First solutional approaches expected from the EMO Hannover 2013
This is not the first time a technical breakthrough was supposed to revolutionise the world of factories: we’re talking about computer-integrated manufacturing, or CIM for short. Derided by many as a CIMera. Around a quarter of a century later, we ask the scientist Prof. Dr. Thomas Bauernhansel, Director of the Institute for Industrial Manufacturing and Management (IFF) and the Fraunhofer Institute for Manufacturing Engineering and Automation (IPA) in Stuttgart, how far Industry 4.0 is just CIMera 2.0. Whether it will gain widespread acceptance or will remain just a bit of media hype, that’s a question to which as yet there is no definitive answer.
Be honest, when did you first hear of Industry 4.0, and what did you think of it?
Bauernhansel: It was in 2011, at a meeting of the Fraunhofer Production Matrix .We all started to google terms like cyber-physical systems, and tried to make sense of what could be meant by Industry 4.0. None of the production luminaries at this meeting had more than the vaguest idea. So the term was not coined by production experts, but comes from the fields of IT and artificial intelligence. Nonetheless, production technology experts had been working on it for a very long time.
Humans take charge of value creation
What’s the difference from CIM?
Bauernhansel: CIM was based on the assumption that we won’t be having people in the factory any more. The concept with CIM is that everything is highly integrated, and centrally controlled from a master computer. Here, humans now had merely an integrative function as planners and “commanders”. Industry 4.0 adopts an entirely fresh approach, focusing on communication, not integration. This means we have decentralised autonomous systems that communicate with each other, irrespective of the particular system and manufacturers involved. We say: the human being continues to play a central role in the factory, but a different one. He takes charge of the value creation process. And we are opting for data management in realtime. This means there’s no time-lagged data image in some central database. What happens is that the data are acquired in realtime at the places where they are currently being generated. In the context of control system technology, we’re talking about milliseconds here. In the context of planning and control, perhaps minutes or hours will suffice.
Industry 4.0 stands and falls with cyber-physical systems (CPSs). But there are experts who say they are often too expensive, not reliable enough and frequently overdimensioned. What’s your answer to one of these critics, who in fact comes from your own institute?
Bauernhansel: My respected colleague Alexander Verl has rightly remarked that ultimately we have to focus on the cost-efficiency of these systems. This critical approach is important, so as in particular to rein in those among the vendors concerned, meaning software firms or also machinery manufacturers, who are scenting business opportunities for themselves here. Ultimately, the system as a whole has to offer an advantage to the customer who is buying a product. At the moment, Industry 4.0 is being driven very largely by factory equipment producers and less by the customers. So there’s not a market crying out for it, but there is a technology that’s looking for an application. So what my colleague Alexander Verl is saying is not in contradiction to my own stance, because in the final analysis the thing has to be commercially viable.
Data security is a problem
Might it also be that many companies fear going into Industry 4.0 because they’re worried their data might be stolen from the cloud? What’s the story behind the “Virtual Fort Knox”, in which, according to the institute, “jointly used sensitive data are as safe as the USA’s gold reserves in the legendary stronghold of Fort Knox”?
Bauernhansel: There’s not going to be absolute data security in any system. It would be misleading to say security is going to be a huge problem, because security is already a huge problem now. Just as today we take the issue of security with the utmost seriousness, we shall take an equally serious approach when it comes to the issue of the cloud and concurrent users. And that’s precisely why we at the Fraunhofer IPA have launched the flagship project Virtual Fort Knox, in which we have taken a long hard look at everything: encoding and physical, communicative and organisational security: who is permitted to do what? Who has access and where?
What would you recommend in general? Should a cloud be located on the internet or rather in a firm’s own intranet?
Bauernhansel: Each company has to find its own compromise, and then decide: what data will I not be putting on the net, and what data will I be putting on my own net? What data will be located in the private cloud and what knowledge for the customers and vendors on the public cloud?
Let me return for a moment to CIM, which only began to be more widely adopted after standardisation. An expert from the automation sector has told me that standards for Industry 4.0 are a real turbo-boost for many activities, but the road to achieving a standard is very long and rocky. What’s your view on this?
