Industrial Internet Now

Highlights from the Industry of Things World Report 2017

The Industry of Things World Survey Report 2017 sheds light on the state of the IoT market. Based on the views of over 1,100 cross-industry leaders, the focus point is now shifting towards real-world implementation and monetization of industrial IoT. According to Maria Relaki, Portfolio Director at we.CONECT Global Leaders, the organizer of Industry of Things World conference series, industrial IoT has moved from theory to application.

The third annual Industry of Things World Survey investigates the opinions of over 1,100 Internet of Things (IoT) and Industry 4.0 managers working in industries such as manufacturing, information and communication technologies, automotive and transportation, healthcare, chemicals and many more. Conducted online from January to March 2017, the survey covers the current state of the worldwide IoT market.

“The results indicate that IoT is now considered essential to business and not just theory or something that is good to have. Eighty-eight percent of the respondents found industrial IoT critical to their organization’s future success,” comments Maria Relaki, Portfolio Director at we.CONECT Global Leaders. “A key finding is that Industrial IoT has already become mainstream: According to our respondents, adoption of industrial internet is at 91 percent. This means that companies are moving beyond the theoretical planning phase, and they are able to discuss how they are going to roll out their plans or even what they have already achieved with IIoT,” she explains.

Trends and phenomena

Relaki points out that the increasing percentage of use of digital technology—up from last year’s 82% to 91% this year—supports the notion that digital transformation really is the way to go. According to her, moving to the implementation phase means that there is a lot going on in the world of IoT because this stage has so many steps. In addition to the overarching theme of digital transformation, among the hot topics discovered in this year’s survey results are monetization strategies, data analytics, platforms, and improved operating performance. “People have had enough of ‘This is the next big thing’. Now, businesses are expecting results,” says Relaki.

However, some themes remain equally important from year to year. “Forecasting demand, cybersecurity, and interoperability have not lost importance,” Relaki notes. For example, over half of the respondents (55%) think regulation, governance, and security play a very important role in digital transformation, while almost two thirds (62%) found cybersecurity and privacy a hurdle that must be overcome in pursuing digital transformation. The lack of industry standards for interoperability and interconnectivity was the second most significant hurdle (39%) from the respondents’ perspective.

Challenges and opportunities

“Businesses have identified the opportunities of IIoT technologies, and they are now looking for specific solutions to answer their needs.”

The survey identified some of the challenges and opportunities of industrial IoT. “Based on the responses to our open-ended questions, businesses have identified the opportunities of IIoT technologies, and they are now looking for specific solutions to answer their needs,” she says. For example, the majority of respondents expected IIoT to yield new revenue streams and business models (66%) as well as new products and services (66%). The biggest potential IIoT improvement areas were found in plant operating performance through improved maintenance and asset uptime (58%) or through improved execution (48%).

The challenge lies in recognizing innovation and integrating it into business. For instance, when it comes to digital transformation, only four percent of respondents found their company as having both vision and execution in place.

Industry of Things World 2017

Industry of Things World 2017 is a strategic conference that brings together stakeholders from a variety of industries, all with the aim of defining the future of the fourth industrial revolution. Organized by we.CONECT Global Leaders, the event is scheduled to take place in Berlin, Germany, from September 18 to 19, 2017. According to Relaki, approximately 1,000 participants representing over 40 different nationalities are expected to attend.

Key themes of the two-day program include, among others, overcoming integration challenges of industry 4.0 in running businesses, monetizing the IIoT in an industrial setting, the impact of AI, machine learning, and robotics on productivity, and the implications of the convergence of IT and OT in terms of security. “This year, the emphasis is shifting from ideas and intentions to implementation. There will be presentations of actual projects demonstrating real-world applications of IIoT technologies and sessions dedicated to the integration of innovation in companies,” shares Relaki.

Among the conference’s 80-plus speakers are Kevin Ashton, a renowned expert in digital transformation and the one who coined the term “the Internet of Things;” Nigel Upton, Worldwide Director and General Manager IoT and Global Connectivity Platforms at Hewlett Packard Enterprise; Eric Schaeffer, Senior Managing Director at Accenture; and Tanja Rueckert, President IoT and Digital Supply Chain at SAP.

