Combining mixed data – unlocking the real value of IoT
Most companies are at a design and test phase in terms of Industrial Internet solutions. Integration into larger, complex systems remains somewhere on the horizon. The full potential of the gathered data will only be truly realized once comprehensive integration into these complex systems becomes a prominent trend. Niall O’Doherty, Director of Business Development Emerging Industries Team at Teradata Corporation, hopes that within five years, the technology necessary for such integration will be commonplace. The question then becomes — will corporate philosophies match the capabilities of these technologies?
Data environments are being inherently redefined due to developments across IoT and IIoT. To do away with detached data “pockets” – which is to say, with data that remains unintegrated into systems or with other data – an overall process of synthesis is necessary. Key to such a synthesis, and subsequent realization of the true value of IoT and IIoT, will be the integration of the already widespread use of sensor data.
“To get to the real transformational value, more of these systems must be put into place. In order for that to happen, sensor data needs to be integrated with product data, customer data, ERP (Enterprise Resource Planning) data and other traditional data. For many organizations, bringing sensor data together with traditional data – and making sense of it all – is still a major challenge,” states Niall O’Doherty.
“I hope that in the next five years we will be able to regard sensor data connected to communications infrastructure as a common feature of business,” he continues.
The increasing flow and current of data across organizations and systems naturally raises pertinent questions about data ownership. The fact that once data enters ecosystems, no single organization, agency or equipment manufacturer is going to have exclusive control of the data and its distribution, casts doubts over the approach of companies and – according to O’Doherty – over the attitudes of individuals.
“Are people going to be willing to share all this information? Are they going to be willing to take the output of their particular optimized process, and put it into the input of another, so that we can build a better understanding of what’s going on in a complex manufacturing environment? I think that a lot these commercial and cultural issues will need to be resolved, otherwise they can really trip up organizations.”
“I hope that in the next five years we will be able to regard sensor data connected to communications infrastructure as a common feature of business.”
Making sense of sensors
With the capacity to extract data from vast processes becoming more prominent, complex analytics must process data in ways that allow for more than simply deciphering averages and statistics. According to O’Doherty, this is particularly imperative for industrial and manufacturing companies.
“With the volumes that sensor data is generating, especially in the industrial world, coupled with the complexity of analytics, you really need to bring the analytics and algorithms to the data. In order to do that, you need a scalable IoT platform.”
In the material handling industry, such a platform could facilitate anything from predictive analytics to looking at how employees move on a factory floor, thus optimizing operations accordingly. O’Doherty uses the enhanced oil industry as an example. By putting highly instrumented equipment on rigs and sensors on the ocean-floor, the Industrial Internet has greatly aided in efficiency and optimizations of complex processes and systems. “What’s innovative for them is how they are now using vast amounts of data to understand the subsurface a lot better,” O’Doherty says.
Same products, new services
For O’Doherty, the creation of new business models via sensor data is not necessarily at the crux of Industrial Internet developments. Instead, he sees business models created for existing products as reaping the benefits of the Industrial Internet in the future.
“I see the power of sensor data and the Industrial Internet in allowing organizations to implement a scale for different business models. Those models may already exist, but as a result of this new data, they can be made more profitable and customer-oriented. It’s about understanding and mitigating risk so that you can potentially implement multiple models for the same products: to different markets, companies or customers.” This also increases the likelihood of new services emerging indirectly from existing products.
The notion of selling services, as opposed to products, is a concrete example of how the evolution of the Industrial Internet allows for the modification of, or experimentation with, existing business models. “For example, the notion of Power by the Hour – meaning a company won’t sell their customers an engine or a train, but instead the power needed to run them – was in fact coined in the 1960s by Bristol Siddeley. So, it’s not necessarily a new business model,” O’Doherty notes. Interestingly, later that decade Bristol Siddeley was bought out by Rolls Royce, currently one of the forerunners in embracing the Industrial Internet.
