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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An IoT platform for material handling
The IoT is all about using sensor data to make better decisions. For decades, warehouse management systems have relied on scans from barcode scanners to confirm floor level activities have occurred correctly. In the last ten years, other automatic identification technologies have achieved the same types of process reliability.
According to an article in Forbes, material handling systems are a natural generator of sensor data. Contributor Steve Banker cites SensorThink, a digital platform for the connected warehouse. Making its debut at ProMat, the leading material handling conference in North America, this platform includes a warehouse control system, a digital platform for capturing IoT data, and Cloud analytics for analyzing the data.
“The digital platform collects the IoT data, cleanses it and harmonizes it. The data can come from material handling systems, lift truck sensors, building automation systems – which control the temperature and humidity of buildings, and security systems,” Banker writes. SensorThink compresses massive amounts of data by only collecting change of state data.
Read more about new potential for analytics and optimization in warehousing at: https://www.forbes.com/sites/stevebanker/2017/04/05/an-iot-platform-for-material-handling/#77581e9b1182
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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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How to use plant floor data to make smart strategic business decisions
In the case of HK Metalcraft, a manufacturer specializing in precision metal stampings, IoT has made possible the harnessing of plant floor data. “Connecting the plant floor to HK’s business operations through cloud ERP turned that data into actionable information,” according to an article published by Industry Week. The piece is based on a White Paper published by US software company Plex.
When coupled with what happens on a plant floor, a cloud ERP solution enables “the kind of insight and control manufacturers need to make critical business decisions.” Cloud ERP has allowed HK Metalcraft to manage the downtime of operators and see everything from the direct overhead down to the specific amount of time that each operator has spent doing a specific job. “Now not only does HK Metalcraft know exactly what caused the downtime but they also have actionable data to improve processes and overall equipment effectiveness.”
Read more about how HK Metalcraft turned data into actionable information at: http://www.industryweek.com/cloud-computing/how-use-plant-floor-data-make-smart-strategic-business-decisions
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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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How to build Big Data competencies
There are two separate topics to consider when it comes to Big Data. One involves finding a solution to an existing problem or challenge. The second has to do with building something new. Matti Vakkuri, Head of Technology, Internet of Things at Tieto, outlines ways in which companies can build their Big Data competencies. He also discusses the aggregation of unstructured and machine IoT data in a manufacturing context.
“If we narrow the discussion to cover only the data drivers of Big Data, we are going in the wrong direction,” says Vakkuri. As an example, in B2B operations in the maritime industry like port optimization or fuel consumption optimization in vessels, data delivers huge benefits for a shipping business. “Less polluting engines – or ones that generate more power from less consumption – could be designed based on data that is collected. This would affect productivity and competitiveness because shipping is a volume business.”
Vakkuri adds that this data needs to be utilized so that all the information isn’t amassed for the sake of it. “You always ought to build something with or on the data. This brings an opportunity for data-driven firms,” he continues. “In nuclear power plants in Finland, there’s a regulation that data has to be stored for a relatively long period. Before Big Data, it was mainly in passive storage only. With Big Data, however, all data can be online. This means that we can do research based on the data that already exists.” Vakkuri points out that this is one of the paradigm shifts – that instead of having inaccessible passive data, we now have available data from which we can build analysis, business, opportunities and innovations.
Amazon is a good example. “The truly remarkable thing about their business is that they see who is checking their products, putting them in their basket but not buying them. Checking but not buying – taking things out of your basket and not proceeding to check out – these send very significant signals. Big Data allows this to be done, but it must be understood that Big Data is also about analytics and machine learning,” continues Vakkuri. The same kind of implementation that is being applied in webstores can be also be used in brick and mortar retails stores using indoor positioning.
Tieto’s Intelligent Building Product Line is another case of effective Big Data usage. “This allows us to establish who is where in our new headquarters at any given moment. As long as I have given my permission, for instance, I can be followed around the office. Knowing where co-workers are at any time helps us to be more collaborative,” shares Vakkuri. Beyond determining where people are, the company is also gathering data to optimize and remodel how space is used in their offices.
A question of ethics
To CIOs and CDOs keen on building Big Data competencies in their respective companies, Vakkuri offers this piece of advice. “The answer is really simple: multi-disciplined people. You need technology guys who possess not just domain competence but also ethics. There must be somebody who is capable of understanding ethical aspects and good manners not based on gut feeling, but rather on an understanding of the logic behind whatever ethics is used.” Ethics, Vakkuri notes, is vital in technologies such as AI and Big Data.
“Another thing is that you need a team of people in which every person is different, and from a different background, but who are able and willing to build things together. Never hire just one person to be a data scientist, but a team made up of individuals with a variety of experiences, ages and educational backgrounds, and who come from diverse cultures.” He believes that the more heterogeneous your team is, the better.
“Never hire just one person to be a data scientist, but a team made up of individuals with a variety of experiences, ages and educational backgrounds, and who come from diverse cultures”
Combining unstructured and machine IoT data
“Let’s start with instruments: Instrument everything. This means – as Twitter co-founder Jack Dorsey once advised – log, measure and test everything. Store everything, store all relevant data,” suggests Vakkuri. “If you install video cameras in your factory, you don’t need to collect the data of the raw image, but more so the metadata of what is in the image, timestamps, location data, etc.”
