The need to connect legacy devices to the IIoT
Pre-internet assets lack the connectivity of newer pieces of equipment. These legacy devices, however, still have years of remaining value if they can be linked to the cloud, enabling their data to be analyzed and revealing actionable insights that could perhaps potentially transform business. Wael Elrifai, Sr. Director of Enterprise Solutions at Pentaho, offers insights on how older systems can be made to work with current ones, and talks about the human side of machine learning.
Businesses that have operated for a considerable amount of time will have accumulated several legacy systems over that period. While they have long life-spans, few of these machines will be immediately compatible with one another. The cost of replacing these pre-internet assets to facilitate communication could easily outweigh foreseeable production benefits. What steps must plant managers then take to combine AI capabilities with legacy infrastructure?
— IndIntNow (@IndIntNow) June 1, 2017
Wael Elrifai, Sr. Director of Enterprise Solutions at Pentaho, begins with this premise. “I usually like to remind people that we talk as though data was not generated in the past on these systems. Remember, however, that there’s a lot of robotics involved already, and these systems have sensors that have been producing data for decades. The truth of the matter is that systems such as PLCs (programmable logic controllers) and SCADA (Supervisory Control and Data Acquisition) have already been capturing that data. What you need to do now is to pull the data off those systems. Things like data integration tools are built for that,” he says.
According to Elrifai, predictive maintenance – a technology that drives value in modern manufacturing – isn’t new either. “The difference today is, because the cost of computing and the cost of storage have dramatically reduced, you can do more with it. It’s been a nice positive feedback cycle: Where you can capture more data, you can do more computing work – applied mathematics, machine learning and AI, among others. This then makes capturing data more valuable.”
Conversely, in situations where it’s entirely mechanical and no data or robotics are involved, he suggests looking for proxies for that data. Elrifai adds that while some retrofitting may be required, from his experience a lot of data is already there and is not being used, so it would be best to begin with that.
IIoT implementation across industries
Some industries are more advanced when it comes to IIoT implementation, while others lag. To convince traditional manufacturing companies of the economic benefits of AI investments, Elrifai offers the following examples.
“The ports industry is already using complex machine learning techniques. The most common one for logistics companies is simulated annealing, a method for schedule optimization that sees to it that cranes are doing the right thing at the right time, and containers are moved according to the right schedule.” Elrifai believes that for some container terminals, it’s mostly about the integration of the larger supply chain.
On the other hand, he recalls a visit to a steel factory that wanted to improve its efficiency. “A couple of times a day, they experienced a very specific kind of failure. This cost about 10 percent of their productivity, and in the steel industry that figure is enormous,” Elrifai explains. Furthermore, the way they knew there was a problem was rather unusual: The control room would start shaking.
While the company wanted to reduce this through predictive maintenance techniques, what they didn’t know was that they were capturing all this data already. Elrifai says like many other companies, this particular steel factory would only look at five, ten or 20 variables, the ones that were in their SCADA system.
“What they didn’t do is integrate this with thousands of other sources. The statistical techniques that factories are doing today are low-dimensionality ones,” he continues, adding that trying to convince groups to do more is a matter of explaining to them that it’s just an evolution of what they are already doing.
Where humans fit in
Another dimension to machine learning and AI is the human factor. As far as the supply chain is concerned, Elrifai is of this opinion. “If you are talking about supervised learning – just prediction, basically – oftentimes the baseline data that you use to train these systems is from humans. And you want these systems to evolve, because systems evolve, factories evolve,” he says. “I think humans are always going to be there, helping to state what the ground truth is. Or, at least for the foreseeable future, they will be doing that.”
In addition, Elrifai points out that in certain cases in factories, several different algorithms are voting whether something is going to fail or not, and a human expert is doing that as well.
“With these kinds of methods – ensembles, if you will – you end up with better outcomes. For instance, the machine by itself might produce 75% accuracy and the human on his or her own might produce 68% accuracy. When you put them together, you end up with greater performance, say 80% or 85%,” he states. “I think there’s still a lot of room for cooperation. I don’t think the algorithms are taking over just yet.”
How to solve new problems
“The common problem people have with technology is that they search for problems. That makes no sense. Solution? Start with use cases.”
Finally, Elrifai – with his background in data science – offers this essential piece of advice to companies that plan to connect legacy equipment to the IIoT. “The common problem people have with technology is that they search for problems. That makes no sense,” he emphasizes. His solution? Start with use cases.
“I think there’s a sense that this is extremely expensive to do. However, all you really are doing is putting up a basic data engineering or basic machine learning infrastructure – this is low-cost. There’s a lot of automation available now around machine learning,” states Elrifai. “In the data world, when you try to build models, about 80-90% of the effort that is put in is made up of data engineering, feature engineering, preparing data, filtering – all the easy stuff.”