In order for the rest of the manufacturing world to keep up with the likes of Rolls Royce, O’Doherty reminds CIOs and CEOs of their roles as “enablers,” who first and foremost allow for businesses to change the way they approach products and services in general. “My advice – to a CIO in particular – would be to ensure you build the right infrastructure and environment to allow people in your company to access the data they need, and add the analysis they want,” O’Doherty concludes.
Niall O’Doherty works as Director of Business Development Emerging Industries Team at Teradata Corporation
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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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The problem with IIoT design
As with all trends and innovations in their infancy, there is bound to be some premature efforts deemed as groundbreaking, when in reality they fail to sustain their relevant functionality beyond initial hype. According to EE Times editor Rich Quinnell, this has been the case with IoT design. “All too often the design behind these [IoT] devices is not all that smart. It’s clever, it’s innovative, but IoT designs are also all too often piecemeal and rushed to market. What’s being created is a system of systems, without the system-level design issues getting addressed,” Quinnell writes.
The Object Management Group (OMG) is nonetheless providing a remedy for “correcting IoT’s trajectory.” For his EE Times piece, Quinnell spoke to Matthew Hause and Graham Bleakly of OMG to find make sense of the issues surrounding current approaches to IoT design. “We’re trying to get people away from building the IoT by hacking,” says Bleakley.
Read more on Hause’s and Bleakly’s thoughts at: http://www.eetimes.com/author.asp?section_id=8&doc_id=1331075
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The learning curve: From the Internet to Big Data to IoT
The technological developments born within the boundaries of the IT industry, and conversations that follow outside these boundaries create trends that are greater than the sum of their individual parts. Challenges are becoming less unique to manufacturers of particular products, and opportunities more ubiquitous to a wide range of service providers and manufacturers alike. In other words, technologies, industries and societies will become increasingly related to, and contingent on, one another in 2017.
Mikko Marsio, Vice President of Digital Business and IoT at Empower group, says that what has unfolded over the past two decades and led companies to where they are today can be understood as both an evolution from a technological perspective, as well as a revolution from an industry and business perspective. From the speculative nature of the IT bubble, to the profoundness of the Internet of Things, Marsio explains how consolidating technology with business is now more imperative than ever before.
“I remember a prediction that was made before I attended an MIT Executive Education course on the Internet in 2000. It envisioned the Internet becoming like electricity, meaning something that we don’t even acknowledge when using,” Marsio reminisces. “If you look at what was laid out in 2000 in conjunction with the IT bubble – for example that the best years for the pulp and paper industry were then and there – no one could actually have predicted how many paper mills would be shut down over the following 15 years.
In order for these mills to stay relevant, they must adapt what they are producing. Companies in general need to understand how both digitalization and end-users are causing their businesses to change. Over the past few years, increasingly many have come to recognize this,” he continues.
An affordable evolution
Despite the predictions regarding the impact of the Internet made at the beginning of the millennium, it would have been impossible for companies to imagine the extent of its integration into businesses. Many companies, and even industries, are now at a point where they are faced with a similar integration problem to solve concerning IIoT. For Marsio, integrating the Internet was the first hurdle for businesses to overcome, Big Data and analytics the second, and IoT the third.
“Big Data is the result of an evolution and I’m not sure that IIoT and IoT can, or indeed should, be separated as distinct developments. I say this because what essentially facilitates Big Data are the digital interfaces created for customer connectivity to machines.”
Machine connectivity and digital interfaces. Sounds very IoT doesn’t it? Marsio recalls an early example of this kind of machine connectivity from his time at Hewlett-Packard, when IoT or IIoT terminology had yet to see the light of day.
“In 2006, when I was working for HP, we were working on how to connect all our office equipment, especially multifunctional machines, to the Internet. This made the remote storage and analyses of data possible, and it also allowed the company to deliver a new kind of value for customers. Since 2006, Big Data has evolved to partly define what IoT is today, as we are now able to gain insights from thousands of data points, analyze these insights in real-time and ultimately use them to drive services. Moreover, in 2017 this can all be done affordably.”
From the short to the long-term
The short-term benefits of such insights can already be seen. However, long-term outlooks still require work. According to Marsio, companies must begin to address how they will develop the execution capacity necessary to scale up tangible opportunities not only now, but also in the future.
