Thanks to Terrence O’Hanlon of ReliabilityWeb for cluing me in to this latest open source project regarding Digital Twins on LinkedIn. Somehow the OMG and I missed connections on the press release. Yet another case of cooperation among suppliers and users to promote the common good. Digital Twins form the bedrock of Industry 4.0 and whatever other modern industrial advance.
News in brief: Users to create standard terminology and reference architectures and share use cases across industries
Non-profit trade association Object Management Group (OMG) with founders Ansys, Dell Technologies, Lendlease, and Microsoft, announced the formation of Digital Twin Consortium. Digital twin technology enables companies to head off problems before they occur, prevent downtime, improve the customer experience, develop new opportunities, drive innovation and performance and plan for the future using simulations. Members of Digital Twin Consortium will collaborate across multiple industries to learn from each other and develop and apply best practices. This new open membership organization will drive consistency in vocabulary, architecture, security and interoperability to help advance the use of digital twin technology in many industries from aerospace to natural resources.
Digital twins, virtual models of a process, product or service that allow for data analysis and system monitoring via simulations, can be challenging to implement due to a lack of open-source software, interoperability issues, market confusion and high costs. In order to ensure the success of Digital Twin Consortium, several leading companies involved in digital twin technology have joined the consortium prior to inception. This category of early innovators, called Groundbreakers, includes: Air Force Research Lab (US), Bentley Systems, Executive Development, Gafcon, Geminus.AI, Idun Real Estate Solutions AB, imec, IOTA Foundation, IoTIFY, Luno UAB, New South Wales Government, Ricardo, Willow Technology, and WSC Technology.
Membership is open to any business, organization or entity with an interest in digital twins.
“Most definitions of digital twin are complicated, but it’s not a complicated idea. Digital twins are used for jet engines, a Mars rover, a semiconductor chip, a building and more. What makes a digital twin difficult is a lack of understanding and standardization,” said Dr. Richard Soley, Digital Twin Consortium Executive Director. “Similar to what we’ve done for digital transformation with the Industrial Internet Consortium and for software quality with the Consortium for Information and Software Quality, we plan to build an ecosystem of users, drive best practices for digital twin usage and define requirements for new digital twin standards.”
Digital Twin Consortium will:
- Accelerate the market for digital twin technology by setting roadmaps and industry guidelines through an ecosystem of digital twin experts.
- Improve interoperability of digital twin technologies by developing best practices for security, privacy and trustworthiness and influencing the requirements for digital twin standards.
- Reduce the risk of capital projects and demonstrate the value of digital twin technologies through peer use cases and the development of open source code.
An ecosystem of companies, including those from the property management, construction, aerospace and defense, manufacturing and natural resources sectors will share lessons learned from their various industries and will work together on solve the challenges inherent in deploying digital twins. As requirements for new standards are defined, Digital Twin Consortium will share those requirements with standards development organizations such as parent company OMG.
Founding members, Ansys, Dell Technologies, Lendlease and Microsoft will each hold permanent seats on an elected Steering Committee, providing the strategic roadmap and creating member working groups.
Sam George, Corporate Vice President, Azure IoT, Microsoft Corp. said, “Microsoft is joining forces with other industry leaders to accelerate the use of digital twins across vertical markets. We are committed to building an open community to promote best practices and interoperability, with a goal to help establish proven, ready-to-use design patterns and standard models for specific businesses and domain-spanning core concepts.”
“The application of the Digital Twin technology to Lendlease’s portfolio of work is well underway and we are already realising the benefits of this innovation to our overall business,” said Richard Ferris, CTO, Digital Twin R&D, Lendlease. “The time for disruption is now, and requires the entire ecosystem to collaborate together, move away from the legacy which has hindered innovation from this industry, and embrace Digital twin technology for the future economic and sustainable prosperity of the built world. Digital Twin Consortium is key to the global acceleration of this collaboration and the societal rewards we know to be possible with this technology and approach.”
“Dell Technologies is proud to be one of the founding members of Digital Twin Consortium. As the rate of digital transformation continues to accelerate, industry-standard methods for Digital Twins are enabling large scale, highly efficient product development and life cycle management while also unlocking opportunities for new value creation. We are delighted to be part of this initiative as we work together with our industry peers to optimize the technologies that will shape the coming data decade for our customers and the broader ecosystem,” said Vish Nandlall, Vice President, Technology Strategy and Ecosystems, Dell Technologies.
“The Consortium is cultivating a highly diverse partner ecosystem to speed implementation of digital twins, which will substantially empower companies to slash expenses, speed product development and generate dynamic new business models,” said Prith Banerjee, chief technology officer, Ansys. “Ansys is honored to join the Consortium’s esteemed steering committee and looks forward to collaborating closely with fellow members to further the Consortium’s success and help define the future of digital twins.”
