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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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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