“In the manufacturing side, we will see less errors and faults in the short-term, which means companies will improve their overall equipment efficiency. Moreover, companies will not only gain insight into processes from, say the control room of a pulp and paper mill, but they will be able to do so remotely. In the long-term what will be more challenging, for example for players in the pulp and paper industry, will be addressing the ‘paper’ part of their businesses.” This is to say that as end-users’ needs change, the customer-value of paper will need to as well.
According to Marsio, short-term objectives and long-term perspectives can be maintained and executed in parallel. This necessitates a systematic management approach to IIoT opportunities and inherently entails considering the future.
“Within the last few years, there has been a change in how companies approach future developments. This means that companies are now anticipating more of a journey with regard to IIoT, as opposed to a project to be tackled, executed and moved on from. Therefore, future trends, developments and opportunities will be considered as a continuous flow of things.”
“Short-term objectives and long-term perspectives can be maintained and executed in parallel. This necessitates a systematic management approach to IIoT opportunities and inherently entails considering the future.”
Not just thinking, but acting ahead
How do companies and organizations evaluate what they could, should and must do now, and what are the potential consequences of those actions, Marsio asks. Part and parcel of a journey mentality is evaluating the future, which can be challenging especially in industries that have been set in their ways for many years, or even decades. When envisaging what a company will be in 20 years, and who and what it will serve, Marsio encourages leaders to think beyond their businesses and consider societies at large.
“Take Tesla. If in the future, we will all indeed have electric self-driving cars, why buy one at all? The same car that drives you could be used by others when you don’t need it. What would happen to companies offering parking spaces in city centers? Or the driving experience itself? German automotive manufacturers typically market the driving experience as the number one thing to consider, but if there is no driver, what’s the relevance of the experience?”
Regardless of leaders thinking ahead, the questions posed above require action in order to gain answers, and that’s what is currently so compelling about IIoT and IoT. The more ordinary and accessible products like Tesla’s become, the more products will be transformed into services, and thus, the more answers companies will have. However, waiting for that to happen, as opposed to making it happen and becoming accustomed to what IIoT allows for, will result in an opportunity lost. As with electricity and the Internet, Marsio holds that companies should aim for such a profound awareness of IoT that it becomes intuitive to corporate mindsets.
“Ultimately, it is essential for companies to consider how they can get to a point where they no longer acknowledge the fact that they are using IoT or IIoT,” he concludes.
Mikko Marsio works as Vice President of Digital Business and IoT at Empower Group
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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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Top 10 ways integration will transform manufacturing in 2017
“Enabling a faster pace of innovation in manufacturing starts by using systems and process integration as a growth catalyst to profitably grow,” writes Louis Columbus, Vice President of Marketing at iBASEt.
In Columbus’ article for Enterprise Irregulars, he highlights the importance of real-time data for both manufacturers and customers. He also mentions how the integration of traditional IT and manufacturing systems are vital in order for the potential advantages of Industry 4.0 to be fully realized.
“The key to revitalizing existing production centers and getting them started on the journey to becoming smart factories depends on the real-time integration of IT and manufacturing systems,” Columbus continues. Manufacturers should also expect the importance of IoT generated sensor data, combined with advanced analytics, to keeping increasing in 2017.
Read more about how integration powers manufacturing innovation at:
Mass customization in manufacturing – enabling customer-centric value creation
Traditionally, manufacturing has been defined by supply chains geared towards maintaining production costs as low as possible, with ultimate emphasis placed on output and distribution. These supply chains have largely been both enabled and limited by the hardware systems at their core. As companies are beginning to introduce data-driven, software enabled supply chains, manufacturing will increase in efficiency and mass customization will follow suit. In terms of distribution, platforms and apps are becoming the preferred medium and should be grabbing the attention of material handling industry as well.
Frank Piller, Professor of Management and Scholar of Mass Customization & Open Innovation, shares his thoughts on the intersection of the Industrial Internet and mass customization.
