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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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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

Image credit: alphaspirit / Shutterstock.com

Interview w/ Niall O’Doherty

2 Comments

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  • Marko Yli-Pietila 06.07.2017 12:57

    It seems that companies are willing to share their data if they see the sharing valuable to them. One good example of successful data exchange is Braincube, https://braincube.com/.

  • Mohamed Sharaf 02.06.2017 17:15

    Are people going to be willing to share all this information? ???

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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.

 

Image credit: Rawpixel.com / Shutterstock.com

Interview w/ Mika Parikka

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