Elrifai believes that a lot of the data prep for data engineering can be done in an automated fashion. “I don’t think people recognize that. They are trying to use old tools to solve new problems,” he concludes.
Wael Elrifai is an author and speaker. He works as Sr. Director of Enterprise Solutions at Pentaho, a data integration and business analytics company with an enterprise-class, open source-based platform for diverse big data deployments.
Keys to effective IIoT design
How does the IIoT change the way industrial products and services are designed? What types of opportunities should companies seize and which challenges should they expect to tackle? Petri Asikainen, Director of Core Technology Development at Konecranes, shares his insights.
Machines report on their usage and condition. Maintenance interaction data is combined with equipment engineering information. Remote-controlled cranes. Logistics chains are truly transparent, as each link is programmed to anticipate the next steps, optimizing flow across the entire chain.
As low-cost sensors and powerful software turn the IIoT from buzzword to reality, the opportunities for new product and service design seem endless. But where should companies start?
Focus on customer value
“The starting point for successful design is a deep understanding of the client’s processes and needs.”
According to Petri Asikainen, Director of Product Development at Konecranes, the key to great IIoT design involves taking a step back from all the cool things that technology can do, and focusing on where it can offer clients the most value.
“Buyers in industrial companies are usually highly rational. Most of them do not care for fancy new gadgets or features just for the novelty of it,” says Asikainen. “The starting point for successful design is a deep understanding of the client’s processes and needs. What is essential and useful for them? Can you make significant improvements in efficiency or work safety? How can you make their life easier?”
Sometimes the solution might not be selling new equipment, but rather retrofitting old equipment with sensors that connect it to intelligent networks to extend its lifespan. Or, it may not mean new sensors but developing software to utilize the sensor data already available in a new way.
“When you have a solid understanding of the user environment, there’s quite a lot you can do by developing the software instead of adding new hardware or sensors – and it’s often more cost-effective too,” he continues.
Design products that communicate
It’s important to note that in the IIoT environment, your products are just small pieces of a larger network. To offer real value, they must fit into, communicate with and improve the client’s existing system.
“With any type of new product, you need to ask the basic questions: How will it communicate with its surroundings and other intelligent systems like enterprise resource planning software? What type of data should it gather and for what end?” explains Asikainen.
Not all data is equally useful either. With increasing amounts of automation, and more smart machines and programs being used at industrial sites, the human operators’ ability to handle new information is often already at its limit. As Asikainen points out, “Especially in user interface design, you should be careful to present only the most crucial information at each given moment.”
Another aspect concerns the high demand for data security in the industrial setting.
“Unlike on the consumer side, you cannot monetize customer data by sharing it with third parties,” Asikainen says. “Data security is a huge issue in IIoT design, as the possible risks with data breaches are often severe.”
Use data to deepen understanding of client needs
“With a clear focus and insight into client operations, IIoT solutions can produce tangible gains for most industrial clients.”
While there are challenges in designing for the IIoT environment, there are also huge opportunities. For one, the influx of new information allows designers to form a deeper understanding of clients’ real needs and to offer them optimized solutions.
“Before, we had an incomplete view into how clients actually use our equipment and services. Now, we can start the design process from facts and real-life data. That is a great advantage for both sales forces and product development.”
In practice, this can mean creating new service concepts or equipment models that fit the clients’ use patterns more precisely. Or it could include offering complementary services that improve safety and quality such as staff training sessions on how to use the equipment efficiently.
The good news, says Asikainen, is that with a clear focus and insight into client operations, IIoT solutions can produce tangible gains for most industrial clients.
“In heavy industrial settings where production volumes and material flows are large, optimizing equipment and tweaking processes with the help of new data can bring in significant savings,” concludes Asikainen.
Petri Asikainen works as Director of Core Technology Development at Konecranes.
Securing IIoT systems still a contractual no man’s land
The industrial internet is a continuously evolving and layered infrastructure built on connected machinery – a large proportion of which has not previously been linked to the internet. The fact that these machines can now be accessed online brings new challenges for IoT service providers as well as their clients. Furthermore, questions remain regarding responsibilities, says Pasi Vilja, Chief Information Security Officer at Konecranes.
Last year, a massive distributed denial-of-service (DDoS) attack swept through the globe and nearly disrupted the entire internet. Experts called it the largest attack of its kind in history. Afterwards, close investigation revealed that the assault had been orchestrated completely through IoT devices. A huge number of web cameras were left unprotected, and this offered an easy opportunity for hackers to mount a large-scale attack via the internet.