Bauernhansel: We’re not all that far away from standards: from a technical viewpoint, the problem of standardisation has already been very largely solved in some fields. The actual problem is more the aspirations of firms who want to set these standards. Here we have to cultivate a community spirit. Even the major protagonists in this issue have to rethink their approach and say: yes, perhaps it makes sense that we have standardisation and openness here, assuring everyone of access to the internet of things and services. After all, it’s no use to anyone if at the end of the day we have several different internets of things dominated by large companies. Only with a standardised system will new business models evolve, able to develop their full benefits for the end-user as well.
The German Research Union, in its “Implementation Recommendations for the Industry 4.0 Future Project”, proposes taking as a model the service-oriented architectures (SOAs), which support interlinked, re-usable applications. What’s your opinion of this proposal?
Bauernhansel: I’m very much in favour of it. Service-oriented architecture has been discussed since the early 1990s. It’s not such a huge innovation on the IT side, it’s been around for a very long time. Really, it only goes to show how sluggishly the “oh-so-innovative” software industry adopts new ideas of this kind.
A glance into the future: what might a vision of Industry 4.0 look like?
Bauernhansel: We decouple affluence and growth from resource consumption, and provide large amounts of the requisite technology through the Industry 4.0 initiative. The networking, the decentralisation and the communication capabilities will lead to high levels of efficiency.
Perhaps on the way there we need some new input: “Inspired by technology” is the watchword at the 4th VDMA Congress on “More Intelligent Production”, which is being held at the EMO Hannover for the first time this year. What inspirational insights are you expecting from this congress, and from the world’s premier trade fair for the metalworking sector?
Bauernhansel: Inspired and driven by innovative technologies, the resource-efficiency potential for all production factors can be upgraded within the framework of Industry 4.0. Technology, not renunciation, has to be our motto. From the EMO Hannover and the congress, I am expecting approaches in this direction for holistically conceived, sustainable production operations of the future.
New Technologies Pair the Physical with the Digital
Digital twinning is one part of the technology road map for Industry 4.0 and the Industrial Internet of Things. A gamut of new technologies must be integrated to work seamlessly together to pair the physical domain with the digital information domain.
Digital twinning seeks to improve the design and maintenance of physical systems by offering datadriven ways to discretely map these physical systems into digital and computerized replicas of themselves. With the arrival of automation and data exchange, digital twinning could be useful in a myriad of industrial applications.
This new industrial context, where the physical and the digital worlds meet, is known as the fourth industrial revolution—or Industry 4.0. Brought on by the intersection of a host of high-technology electronic and computer systems, the “new way” of Industry 4.0 promises increasing gains, efficiencies, and flexibility. A gamut of new technologies must be integrated to work seamlessly together to pair the physical domain with the digital information domain. Digital twinning is only one part of the technology roadmap for Industry 4.0, as these additional technologies are helping to enable digital twinning for Industry 4.0 to manifest its potential:
• Pairing technologies
• Cyber-physical systems
• Augmented, virtual, and mixed reality
• Artificial intelligence
• Additive manufacturing
• 3D printing
• Digital thread
Pairing technologies are critical to digital twinning and the world of Industry 4.0, as these technologies empower a device or system to find, connect, and communicate with other devices and systems. For example, sensors and the Industrial Internet of Things (IIoT) products require the ability to find and connect with other devices successfully. Technologies such as Bluetooth®, among others, are employed to make these connections. To accomplish this, connected devices must be able to interrogate other potentially connectable devices successfully. When inquiring other devices, units must be able to ascertain whether they are communicating with a unit that they should be corresponding and exchanging data with. When properly enabled and successful, this accomplishment is called pairing.
Security issues are paramount. Every device should pair only after proper identification has been confirmed to avoid crosstalk or misinformation. Shortcuts may be achieved through programming algorithms that allow the devices to quickly and easily identify other units that they should pair with. Pairing gets accomplished through authentication keys employing cryptography. Pairing works to ensure that the connections stay bonded in a data exchanging relationship between devices and works to prevent an external source from prying into their data exchanges.
Being that flexibility is paramount, units must be able to make and break their pairing quickly and without external, human involvement. Successful pairing may require ongoing communication to keep the pairing active. If one of the units determines that the pairing bond is no longer relevant to its successful operational objectives, it will remove its pairing relationship and present itself available for a different pairing opportunity.
