basic use cases and moving onto more complex algorithms and dependencies, and eventually introducing machine learning.”
At Cumulocity, Salminen deals with several manufacturing and industrial companies. He recognizes companies that require warehouses – or those whose supply chains do – currently expect sophisticated IoT solutions from a production and manufacturing point of view.
“Things are changing at such a pace that it is now very cost efficient even for smaller companies to deploy off-the-shelf IoT solutions for their supply chains.”
Salminen encourages companies who have examined the cost of IoT solutions for manufacturing or supply chain management over recent years to do so again. “Things are changing at such a pace that it is now very cost efficient even for smaller companies to deploy off-the-shelf IoT solutions for their supply chains as the price of hardware, connectivity and software has dramatically reduced over the last 5 years,” he reasons.
Holding algorithms accountable
The accuracy and efficiency at which these solutions can be implemented will greatly depend on the algorithms triggering them. According to Salminen, the more automated these algorithms become, the greater influence they will have not only on supply chains, but beyond them as well.
“Today, most actions are still done by human users and there are several reasons why that is. For example, accountability – in the long-term, we will need to ask how this will change. Will decisions be made based solely on the algorithms that machine learning will make possible? Maybe. However, such questions and their answers go beyond technology itself, as they are concerned with making sure that somebody other than a computer takes responsibility for decisions. It’s a complex domain,” Salminen says.
So, how to legally and ethically approach decision-making when no human is involved in the process? Very similar questions are currently being asked in the automotive industry.
Closely tied to the accountability of machine-to-machine decision-making within IoT projects is the matter that is security. In general, issues surrounding security are never too far away when connectivity, machines and material networks are concerned.
Multilevel security management
“The issue with security is that it exists on so many levels in IoT projects: from hardware – meaning devices, machines and assets – and connectivity, whether that entails using mobile connections or new narrowband IoT solutions, to the backend, meaning the cloud or servers. On top of all that you might have special applications for company users, partners or even consumers. Security needs to be controlled and monitored at all these levels.”
“Often, security breaches take place when more than one area has been overlooked. A most common area attracting hackers and attacks is wherever any connected device exposes ports reachable from the internet. These are being scanned by hackers,” Salminen continues. He reminds companies that if they are dealing with hardware, that they should keep their wide area network ports closed; from a connectivity perspective, they must make sure that everything is fully encrypted in transit.
“If you are providing cloud services on top of hardware and connectivity, make sure that your whole system is robust and secure. Ultimately, there is no single thing companies must consider when it comes to security, but rather, it is an area that must be owned end-to-end by someone in the project.”
Setting future standards
When asked how he sees the future of machine learning and IoT taking shape, Salminen is simultaneously restrained and excited. He sees industries as being past the initial hype. The implementation of more advanced and sophisticated use cases is now becoming a reality.
“The connectivity costs per device are decreasing, and thus enabling many new developments when it comes to industrial solutions. This is happening across widespread assets, meaning that for example, narrowband technologies are becoming more popular. However, what is still lacking from IoT and will need to be addressed in the next few years is standardization.”
While Salminen is adamant that a comprehensive philosophy regarding standardization must be adopted, he does not foresee the use of one single underlying standard that will apply across industries.
“My guess is that there won’t be an overarching standard that will include everything from devices to data structures. There will be so many different IoT use cases that it will be impossible to create something that would cover all of them,” he says. “We see lot of traction with MQTT (Message Queuing Telemetry Transport) as a messaging protocol due to the fact it doesn’t even try to standardize all parts of the IoT stack. For example, it does not deal with message payload format which is left to the developer to decide.”
On the other hand, Salminen believes that standards like Lightweight M2M by Open Mobile Alliance are not being picked up by market players because probably it doesn’t fit many use cases, among other reasons.
Nonetheless, he reminds those looking to initiate IoT projects to certainly consider available standards, but not to be limited by them. He also states that the key thing to ensure is that you are not locked into any standard, but to have flexibility in case your needs change in the future.
“If I were starting an IoT project, I would be looking at the most recent connectivity options, the role of standards, if such exist, and whether or not they are relevant to my business. However, I wouldn’t force any standards at the moment, as there are in fact very few that are relevant,” Salminen concludes.
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Why collaboration, not competition, is key in a hyperconnected world
Similar to the internet in its early stages, IoT will need to secure a safe and standardized collective environment in which to operate. According to Iain Groves, Solution Owner at Fujitsu UK & Ireland, “communications protocols and service quality definitions are needed to ensure the development of the highly heterogeneous and open environment required.” Due to the nature of the IoT, this environment will represent something that is greater than the sum of its parts. Thus, “a new, dynamic form of network management will be the only effective approach for the IoT,” writes Groves.
IoT undoubtedly presents significant opportunities both for large manufacturers and consumers alike. However, a balance must be struck between collaboration and competition in order for those opportunities to be realized. It is only then, Groves notes, that “we can ensure that the IoT reaches its full potential – and works for everyone.”
Read more about how to create a strong IoT ecosystem at: http://www.itproportal.com/features/why-collaboration-not-competition-is-key-in-a-hyperconnected-world/
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The Augmented Reality and Virtual Reality revolution in manufacturing
According to Leroy Spence, Head of Sales Development at EU Automation, “like any disruptive technology with roots in the consumer market, industry viewed VR with a certain level of scepticism to begin with.” That is to say, industrial manufacturers didn’t at first consider developments in VR as having value in terms of production. However, for example in the automotive industry, designers and engineers use immersion labs where Oculus Rift headsets support the virtual testing of designs on vehicles. In his article for automation.com, Spence notes how one of the biggest indicators of the potential of AR and VR for industry has come from a shift in recruitment at major engineering companies.
