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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

Via Material Handling Product News

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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

Image credit: Zapp2Photo / Shutterstock.com

Via Reliabilityweb

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How can Industry 4.0 help the global steel industry achieve greater efficiencies?

Taking place in Warsaw, Poland, the Future Steel Forum assembles speakers from academia and the steel industry to examine how technological innovations can revolutionize steel production. Matthew Moggridge, Editor of Steel Times International, talks about the themes and perspectives steelmakers must consider as they shift to a digital manufacturing platform.

According to the 2016 Global Industry 4.0 Survey conducted by the consulting firm PwC, the buzz surrounding Industry 4.0 has moved on from what some had earlier considered as hype to actual investment and real results. This investment, in turn, is translating into increasingly advanced levels of digitization and integration. 67% of respondents from the metals sector, among them companies in the steel industry, say they expect to reach advanced levels of digitization in their vertical value chains by 2020.

Matthew Moggridge, Editor of Steel Times International, shares a similar view. “The steel industry is well prepared for Industry 4.0 and has, for a long time, been at the forefront of industrial technological development,” he says.

“There are companies, such as Primetals Technologies, SMS group, Danieli Automation, Fives, among others, who have been pushing the boundaries of digital manufacturing and partnering with leading steelmakers such as ArcelorMittal, Tata Steel, Voestalpine and many others to develop the concept of Industry 4.0.”

Moggridge adds that in the US, Big River Steel is arguably the first smart steel plant. The company recently partnered with Noodle.ai, a San Francisco-based Enterprise Artificial Intelligence company, to implement Enterprise AI to optimize operations at the former’s scrap metal recycling and steel production facility in Osceola, Arkansas.

“On the one hand, digitization has moved from being an augmenting capability for steel companies to something that is now becoming a disruptive force. On the other hand, it is delivering supply chain agility, deeper process understanding and higher production utilization.”

Efficiencies and challenges

Broadly speaking, Industry 4.0 assists the global steel industry in its quest for greater efficiencies while raising new concerns. On the one hand, as digitization has moved from being an augmenting capability for steel companies to something that is now becoming a disruptive force, the PwC report says that it is delivering supply chain agility, deeper process understanding and higher production utilization.

The report states: “Automation is combining with data analytics to enable much higher flexibility as well as more efficiency in production. Algorithms are linking the physical properties of the materials with production costs and plant constraints to improve efficiency. Processes that were previously separated are now being integrated, leading to reductions of heat loss, energy consumption, throughput time, inventory as well as better price optimization.”

On the other hand, the people aspect also needs to be addressed. PwC states that companies will need to make sure staff members understand how the company is evolving and how they can be a part of the change. From PwC’s interviews with metals companies, the biggest challenges involve issues such as culture, leadership and the economic case for change.

In addition, Moggridge cites Dirk Schaefer, assistant professor of design engineering at the University of Bath, UK, who argues that the development of a new work force will also prove challenging within the context of Industry 4.0. Schaefer believes that investing in workforce education is essential. “Each of the previous industrial revolutions resulted in a surge of unemployment. There is no reason to believe that this will be any different this time around, unless preventive action is taken today,” Schaefer asserts.

 

Conference overview

These topics will be addressed by experts at the Future Steel Forum in Warsaw, which takes place today and tomorrow. Other discussion points include the impact of smart manufacturing on the steel industry, Industry 4.0 and its implications for plant safety, the future of cooperation between automation and steel manufacturing, and the role of human beings in the factory of the future.

Taking part are speakers from academia, the steel industry and the world of steel production technology such as Dr. Rizwan A Janjua, Head of Technology, World Steel Association; Jose Favilla, Director, Industry Solutions for Industrial Products, IBM; and Professor Chris Hankin, Imperial College London, among others.

Connecting the dots

As for Industry 4.0-related themes that are set to gain ever greater prominence in the coming years, Moggridge, who will deliver the welcoming and closing remarks at the event, has this to share. “Cyber security will always be a big issue that will constantly need to be addressed, but also the role of the human being in an increasingly automated environment, not only in steel but in other areas of industry as well,” he says.

“What people tend to forget about the steel industry is that it is already a very automated environment. In many ways, it’s just a case of connecting the dots before steelmakers can claim to be true advocates of Industry 4.0.”

Matthew Moggridge is the Editor of Steel Times International. The Future Steel Forum takes place in Warsaw, Poland, on June 14–15, 2017. futuresteelforum.com

Interview w/ Matthew Moggridge

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

Image credit: Lightspring / Shutterstock.com

Via Inbound Logistics

2 Comments

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  • Bhanwar Singh Rathore 22.07.2017 16:53
  • Bhanwar Singh Rathore 22.07.2017 16:40

    Transit visibility has improved assurance level in planning and customer satisfaction level

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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.”

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.

Interview w/ Alun Jones

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