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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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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Integrating IT, OT and security: convergence to collaboration
“OT and IT are working towards the same goal, uptime,” writes Daniel McGinn of Schneider Electric. “Whether that means the comfort of building occupants and business continuity or the specific needs of safety and security.” In part one of a two-part blog entry, McGinn highlights the integration of IT and OT, and what that means for security. Companies developing IoT solutions must consider the implications these new systems, applications or even platforms have in terms of security.
“Keeping buildings operational, IT running and security systems up, depends on the availability of the network and server platforms they are running on, and these systems are only as available as the power supporting them. Because these systems are increasingly connected and open, the departments themselves must come together as well,” notes McGinn. “This is where safety and security now begins for any structure that requires active monitoring and access control.”
Read more about the risks and management challenges of IT environments at: http://blog.schneider-electric.com/datacenter/power-and-cooling/2017/01/04/integrating-it-ot-security/
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Trends and themes from the Industry of Things World USA Survey Report 2017
Findings from the Industry of Things World USA Survey Report 2017 reveal that compared to Europe, the US is very advanced on the software side and IoT platforms, analytics tools, as well as AI approaches. Maria Relaki, Group Production Director at we.CONECT Global Leaders, the organizer of the Industry of Things World conference series, talks about the key results that shed light on the current state of the American IoT market.
A clear majority of the cross-industry IoT and Smart Manufacturing managers that participated in an online survey by Industry of Things World – conducted from August to October 2016 – considers Smart Manufacturing as the main driver and contributor for US manufacturing competitiveness.
“One interesting fact from this year is that 71% of the respondents actually have industrial IoT or Smart Manufacturing systems in their organization, compared with 41% who rated the importance of IoT project implementations in their companies as very important last year,” states Maria Relaki, Group Production Director at we.CONECT Global Leaders.
Relaki adds that another key finding is that access to necessary infrastructure is rated as the biggest challenge to implementing IoT within companies (53%), followed very closely by cost (46%). “The positive news is that in 2016 the biggest challenge was ‘uncertain ROI/ lack of business case’ – so here it seems we have had some significant progress. The business case is now a given and it’s a matter of making it happen,” she says.
The survey also reveals that Smart Manufacturing systems are not applied only on a machine (32%) or plant level (43%), but they are being more and more integrated across all levels (36%) within a business.
Challenges and opportunities
By far the biggest opportunity that IoT offers has been recognized as the increased efficiency (68%) that comes with smart manufacturing systems. “As the second biggest opportunity, respondents listed the competitive advantage that IoT can offer as well as, very interestingly, that it can increase product quality,” continues Relaki.
Access to necessary infrastructure is rated as the biggest challenge to implementing IoT within companies.
According to survey participants, some of the biggest challenges they face involve the lack of standards and interoperability, and costs associated with the integration of new systems. They also cited security breaches – that IoT needs a new security approach rather than the traditional one, for instance – and management buy-in as other factors that affect the implementation of industrial IoT technologies in their organizations.
Relaki believes that the industrial IoT landscape is going through a transition. Shifting industry boundaries are changing competition, and businesses need to be aware of that. “Traditional competitors need to look beyond their universe and keep an eye on how IoT technologies can enable other businesses to eat away parts of their market share.”
The interoperability of connected devices in the world of IoT is still a big issue, one that is subject to discussion. “How far away are we from a universal standardization? Discussing the implementation of open source and open standards might be a way to move into a direction with fast results,” offers Relaki.
While robotics is a theme that is becoming more and more relevant, she says that the human factor in all of this must not be forgotten. “The Internet of Things involves new ways of thinking about how humanity and technology can cooperate differently when ‘things’ get smarter. Augmented reality and virtual reality in manufacturing simulation, as well as M2M and Artificial Intelligence for improved productivity, will be discussed throughout the conference.”
Industry of Things World USA 2017
Organized by we.CONECT Global Leaders Industry of Things World USA is an international knowledge exchange platform where over 500 high level Industrial Internet of Things executives will meet. Scheduled to take place in San Diego, California from February 20 to 21, 2017, this year’s two-day program aims to encourage and inspire participants to rethink their technology and business strategy for scalable, secure and efficient IoT, from cloud, robotics and automation to standards, interoperability and security.
“We will have the pleasure of hearing from Alex Tapscott, a blockchain expert, on the impact of Blockchain on the Industrial Internet and how this will change the way we do business. Jeff Burnstein, President of the Robotic Industries Association, will discuss how robots in a smart factory can use self-optimization, self-configuration and artificial intelligence to complete complex tasks in order to deliver vastly superior cost efficiencies and better quality for goods or services,” shares Relaki.
At the same time, the event will attempt to demystify the complexity of getting started with integrating robotics into an IIoT network. “Small and medium sized companies in particular may be overwhelmed by jargon, fears about cost and the difficulty of knowing how to apply these technologies, so these talks will hopefully be of use to them in understanding how to explore robotics and IIoT further.”