The National Science Foundation (NSF) defines cyber-physical systems (CPS) as, “The tight conjoining of and coordination between computational and physical resources.” The critical element in this definition is that it focuses on a system approach— where a set of connected things or parts form a complex whole.
A current example of a CPS is the automated airline flight-control systems. Industry 4.0 requires traffic control, not for airplanes, but for the machines, computers, robots, sensors, and processes that comprehensively work together for its realization. It represents a system of higher order than IIoT, because it sits one level higher in the complexity chain. Where IIoT is concerned with collecting, handling, and sharing of large amounts of data, CPS is focused on ensuring that this large amount of data, collected from multiple systems, gets properly utilized across multiple disciplines that are relevant to the industry involved. The unique dilemmas of any given industry will require engineering expertise to address these specific challenges.
Augmented, Virtual, and Mixed Reality
New technologies are augmenting our reality. They are providing us with the ability to overlay digital content in front of us physically, merging the real with the virtual, creating a mixed reality that should be considered augmented. This gain is allowing engineers to view things in new ways. For example, rather than viewing a DT on a computer monitor, we could view a DT using an augmented reality (AR) headset that enables the users to engage with digital content or interact with holograms.
The use of such AR empowers viewers to have an immersive experience whereby they engage their bodily senses.
Reality-enhancing headsets can create real-time experiences of actual conditions happening in the physical world, by way of experiencing them through a digitized environment. AR could lead to new insights and understandings. Additionally, a DT display could appear in the user’s field of view, making real-time feedback that much more accessible and easy to use.
Artificial Intelligence Technologies
IIoT offers the promise to provide connected data; therefore, useful data must be stored and analyzed. Artificial intelligence (AI) is a solution to how to analyze and successfully handle large amounts of digital data. It helps in allowing digital twinning to become more realized because it promotes value by enabling rapid integration, hybrid integration, investment leverage, and system management and compliance.
Through machine learning, it offers the opportunity to use digital data to model, analyze, train, apply, and infer how best to make decisions. AI is helping to change the traditional perspective of computing, moving it beyond what primarily has been an automating- and scaling-process perspective towards a knowledgebased perspective, via actionable insights. Soon, it will help engineers gather new insights and ways to create value. By using a data-science approach, rapidly powered decisions will enable the generation of further opportunities.
Additive manufacturing (AM) is a method of production in which 3D objects are built by adding layer-upon-layer of material. AM holds promise because it leads to industries that can address variable demand and produce products that are distributable and flexible. Two areas of AM – 3D printing and digital thread – are advancing to make digital twinning possible.
3D printing is perhaps the most well-known example of AM. In 3D printing, a printer is programmed to print an object using plastics, metals, or other custom materials with virtually zero lead-time. 3D printing is extremely flexible and eliminates the issues involved in producing objects with large economies of scale. What this means for the future is that you will be able to get what you want quickly—as if walking up to the fast food counter.
With complex systems, however, AM has been confined primarily to the laboratory because all the systems involved do not operate under a unified system and, thus, are hard to scale. Digital thread promises to change that.
A digital thread is a single, seamless strand of data that acts as the constant behind a data-driven digital system. It activates the potential of AM by allowing a unification of disparate applications by way of their adherence to the thread, which is their source of shared information. A digital thread creates an easier process for collecting, managing, and analyzing information from every location involved in the redesigned Industry 4.0. It enables better and more efficient design, production, and utilization throughout the entire process.
Digital twinning will be a hallmark of Industry 4.0, helping to increase gains, efficiencies, and flexibility for existing products and processes. But digital twinning is just one part of the Industry 4.0 road map. Pairing technologies, CPS, AI, and AM are key to seamlessly bringing together the physical realm and the realm of its DT information and insights. While these technologies are bringing their complexities into the digital twinning equation, ultimately, they promise to enable Industry 4.0 to manifest its potential.
by Paul Golata for Mouser Electronics
Industrial manufacturers turn AI to “turbocharge” products and services, says Accenture
The vast majority of manufacturers are turning to artificial intelligence (AI) to “turbocharge” their products and services, finds a new research report from Accenture.
Based on a survey of 500 manufacturing companies in six industries across Europe, North America and Asia, the report notes the ability of AI – particularly when combined with mobile computing and big data analytics – to transform not only core operations, but also worker and customer experiences, and ultimately even business models, and to enable “Industry X.0” strategies.