“Manufacturing will really begin to drive business models,” says Piller, who has been leading the Technology and Innovation Management Group at RWTH Aachen University for a decade. Rather than regarding the Industrial Internet solely as an enabler of new business models, Piller sees the technological developments made possible by IIN and IIoT as “drivers of business models.” According to Piller, mass customization plays a pivotal role making this paradigm shift that manufacturing industries are already experiencing, more customer-centric.
“I see the question of manufacturing and the Industrial Internet being defined by two stems of debate; enabling operational excellence on a larger scale on one hand, and using Industrial Internet technologies to drive new business models on the other.” What mass customization makes possible via these two defining principals, is for manufacturing, supply chains and the business model inherent to them, to become more customer oriented. “The ultimate goal of mass customization is for manufacturers not only to become customer-centric, but more so customer-driven, to exploit the heterogeneity of customer demand,” says Piller.
According to Piller, manufacturers will often see the high variety of demand as a challenge, a cost driver and ultimately as a hindrance to maintaining a truly customer-centric manufacturing process. However, what mass customization does, notes Piller, is turns this assumption on its head.
“We should instead see high variety of demand as a profit driver, and do so by allowing for the input of each individual customer at the beginning of the value and supply chains. This doesn’t entail reinventing engineering to order process or craft customization, but doing this with an industrial efficiency that the latest Industrial Internet technologies make possible.”
For material handling, the integration of automated and semi-automated robots into production lines is a big driver for coping with higher degrees of variety, says Piller, who also sees mass customization as something already being utilized in material handling equipment. “A lot of material handling equipment is already engineered to order, meaning it’s highly modular and therefore can fit into existing plant layouts, as well as be integrated into planning and production. Deploying this in larger volumes is the next step.”
“A lot of material handling equipment is already engineered to order, meaning it’s highly modular and therefore can fit into existing plant layouts, as well as be integrated into planning and production. Deploying this in larger volumes is the next step.”
From prediction to action
Closely linked to the paradigm shift taking place in manufacturing are the opportunities that predictive analytics opens up. Piller sees these opportunities as something material handling companies should be taking advantage of and implementing in their systems. “As the basic premise of predictive analytics is that we must guess less, and know more, an implication for a material handling company could be making better forecasts of the incoming flow of material.”
A consumer goods company will traditionally do some market research or extrapolations of the first few weeks of sales, in order to see how sales will develop for the rest of the season. “Now they can get access to much more unstructured data from social media conversations or purchasing behavior in key stores, and thus better predict the operational planning necessary to meet the demand,” says Piller.
However, as with most new data related developments, predictive maintenance and analytics don’t come without potential pitfalls. Piller appropriately sums up the paradox surrounding predive analytics and maintenance, by stating, “the better we are with predictions, the worse we become in executing.
“Imagine a huge plant that has many material handling systems across the globe, and let’s say they are all assessed using predictive maintenance. The plant manager will then know ‘ok, in a weeks’ time, 20 out of my 1000 pieces of equipment will breakdown, and I only have 2 repair teams. How do I allocate them?’ Therefore, action as opposed to prediction is the ultimate goal.”
First an app, then a platform
Another significant development that will only increase the capacity of the Industrial Internet to create new customer-driven business models, is the emergence of the platform economy. However, according to Piller, traditional industries should not be looking to immediately develop a platform as the likes of Amazon and Uber have. For instance, the transportation and material handling industries would benefit by starting off with an app.
“Of course, managers think that ‘we will become a platform,’ but this requires a big mental shift in companies, a shift towards openness. However, I think that traditional industries should first acknowledge the possibilities an app introduces to their business. In a connected world, an app can be a piece of equipment and shouldn’t be limited to the notion of a smart-phone app,” Piller notes.
Becoming a platform-based industry certainly doesn’t happen overnight. What Uber or Amazon managed to do on a consumer level, would be extremely difficult to successfully execute in the industrial world, simply because of the level of openness it requires. For Piller, more companies need to recognize the benefits of an app.