To find out more about the agenda and speakers of Industry of Things World USA 2017, visit http://industryofthingsworldusa.com/en/.
Download the full survey report:
Maria Relaki works as Group Production Director at we.CONECT Global Leaders and is responsible for the Industry of Things World global event series.
Image credit: we.CONECT Global Leaders
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
Image credit: chombosan / Shutterstock.com
A wider view gives more accurate results
Taking a step back and analyzing processes from a larger perspective might take you to surprising places, such as a dinner table in a Chinese household, says Petri Asikainen, Director, Product Development at Konecranes. According to Asikainen, to get the most out of your production processes, having a wide view of the process in hand is crucial. By seeing the processes as a whole, monitoring them as widely as is possible and by adjusting the production facilities’ metrics accordingly, noticeable boosts can be gained in the total output.
Industrial monitoring is going through big changes. From the variety of ways in which equipment in an industrial setting can be monitored, to the new possibilities in remote monitoring and -operation, operators in facilities have gained new ways to operate efficiently. A great example of this can be found in the context of waste processing.
“We were asked to optimize the operating activities in a certain waste management facility. We noticed that once we installed a Remote Operating Station for the crane operators in the power plant’s main operating room, suddenly the old local operating room over the waste bunker wasn’t the number one choice for working anymore,” Asikainen says.
Having all the personnel operate the plant from one location allows for better communication between the operators in charge of different parts of the facility. It also offers noticeable savings for companies, as there’s no need to build additional local operating rooms just to be physically present for the operation of the cranes anymore.
Monitoring everything there is to be monitored
In many industrial operations, the crane is in the center of the production process. This unique position allows for the possibility to gain deep insight of the production process.
“The entire material flow in the facility might be dependent on the crane, and this gives us plenty of opportunities to create different types of insights for customer’s needs. One example, found in the context of the paper industry, is that we can better identify where reject appears. This is valuable information for the manufacturer, and if it can help to improve the material efficiency of the manufacturers process by half a percent – it might already cover the cost of the crane data gathering capability and analysis work with extremely short pay-back time,” Asikainen explains.
The cranes’ movement patterns can also be tracked in the production facilities, so that the biggest bottle necks can be found. This tracking also helps map the actual material flow. If a load is moved from one place to another several times back and forth, the whole process slows down.
“Existing manufacturing facilities continuously face the need to respond to the global race for lowering costs and improving efficiency. A thorough analysis of the material flow can help improve production. Through it, we can find out what kind of crane setup would suit the client’s process, a result which is based on the actual measured data. When one is considering rebuilding an existing facility, this kind of efficiency analysis is a good tool to define the profitable targets to invest in.”
As to why Asikainen ended up practically monitoring how a household dines, he uses it as an example of how processes can be optimized in various, wider ways.
“We discovered that the loaders used in the waste facilities in China were continuously moving heavier loads compared to their European counterparts. One reason for this was that the households use a large amount of oil in cooking. The residue ends up in the trash and then to the waste processing facilities. This has an effect on the raw material, making it finer and increasing its energy density,” Asikainen explains.
This has a direct effect on how the whole process is set up and the crane is optimized. When the operators have a better view on the type of waste coming in to the facility, the effect that the differences in waste have on the energy output can be taken into account better.
Benefits of monitoring
When asked about the benefits the increased monitoring brings for companies, Asikainen brings up the similarities between lift trucks and cars. Both have similar concerns, such as tire leaks. For both, leaks can be monitored and fixed faster through monitoring. Early reaction to low tire pressure decreases extensive wearing and improves safety.
The operability of cranes develops in similar trends as cars – functionalities which 15 years ago could have been sold only to extreme needs, are now common even in the most value-focused cranes.
“Snag prevention is an example of this. It automatically stops crane movement if a hook, a sling or a load accidentally gets caught on something. Having real-time information on both the environment in which the crane is operating as well as the loads that they are moving, has made it possible to halt the crane if something gets caught in the way”, Asikainen says.
Hook centering is also an effective technology to improve efficiency and safety. If you lift a load and the hook isn’t centered, the load starts to swing as it’s lifted off the ground. The hook centering positions the crane above the hook, eliminating a possible human error. The hook is where it is supposed to be before lifting the load.
Maintenance by demand
Another way in which the increased monitoring can be utilized by companies is by making maintenance more effective. When you have sensors measuring thousands of points of data, you can efficiently both prevent halts as well as optimize maintenances routes.
“With the increased amount of information, technicians can focus their attention to issues needing extra care. The crew can also receive info on which manuals, tools and parts they must have with them beforehand,” Asikainen says.
All in all, maximizing the improvement through monitoring is dependent on two things – both the gathered data and the insights. Through having both, companies can achieve a more holistic view of their process, one which is based more on the actualities of the operating environment, and not just on subjective, professional guesses. This makes the whole manufacturing process more reliable and transparent.
Petri Asikainen works as Director, Product Development at Konecranes.