Yet the research found that only a small group is already leveraging AI in a way that Accenture refers to as Applied Intelligence – intelligent technology and human ingenuity, combined with analytics and industry expertise, applied at the core of business – at scale. For instance, while 98 percent of the surveyed organizations have begun to enhance their offerings with AI, only 16 percent of them have established a holistic AI vision for their business, only 5 percent are committing resources to AI-driven product initiatives, and only 2 percent report that they have begun to leverage AI solutions at scale.
The research also highlights the challenges companies face when trying to use the technology: The concerns cited most often were data quality (identified by 51 percent of respondents); data- and cyber security (45 percent); deciding between ‘buying vs. making’ AI-embedded solutions (45 percent); and data sharing and protecting intellectual property (40 percent).
“The re-invention of industrial products with AI is still in its early stages, and getting it right is anything but easy,” said Eric Schaeffer, a senior managing director at Accenture and global lead of its Industrial practice. “However, the successes of the AI leaders in our sample clearly show that it can be done and that the business case for AI in industrial is very strong.”
The report mentions how companies that re-invent their products by combining AI with other digital technologies can reap huge rewards. For instance, it cites other Accenture research showing that mastering AI can enable industrial-equipment manufacturers to boost their market capitalization by as much as 25 percent.
How AI leaders succeed
To get to these kinds of results, companies must go through a journey which, according to the Accenture report, comprises four stages: (1) exuding belief in AI and its ability to digitally reinvent products; (2) building a vision for leveraging existing offerings with AI; (3) committing resources to AI-driven product reinvention; and (4) executing on their vision and planned initiatives to digitally reinvent the product at scale.
Through clustering the surveyed companies by both industry and “AI-journey stage,” the report shows that AI maturity seems to vary by industry: Automotive companies seem to be more likely to commit to and execute AI initiatives, with 9 percent reaching the third stage and 5 percent reaching the fourth stage. However, only 7 percent and 3 percent of consumer durables companies and industrial and heavy equipment makers, respectively, reach the third stage, with only 1 percent of companies in each of those two sectors reaching the fourth stage.
Other results indicate what sets apart the 16 percent of companies that are at least envisioning AI-enabled ways to reinvent their products: Companies that reach the “vision” stage develop the investment and ecosystem strategies to acquire, process and secure the data needed to drive maximum value from AI. Moreover, they carefully analyze what they need to focus on: 82 percent of these “visionaries” cited enhanced “customer loyalty” and “deeper insights from product and service usage” as the key value drivers for themselves. The same proportion also said that “greater safety” and “smarter solutions and services” would be critical benefits of AI use for their customers.
Most of the 5 percent of surveyed companies that commit significant resources to AI initiatives concentrate on building both the IT capabilities and the skills necessary for large-scale AI implementation: 91 percent of those companies cited analytics and systems integration skills as imperative, and 64 percent said they would change elements of their business model as a result of embedding AI.
The 2 percent that reach the fourth stage – execution at scale – set themselves apart by closely working with ecosystem partners to identify, in granular detail, the AI components they want to combine with other digital technologies, now and in the future, as part of their customer value propositions. Among the key AI solutions these companies are planning to use are computer vision (73 percent), deep learning (64 percent) and robotics process automation (64 percent).
“Our findings suggest a strong correlation between a holistic, well-planned strategy and AI success,” said Raghav Narsalay, a managing director and Industry X.0 research lead at Accenture. “However, three-quarters of the companies we surveyed are still experimenting, using what might be called a ‘scattershot’ approach. But this is likely to change in the near future — and that’s when we’ll really see a rise in product reinvention with AI.”
Results from the research also indicate that the rate of product reinvention with AI will likely vary by industry. Changing sources of profitable revenue is a priority for 65 percent of those in the automotive sector, for example, while the sub-groups of the reports’ “industrial equipment manufacturing” cluster are split: Most heavy equipment makers (57 percent) state that product-lifecycle sales and marketing strategy is their key priority, while 42 percent of those in the industrial and electrical equipment sector expect that embedding AI will lead to changes in their innovation architecture.
hordon kim / firstname.lastname@example.org
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