“This is a great example of the vulnerabilities born out of millions of unprotected devices suddenly being connected to the internet. As the number of internet connected devices continues to grow, new vulnerabilities also arise, bringing forth questions about internet safety which we haven’t faced before,” Vilja says.
The need for shared solutions to these questions is growing increasingly dire as more and more machines – many of which were designed before the advent of the IoT era – are connected into the internet, and operated in ways which couldn’t have been considered at the time they were made.
Implementing security measures in the era of IoT
According to Vilja, security in the context of the industrial internet can be implemented mainly through the same types of practices already used in securing computer networks. Keeping up a proper firewall, requiring identification, and constantly surveying and reacting to problems that arise quickly are important, as is updating software.
“The same principles work in both an ordinary IT context and an IoT environment. On the software level, there isn’t that much of a difference in how the systems can be kept safe in either setting. Still, the industrial context adds a layer of complexity to the equation,” Vilja says.
One of the greatest differences in terms of web security in an industrial context is the machinery’s long lifecycle, which brings forth new questions on service providers’ responsibility to offer their clients updates for extended periods.
“Some machinery in industrial use still run on Windows XP or even NT. For the former, support ended in 2014 – and for the latter, in 2004. How are we going to ensure that systems will be kept secure when some of the machines have lifecycles of 50 years? These are still questions to be discussed,” Vilja says.
Another issue comes up with the variety of machines being connected to the web. Industrial companies might have a combination of old, non-connected machinery which is now being connected to the web, point-to-point connected machines, and newer internet connected machines. When they open all these machines gradually to the internet, questions arise on how to make sure that no gaps are left between the different ways to connect.
Discussions about responsibilities still underway
Who has the ultimate responsibility regarding the IoT solutions in use and keeping them up to date? Is it the service providers? And if so, then how long and how actively do they have to ensure that the security is current? According to Vilja, these questions are still open for discussion, and no concrete best practices have surfaced yet.
“This is very much a discussion still to be had. Service providers must take responsibility to ensure that the services they offer are maintained to protect against new security threats. But only the clients know their full set-up and probably don’t want automatic updates from multiple providers. And how knowledgeable are the clients about the relevant security features or risks? This is still a contractual no man’s land,” Vilja says.
Another concern is that in highly specialized systems that have been tweaked or integrated by clients, the updates could cause interruptions – or even shutdowns – in their operations. On the other hand, refraining or neglecting to update their systems could also end up leaving their entire systems vulnerable.
According to Vilja, in order to form proper guidelines, open discussion and continuous surveillance are essential. Eventually, the best practices will be formed, and they are likely to follow precedents from the computer market.
Ultimately, the same rules apply to web cameras and smart refrigerators as for industrial sensors – basic security measures go a long way, and they must actually be implemented in order to ensure operational safety.
Pasi Vilja is the Chief Information Security Officer at Konecranes.
The Operations Technology (OT) vs. Information Technology (IT) debate turns to better security
While OT managers may see the benefits of IoT-enabled asset monitoring, IT leadership can see IoT connectivity as a security threat. IoT-connected machinery offers uptime rewards at minimal risk but when done wrong, that connectivity into OT systems can pose big threats.
Material Handling Product News has interviewed several security experts on the ways to avoid security vulnerabilities when moving from closed-off OT systems to wireless networks and IoT connectivity.
“Integrating these systems can provide a lot of efficiency and help with goals like uptime, but at the same time, as things become more connected, they become more vulnerable.” says Keith Blodorn, director of program management at ProSoft Technology, which specializes in industrial communications and remote access solutions.
Read more about how companies are solving IoT connectivity data security issues: http://www.mhpn.com/article/the_operations_technology_ot_vs._information_technology_it_debate_turns_to
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Pushing IIoT predictive maintenance forward: two challenges to overcome
Enabled by wireless technology and connected devices, communication between machines and human technicians is fueling a shift from preventative to predictive maintenance. To push IIoT predictive maintenance technologies up the slope of enlightenment and spark mainstream adoption and success, two major challenges must be overcome: the challenge to obtain high quality data from industrial machines, and that to fuse sensor data with maintenance activities.
An article in Reliabilityweb offers solutions ranging from deep learning algorithms to tapping into the intuitive human capacity of sound-based diagnosis.
Read more about ways to overcome IIoT maintenance challenges and combine deep learning and human input: http://reliabilityweb.com/articles/entry/pushing-iiot-predictive-maintenance-forward-two-challenges-to-overcome
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How the Internet of Things impacts supply chains
Enterprise resource planning and supply chain management (SCM) have gone hand-in-hand for quite some time, but the IoT revolution will allow those solutions to be enhanced by intelligently connecting people, processes, data, and things via devices and sensors.