He would like companies to forget the notion that storing and analyzing data is expensive – they really are not nowadays. “The paradigm has changed so that infrastructure costs are down, so invest in human capital and in people who can analyze the data. But if you want to conduct a comprehensive analysis, you need to combine the data sets together which has never been done before, so be provocative and think outside the box. While you’re at it, forget the box altogether.” He adds that this also depends on how open-minded and competent your employees are, and what types of ideas they propose. “In the end, it’s all about what innovations you can get to with the data.”
On Big Data and machine to machine implementation
As far as Big Data and machine-to-machine (M2M) implementation are concerned, Vakkuri has this to share with manufacturers. “M2M communication needs to be more standardized. This also means using the right tools for the right purposes. How do you know what’s right? Only by building proof of concepts and then testing,” he explains.
Vakkuri likewise recommends establishing a partnership involving the research side – such as universities and similar institutions – within the ecosystem. “Carrying out a research collaboration usually means that your costs will be lower when developing something new and quite often new innovations come from the applied research side.”
Last, data quality and information security are two other points Vakkuri would like to underline when it comes to M2M communication. “Information security needs will be more complex. The more machines and data are connected, the more challenges will arise in terms of information security.”
Matti Vakkuri works as Head of Technology, Internet of Things at Tieto.
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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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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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Think beyond the cloud – How platform players can prepare for the next phase of the IoT
Many companies are already combining advanced automation, cloud computing and IoT services, among others, to transform their operations. In terms of the maturity of industrial ecosystems, however, Marianne Hannula, Head of Service Product Management and R&D at ABB, believes that there is still the need to establish business models that fully support the technology. She talks about what needs to be done to usher in the era of global supply chain ecosystems and how to take full advantage of its build up phase.
“I first came across machine-to-machine communication more than 15 years ago when I was working with mobile internet applications; a lot has happened since then,” shares Hannula. “All this technological advancement has built momentum for the evolution in industrial ecosystems. These now refer to ecosystems that manage and control the performance of multiple pieces of equipment that form the supply chain, not just order-type data transfers.”
The different players in the field have been somewhat focused on their own operations and that has, in certain cases, slowed the development of new ecosystems. According to Hannula, ecosystems that offer a win-win situation for both parties are needed. “In the case of IoT, this should mean win-win-win-win for multiple parties, not just two.”
Data ownership as a matter of agreement
Hannula sees positive signs in that the time has come when these groups – not just the few early adopters and startups – are beginning to be more open to collaboration and data sharing. “That openness is needed to enable global supply chain ecosystems to develop, where data from multiple sources are combined for the improved performance of the supply chain and for the benefit of end users.”
As data ownership is more complex in an IoT context, the topic has given rise to various opinions. Hannula sees it as a question of what has been agreed upon. “Basically all players should own their data. Sometimes access to this data might be the sellable item. The question arises when the data facilitates new knowledge as it is analyzed and/or combined with other data,” she continues. “Who then owns this knowledge? Who should be held accountable for the consequences when it is used and something adverse occurs? I view this as a matter of agreement, but the responsibility has to be clearly defined between the stakeholders.”
‘Don’t wait for tomorrow’
With regard to IT and OT convergence, Hannula advises companies to not wait for tomorrow. “Use your existing IT structures and find the connectivity means to combine the data available from multiple devices and systems to enable better business decisions now. With that you will learn and find out where the biggest benefits for your business are.”
In addition to the ability to perform predictive maintenance, Hannula believes that the other clear-cut benefits of the Industrial Internet involve the optimization of performance and quality as well as reliability.
“With real-time data turned into knowledge and insights, we are able to detect topics where the process productivity can be improved and become more efficient. Just saving a second or two in some repetitive process can translate to considerable additional revenues,” she explains. “If that happens in one part of the supply chain, the benefits that can be available at the supply chain level can be even greater. Another example could be automatically combining data from various sources and using that to fine-tune equipment performance. This could lead to savings and better quality.”
“When one talks of the IoT, you often only hear the word cloud. But you also have to consider how these clouds can start talking to each other. Then apart from these clouds, there are storms, sunshine and stars.”
Advice for platform players
Hannula believes that there will be a major build up phase for IoT ecosystems during the next five years. “More and more industrial companies will get involved, there will be more and more cloud solutions, and connections between different cloud platforms will become available. The technological knowledge collected over the years and currently stored in documents – or even only in the heads of experts – will turn into a more automated format, making it scalable and useful to business.”
To take full advantage of these changes, she says that companies should focus on three things. “First is that the future starts today. Start using what is available now and learn from it. The next thing to remember is that IoT is not a game of solitaire: Experiment and collaborate with other players. The third and last is don’t wait to be disrupted. Be open to the idea of adopting new business models and remember the human aspect of all this. Instead of considering something as a threat, embrace it as an opportunity and be curious from a technical perspective.”
Above all, Hannula recommends that platform players widen their general outlook. “When one talks of the IoT, you often only hear the word cloud. But you also have to consider how these clouds can start talking to each other. Then apart from these clouds, there are storms, sunshine and stars,” she concludes. “The cloud is a simplification of a technical aspect, but to gain the full benefit of IoT a broader view is needed.”
Marianne Hannula works as Head of Service Product Management and R&D (Portfolio Management, Development, IoT) at ABB.
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