Spence goes on to say that recently, firms have been very open about actively recruiting graduates with game design degrees. “Astute with VR, Android and mobile technology, this next generation of engineering recruits are helping make Industry 4.0 and Internet of Things (IoT) applications a reality.”
Read more about the potential of AR and VR for industry at:
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Reinventing traditional industries
New realms and areas of innovation can be daunting for individuals and organizations alike. Navigating through the maze of prospects and opportunities, and then recognizing the viable ones to pounce and build on, makes the Industrial Internet of Things a jungle of locks for the shrewdest of locksmiths to take on. Anja Hoffmann, the Founder of Copenhagen based strategy and innovation consultancy company Sentio Lab, is convinced that industries are full of such locksmiths, however, she holds that too often those clutching the keys don’t have a clear mindset as to what locks they ought to open. For Hoffmann, companies across industries must first focus on themselves, before venturing to capitalize – and make sense of – the countless opportunities made possible by new data and technology.
“As increasingly many industries are becoming tech-driven, I always start by looking at the mindset of the company looking to shift their focus to technology, regardless of their industry,” says Hoffmann, who also works as an Innovation Mentor for High-Tech Companies at Scion DTU. “In general, I think we have a lack of mindset across industries when it comes to understanding both the opportunities, and challenges in developing business models using new technologies,” she continues.
Hoffmann’s call for a more measured approach is far from baseless. She has been working closely with a wide range of industries for several years, thus gaining a unique vantage point. For her, more “traditional” industries often struggle to reinvent their approaches internally, especially when it comes to customer relations. However, there are exceptions.
“The pulp and paper industry, which I’ve been working with for some years, is an interesting example of a sort of hybrid between tradition and innovation. When it comes to the production process they remain very traditional and even historical, but because many of their customers are creative and innovative, they have been forced to think about new ways for longer than for instance the steel industry.”
In the case of the pulp and paper industry, mindsets have thus been revised by partners and other actors within an ecosystem, something Hoffmann feels very strongly about. For her, ecosystems and value chains represent one and the same thing.
“Companies need to look into their ecosystem, meaning their partners and so on, and ask themselves: what kind of an ecosystem will match our business ambitions in the future? Ecosystems are so important not only in regards to discovering and integrating new technology, but innovation in general. If you consider some of the companies who have asked questions, and succeeded in answering them, they are companies which have recognized that they are more of a service company than a manufacturing company.”
“Companies need to look into their ecosystem, meaning their partners and so on, and ask themselves: what kind of an ecosystem will match our business ambitions in the future?”
Reconciling tradition and innovation
Finding a balance between the two is easier said than done. According to Hoffmann, there should nonetheless be no reason why more “traditional” companies and industries wouldn’t be able to successfully implement a tech-driven agenda, and thrive from what the Industrial Internet promises. She mentions German elevator manufacturer ThyssenKrupp as a great example of a manufacturing company looking beyond its own industry, and desiring to be part of something what would traditionally fall outside the operating parameters of an elevator manufacturer.
“Although ThyssenKrupp manufacture what is essentially a technical product, their latest Industrial Internet developments have not been oriented for a traditional elevator business, but instead more for a service business. Because they have built an intelligent ecosystem around their solutions, ThyssenKrupp are transforming not only elevators, but elevator services to be a part of a smart-city movement.”
Living in the present, preparing for the future
Considering the popularity of the phrase “we live in a constantly changing world,” and other variations of it, Hoffmann reminds us that while this certainly remains the case, it’s vital to focus on the here and now.
“If companies pursue interesting and innovative partnerships across corporate investors and start-ups, they can minimize how much they change at once. Sometimes we focus too much on the fact that we live in a fast-paced business world, which is of course true, but we also need to do remember to experiment in the present, especially with already existing businesses. In order to do this, companies must assess how they can change their own DNA by looking into their value chain or ecosystem.”
When it comes to introspection into ecosystems, the role of the Industrial Internet can’t be understated. One prevailing innovation concerning manufacturing companies has been the increasing integration of co-bots – or “collaborative robots” – into joint working environments with humans.
Not too long ago, robots being used in the automotive industry for example, were kept in cages. Since then, co-bots have emerged and are forcing manufactures to rethink the structure and processes of their value chains. According to Hoffmann, this brings the need to question the necessity and future value of certain skills within the entire Industrial Internet landscape.
“When it comes to co-bots, what will be a challenge in the near future not only for the manufacturing industry, but also for the retail industry, is the demand for new skills from workers. The Industrial Internet is affecting business models as well as society, without forgetting the potential drawback of information overload and issues of security.”
Thus, the impact of co-bots should be assessed from a workforce and societal perspective. Co-bot technology will undoubtedly assist in driving profits and quality via reduced margins of error and sustained performance capabilities, however, as Hoffmann notes, companies “will no longer need programmers with specific technical skills to work with these robots.”
Despite new workforce demands and implementation possibilities in terms of what the Industrial Internet offers – and as long as leaders acknowledge the challenges and opportunities, without “just pushing a certain agenda forward,” – Hoffmann is certain that companies across industries will prosper from IIoT solutions on many fronts.
Anja Hoffmann is the Founder of Copenhagen-based strategy and innovation consultancy company Sentio Lab.
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