“Think of it as SCM 2.0,” writes Udaya Shankar, Vice President and Head of Internet of Things for Xchanging, a business process service provider for the global insurance industry. According to Shankar’s article in Inbound Logistics, this deeper intelligence can come to life in many different ways when it comes to supply chain data and intelligence – from the automation of the manufacturing process to improved visibility within the warehouse.
One area that Shankar believes will play a prominent role in the future supply chain, as it’s impacted by IoT, is in-transit visibility. “The logistics ecosystem has many players, and thus, many moving parts. Products are handled and transferred between the manufacturer, suppliers, the distribution center, retailer, and customer.”
Read more about how IoT can help supply chain professionals at:
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The deciding factor – how to utilize IoT data analytics for business intelligence
To make the most of data, it has to be transformed into information, which then has to be transformed into intelligence. As companies seek to leverage data – whether it’s internal, external, structured, or unstructured – to improve profitability or boost operational efficiency, analytics makes it possible to gain insights on business areas that were previously out of reach. Alun Jones, Data Scientist at Konecranes, talks about how organizations can best use IoT data analytics to arrive at more impactful business decisions.
According to McKinsey, the potential economic impact of the IoT could reach $11 trillion per year in 2025. That figure is equivalent to around 11 percent of the global economy. Turning that possibility into reality depends on how effectively IoT data analytics is used to drive better decision-making. The technology research firm Gartner identified IoT data analytics as one of the key IoT-related technologies that should be on every organization’s radar in 2017 and 2018, second only to security.
To identify target-rich, high-value data that can be used to generate business intelligence, the following steps should be taken.
1. Be aware of what you already have. It makes sense to know if some of that information is already available or accessible even if it isn’t immediately apparent. If you don’t know, then find out: Build a data map for your enterprise.
2. Think like a custodian, not an owner. The term “data owner” can be misleading as it appears to imply not only ultimate responsibility, but also the ability to utilize data for one’s own purposes. Both are not necessarily true of data use within a business. A data custodian, meanwhile, is responsible for the technical environment and controls around data.
3. Every action is part of the value chain. The siloed approach to data access makes unifying the analytics layer a challenge. To generate scalability and real-time performance, however, all types of analytics – descriptive, diagnostics, predictive and prescriptive – must be brought together into a single engine.
The role of cloud analytics platforms
In terms of using cloud analytics platforms to derive value from IoT data, it’s important to remember that not all data is created equal. Companies should think of ways to get data from a device into a position where it can be analyzed; the priorities of that data need to be determined as well.
Next, it’s also essential to gather data from numerous sources as interoperability is key in a heterogeneous environment. Last, it is advisable to have distributed data sources so that the cloud is there by default. Cloud simply means off premises; there will be distance between individual data sources and the computer power performing the processing. If you are uncomfortable with the cloud then find out why, and work to alleviate those anxieties. Processing IoT data close to the source results in less network delay than transferring it to the cloud, processing it there, and sending back the actionable result, such as computing and analysis at the edge.
“Harnessing IoT data analytics for business intelligence is not a one-time exercise, but a continuous process.”
As far as the barriers to widespread IoT value delivery are concerned, these could be overcome in two ways. First is technical. This covers everything from data gathering and low power or no power devices. (At the moment, for example, sensors and devices need power to drive them or the transmission of data, and in the future there will be a need to have devices that have lower power requirements.) Data architecture and cost of hardware should likewise be considered.
Second is the people aspect. Gatekeepers need to change. Management must improve its ability to understand and interpret the output from analytics. Individuals need to collaborate, even with those outside their respective enterprises. Normal business practices mean that things are driven on short-term departmental measures – this must be reconsidered as well. Do you design your plant to be cheaper to build, or more efficient and flexible to run?
Overall, harnessing IoT data analytics for business intelligence is not a one-time exercise. Rather, it’s a continuous process. Bear in mind that not everything is going to work. Optimizing what you do today is not enough either.
In addition, look at how to change the business model in a way that fits the market. For instance, GE builds airplane engines. They innovate by fitting sensors to gather that data and transfer it back to the factory. This is adopted over time so all engines send data about themselves. Over time, this is optimized so that GE knows the state of each asset and is able to predict when parts are likely to fail. This reduces downtime, making maintenance more efficient. Once the asset behavior is understood and de-risked, the business is transformed from building engines to offering engines as a service. GE’s software platform is now the key element in their business model. Cranes are a little behind but are catching up fast as the platforms needed to support such devices are already being built.
Alun Jones works as a Data Scientist at Konecranes. He is participating in several panel discussions at the IoT Tech Expo Europe event in Berlin on June 1-2, 2017.
— IndIntNow (@IndIntNow) June 7, 2017
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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