To find out more about the agenda and speakers of Industry of Things World 2017, visit www.industryofthingsworld.com/en/ .

Download the full survey report here.

Maria Relaki works as Portfolio Director at we.CONECT Global Leaders and is responsible for the Industry of Things World global event series.

Image credit: Industry of Things World

Update: Kevin Ashton, who first coined the term ”Internet of Things” talks about the next phase of the Industrial Internet. Video was filmed at the event venue in Berlin.

 

How Industrial IoT enables the factory of the future

Trillion-dollar projections on the expanding size of the market are urging companies to capitalize on the Industrial IoT. For many, however, it remains unclear how industries should apply IIoT to begin making the hyper-efficient and agile factory of the future a reality. Fabio Bottacci, founder and CEO of VINCI Digital and Industrial IoT Expert Contributor at the World Economic Forum and at the Brazilian Development Bank (BNDES), shares his insights on how Industrial IoT is already increasing operational efficiency, saving time and reducing cost.

As the Fourth Industrial Revolution transforms manufacturing and material handling, enterprises continue to look for ways to create value from converging technologies. But what are the steps that companies need to take to put together an effective agenda of action? Fabio Bottacci finds it essential that the implementation of industrial internet is incorporated into the company’s strategy and business development. In other words, chief executives must embrace change. “In order to advance decision-making on the correct level, CEOs must be included from the very beginning, possibly as initiative main sponsor. IT officers alone cannot drive real digital transformation,” says Bottacci.

Bottacci advises manufacturers to initiate the transformation by defining a specific set of goals, to be assessed and validated initially on a pilot project, before the implementation at scale of an end-to-end Industrial IoT solution. The next step is to deploy an industrial internet pilot in one facility, or on a specific production line, which will be used as a case study for learning how IoT works in this particular industrial environment. The pilot facility is then reworked and developed according to observations. After the test phase, it is easy for a company to apply the same principles, with proper adjustments, at scale to other facilities.

Bottacci uses the concept of flexible infrastructure to refer to how transformation can be simpler in certain contexts. “It is easier to justify large investments in industrial internet in environments where industrial internet is incorporated into production by transitioning directly to automated, advanced IIoT environments. The transition phase is less complicated when the existing infrastructure is light, because there are fewer things that must be accounted for in applying new solutions,” he explains.

A case in point is Romania, where the internet infrastructure is now top of the class in Europe. The Romanian infrastructure was created rather recently compared to more affluent European countries, and therefore, the entire web is more modern than that in Finland, for example.

Industrial internet in practice

“IIoT coupled with AI or ML turns maintenance into a dynamic, rapid and automated task.”

Bottacci emphasizes that applications of industrial IoT are already a reality. According to him, there are dozens of different use cases of IIoT in enterprises. “Companies are already developing IoT applications that work, and they have started making a difference. For example, transportation and warehousing benefit from automated vehicles and asset tracking. In manufacturing, predictive maintenance and asset performance management are key areas where industrial internet boosts value creation.”

Predictive maintenance keeps assets up and running, decreasing operational costs and saving companies millions of dollars. Data from IIoT-enabled systems – sensors, cameras, and data analytics enabled by powerful artificial intelligence (AI) or machine learning (ML) algorithms – helps to better plan maintenance, allowing manufacturers to service equipment before problems occur. “Data streaming from sensors and devices can be used to quickly assess current conditions, recognize warning signs, deliver alerts and automatically trigger appropriate maintenance processes. IIoT coupled with AI or ML thus turns maintenance into a dynamic, rapid and automated task,” Bottacci explains.

“Other potential advantages include increased equipment lifetime, increased plant safety and fewer accidents with negative impact on environment,” he adds.

The importance of edge analytics

“Companies have been proactive in moving the processing of IIoT to cloud services,” Bottacci notes. However, in his opinion, it is not necessarily a wise move to have everything in the cloud. During critical stages of the manufacturing process it is crucial that decisions can be made instantaneously. Here, manufacturers can benefit from edge analytics.