“Established B2B companies are very conservative when it comes to putting their data in a platform, so even if a platform is created by an established player, filling it with meaningful data is a question on its own. Therefore, in terms of market entry, being an app on a platform has a lot of advantages. My advice would be to learn how to become the preferred app, like the Angry Birds of material handling.”
Experimenting for future solutions
Piller is confident that companies experimenting even with more left-field utilizations of the Industrial Internet will ultimately drive innovation, and do so in a customer oriented way.
“Take the Amazon Dash Button, a solution which costs the consumer $4.99. At that price point, even a small established company can start experimenting by asking, for example, ‘what could we do, if we managed to increase the connectivity between equipment that allows you to monitor actions and actives?’”
According to Piller, the issue some managers and CIOs face is making sense of the huge pile of possible things to do – and sometimes they end up doing nothing.“Therefore, I think it’s always better to start experimenting and testing assumptions in order to get real feedback, instead of making huge PowerPoints,” he concludes.
Frank T. Piller works as Professor of Technology & Innovation Management at the Business School of RWTH Aachen University, Germany
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The most read articles of Industrial Internet Now in 2016
The past year has further matured the solutions in the field of the Industrial Internet, and also sparked a great deal of thought-provoking discussion and articles. Some of the most discussed topics this year include IT/OT convergence, change management in digitalization, and the future of manufacturing. Below are the five most read articles of Industrial Internet Now in 2016.
5. Semantic interoperability opens doors to the digitalized world
Dr. Richard Soley, the Executive Director of the Industrial Internet Consortium, expressed his skepticism towards a universal IoT standard, stating “I’ll be glad to be the first one to say that there will never be a single universal standard”. According to his view, instead of aiming for a universal standard, achieving interoperability is a much more important issue to tackle.
4. Building an insight-driven business
Having huge amounts of information is one thing, harnessing data to create new business models is another, stated Alun Jones, Data Scientist at Konecranes. In his article, Jones discussed about utilizing machine learning, understanding security issues and improving operational efficiency as steps in building an insight-driven business.
3. Managing change in the connected workplace
Alexander Reay, Chief Digital Officer at Sodash and President of the Nordic IT Association, explored the role an organization’s structure and culture play in maximizing IoT’s potential for businesses. He also focused on the leadership issues that need to be addressed during this transformation.
2. Five steps to digital innovation
First you need to map and prioritize the needs of your organization. Technology comes in second. But what came next? Marko Yli-Pietilä, the Business Development Director and Managing Consultant at Midagon, walked us through the five stages of successful digital innovation.
1. Future manufacturing moves from global to hyperlocal
Economies of scale are diminishing and the threshold for manufacturing products are getting lower. In the future, we might end up with small production facilities producing limited batches of products for highly specified markets. The most read article of Industrial Internet Now in 2016 shared the thoughts of Risto Linturi, Executive Catalyst and Chairman of the Board at Sovelto, who believes that the ways in which we manufacture will change drastically in the future.
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2017 will be about data ownership and security
Whether it’s the sensors or it’s the platforms people are using, the Industrial Internet market has become much more mature overall in 2016. Good examples of monetization have emerged, demonstrating that organizations have begun leveraging capabilities and competencies and turning these opportunities into something that could positively affect their bottom line. Juha Pankakoski, Chief Digital Officer at Konecranes, assesses the key developments of the past twelve months and looks ahead to the themes and possible breakthroughs of 2017.
Companies that have been in this area for quite some time are now starting to leverage value from the data that has been collected. It’s one of the things we assumed to happen already a couple of years ago, but are only now seeing to realize. Once you have proper quality data to mine for information, you notice that there are several additional layers of value that you can start generating by aggregating information from multiple sources.
Connectivity and intelligent machines are somewhat breaking the boundaries of traditional industries. On a number of fronts where automation has already been set up, for example, the speed at which it is possible to gain insight from machines is increasing at a significant pace. Some of the very successful cases have come from industries where assets are quite remote and not that easily accessible, such as the vessels or oil rigs. New solutions now allow remote support teams to better understand what is happening in the field and help local teams using different augmented reality and virtual reality (VR) devices. This is definitely something that is proving to be exciting.