“Edge computing enables real-time analytics. Edge analytics is an approach to data collection and analysis where automated analytical computation is performed on data at a sensor, network switch or other device instead of waiting for the data to be sent back to a centralized data store. IIoT can be supplemented with Arduino-based, open-source computer hardware and software applications that allow some of the processing to take place on site, at the edge of the network and near the source of the data. Edge computing helps ensure that the right processing takes place at the right time, in the right place,” Bottacci explains. “Edge computing is a preferable option for the cloud in terms of security, as proprietary data is kept within the company firewall. Moreover, edge computing becomes vital when you need real-time analysis and automated action to save critical-mission production lines or facilities from potential heavy damages.”

Creating value with Industrial IoT

“There’s no value in the data without advanced algorithms of machine learning.”

Bottacci says that value can be created in surprisingly simple ways by putting data to work. As an example of enhancing safety and efficiency in material handling, he refers to a fleet management system in Silicon Valley. “Peloton Tech’s truck platooning system is a case study that illustrates how IIoT is already creating value. The system uses vehicle-to-vehicle communication to connect the braking and acceleration between two trucks. The lead truck controls the simultaneous acceleration and braking of the whole fleet, reacting faster than a human or even a sensor system could. What follows is a reduction in aerodynamic drag, which leads to companies saving around seven per cent in fuel cost. In terms of annual savings, this is a remarkable number,” says Bottacci.

In Europe, trucking companies such as Scania and Volvo Trucks have adopted IIoT fleet thinking. “It still takes courage to adopt innovations like these,” Bottacci admits. However, he recommends getting started quickly by building a case study of industrial internet and then working towards expanding IIoT to cover more and more of the industrial realm. “Companies should start seeing emerging technology like Industrial IoT not as a threat but as the only way to survive in a matter of a few years. That’s two or three years if you are an optimist, five to ten if you are more conservative,” estimates Bottacci.

In Bottacci’s view, the simple capacity of devices to seize data is not what the Industrial Internet of Things is essentially about. “Even if you have all the infrastructure and the technology to get the data – sensors, WiFi, the gateway, the cloud – and the capacity of analyzing the data, there’s no value in it without AI, more specifically advanced algorithms of machine learning.”

“IIoT is about AI or ML analyzing data in real time so as to make decisions and act, most of the times several days or even weeks before a potential issue. This process results in actual business outcomes,” Bottacci states. “Prescriptive analytics react autonomously, real-time: In a mission-critical situation, a prescriptive system will autonomously decide what to do. This is where edge analytics is imperative,” he explains. “My point is: You can’t consider industrial internet standalone. The real value comes from how companies use AI and ML-enabled IIoT solutions in analyzing and processing data.”

Fabio Bottacci works as an independent advisor. He is founder and CEO of VINCI Digital and an Industrial IoT Expert Contributor at the World Economic Forum and at BNDES, the Brazilian Development Bank.

Unifying industrial IoT technologies with monitoring

While the majority of industrial companies have multiple systems and technologies managing various components of their operations, only a few have been able to aggregate the data from all of these systems together to drive business prosperity. When brought together, the data from these disparate systems can produce insights across all aspects of the business.

“The teams and upper management of these industrial organisations could – with a unified monitoring tool – see every link in the supply chain and their products’ evolution from early stages right through to delivery,” Paessler AG’s APAC sales director Andrew Timms told IoT Hub. Besides operations-wide oversight, monitoring tools can also be used for the management of particular components of the value chain.

Read more about aggregating data from RFID, SCADA, IT and other sources at: https://www.iothub.com.au/news/unifying-industrial-iot-technologies-with-monitoring-458258

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?

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

Image credit: Sergey Nivens / Shutterstock.com

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

Image credit: Zapp2Photo / Shutterstock.com

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:
http://www.inboundlogistics.com/cms/article/how-the-internet-of-things-impacts-supply-chains/

Image credit: Lightspring / Shutterstock.com

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.”

Delivering value

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.