The industry that is really providing excellent opportunities for others is the automotive industry, with connected vehicles and automated driving becoming more and more commonplace. Companies with billions of dollars in revenue are developing all these capabilities and putting them in place, making them more cost efficient at the same time. These solutions are then deployed in many other areas and industries where automation or “sensing” is required.
Lessons from hackathons
From our perspective, all the hacks we have organized have taught us something. In the case of the recent Maritime Hack, we encountered practical challenges associated in combining open and closed data. We found out that it is still much more challenging today than perhaps expected to get all the parties inside the port to share data with each other, despite sharing the same customer and the same objective. There are practical restrictions – legislative, contractual, but also artificial – and still quite a number of concerns in the open data sphere. This isn’t entirely unexpected, but it’s still somewhat disappointing because it’s by combining those different data streams and different actors which is required to arrive at additional value. There’s still some way to go before net openness can be achieved.
Remember that hackathons aren’t hacks as such. They deliver varying results based on the input, expectations and preparation that companies have put into those sessions. Some companies we know have had very limited success and varying results from the event. You take a risk when going into an event like this that doesn’t have a definite, specific outcome in mind. When you go in to meet new companies and new people with new capabilities, you can either be pleasantly surprised or find out that a company’s capabilities may not be exactly what you are looking for at that point in time.
As for possible breakthroughs in 2017, one of the things we will see is VR or enhanced reality-devices being used in field service operatives’ day-to-day industrial work.
Themes of 2017
2016 was the year of analytics. Data ownership and security will be very appropriate themes for 2017. Security is an underlying topic that can’t be avoided. We are already seeing connected devices that are being used for unintended purposes, such as DDoS attacks. We are also seeing several other areas where connected cars have been manipulated from a distance. It’s an unfortunate fact that every new machine or item that is connected or “smart” in one way or another is subject to hackers coming in, breaching security restrictions, and using them for unintended purposes. In an industrial environment, such situations can be hazardous.
Preparedness for this is not a straightforward or easy thing to do as it requires that you have security built into your architecture from the very beginning. Companies should either redesign their solutions or build additional layers of security into their solutions, so if something does happen, the machines can be safely ramped down to avoid an adverse effect.
In terms of business opportunities, the big potential is in data and the sharing of data. Going back to the hack itself, it is expected that we’ll see more and more collaboration between parties in terms of sharing data and information across customers and customer premises. The sharing of data and of knowledge – be it between the machines themselves or between the databases that contains the information – will then be used to generate new business cases. Interoperability and communication between machines and processes is something that will greatly profile 2017.
As for possible breakthroughs in 2017, one of the things we will see is VR or enhanced reality-devices being used in field service operatives’ day-to-day industrial work. These devices will allow the user to get support from the back office and from the applications that can deliver additional data, information or material related to the job in hand. In this area, we will see many interesting developments. There are many solutions in the pipeline, and I would be a bit disappointed if we didn’t see several viable applications that can truly be used in an industrial environment.
We may also see some interesting announcements from large software companies on how they plan to develop and combine their IoT offering with more traditional software packages or cloud services as bundles. Whether its software or machinery, I see companies building on what they already have in their portfolio to provide a platform to develop their capabilities to the next level.
Juha Pankakoski works as Chief Digital Officer at Konecranes
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Pressing global challenges and disruptive technology will radically reshape manufacturing
The Global Manufacturing and Industrialisation Summit (GMIS), a world-first gathering of global governments, manufacturing businesses and civil society, will be held in Abu Dhabi next year, according to an article on Mubasher. Innovations in the manufacturing sector are set to have a transformational impact on the global economy and GE predicts that the industrial internet market will grow and lead the way in improving competitiveness and creating new jobs.
As the digital and physical sides of manufacturing converge, advanced technologies are becoming more essential to competitiveness. According to Anil Khurana, PwC Partner, capital-intensive heavy manufacturing, like oil and steel production, is getting a boost from new manufacturing and information technologies emerging from the hi-tech sector.