Digital Twin Consortium members are committed to using digital twins throughout their operations and supply chains and capturing best practices and standards requirements for themselves and their clients. Membership fees are based on annual revenue.
Digital Twin Consortium is The Authority in Digital Twin. It coalesces industry, government and academia to drive consistency in vocabulary, architecture, security and interoperability of digital twin technology. It advances the use of digital twin technology from aerospace to natural resources. Digital Twin Consortium is a program of Object Management Group.
While at the Hannover Messe Preview last week in Germany, I talked with the representatives of a German consortium with the interesting name of “it’s OWL”. Following are some thoughts from the various organizations that compose the consortium.
Intelligent production and new business models
Artificial Intelligence is of crucial importance for the competitiveness of industry. In the Leading-Edge Cluster it’s OWL six research institutes cooperate with more than 100 companies to develop practical solutions for small and medium-sized businesses. At the OWL joint stand (Hall 7, A12) over 40 exhibitors will demonstrate applications in the areas of machine diagnostics, predictive maintenance, process optimization, and robotics.
Prof. Dr. Roman Dumitrescu (Managing Director it’s OWL Clustermanagement GmbH and Director Fraunhofer IEM) explains: “Our research institutes are international leaders in the fields of machine learning, cognitive assistance systems and systems engineering. At our four universities and two Fraunhofer Institutes, 350 researchers are working on over 100 projects to make Artificial Intelligence usable for applications in industrial value creation. With it’s OWL, we bring this expert knowledge into practice. In 2020, we will launch three new strategic initiatives worth 50 million € to unlock the potential for AI in production, product development and the working world for small and medium-sized enterprises.”
In the initiative ‘AI Marketplace’ 20, research institutes and companies are developing a digital platform for Artificial Intelligence in product development. Providers, users, and experts can network and develop solutions on this platform. In the competence centre ‘AI in the working world of industrial SMEs’, 25 partners from industry and science make their knowledge of work structuring in the context of AI available to companies.
Learning machine diagnostics and ‘SmartBox’ for process optimization
The Institute for Industrial Information Technology at the OWL University of Applied Sciences and Arts will present new results for intelligent machine diagnostics at the trade fair. Using a three-phase motor, it will be illustrated how learning algorithms and information fusion can be used to reliably identify, predict, and visualize states of technical systems. Patterns and information hidden in time series signals are learned and presented to the user in an understandable way. Inaccuracies and uncertainties in individual sensors are solved by conflict-reducing information fusion. For example, motors can be used as sensors. Within a network of sensors and other data sources in production plants, motors can measure the “state of health” and analyze the causes of malfunctions via AI. This reduces scrap and saves up to 20 percent in materials.
The ‘SmartBox’ of the Fraunhofer Institute IOSB-INA is a universally applicable solution that identifies anomalies in processes in various production environments on the basis of PROFI-NET data. The solution requires no configuration and learns the process behavior.
With retrofitting solutions of the Fraunhofer Institute, companies can prepare machines and systems in their inventory for Industrie 4.0 applications without major investment expenditure. The spectrum ranges from mobile production data acquisition systems in suitcase format for studies of potential to permanently installable retrofit solutions. Intelligent sensor systems, cloud connections and machine learning methods build the basis for data analysis. This way, processes can be optimised and more transparency, control, planning, safety, and flexibility in production can be achieved.
Cognitive robotics and self-healing in autonomous systems
The Institute of Cognition and Robotics (CoR-Lab) presents a cognitive robotics system for highly flexible industrial production. The potential of model-driven software and system development for cognitive robotics is demonstrated by using the example of automated terminal assembly in switch cabinet construction. For this purpose, machine learning methods for environ- mental perception and object recognition, automated planning algorithms and model-based motion control are integrated into a robotic system. The cell operator is thereby enabled to perform different assembly tasks using reusable and combinable task blocks.
The research project “AI for Autonomous Systems” of the Software Innovation Campus Paderborn aims at achieving self-healing properties of autonomous technical systems based on the principles of natural immune systems. For this purpose, anomalies must be detected at runtime and the underlying causes must be independently diagnosed. Based on the localization it is necessary to plan and implement behavioral adjustments to restore the function. In addition, the security of the systems must be guaranteed at all times and system reliability must be increased. This requires a combination of methods of artificial intelligence, machine learning and biologically inspired algorithms.
Predictive maintenance and digital twin
Within the framework of the ‘BOOST 4.0’ project, the largest European initiative for Big Data in industry, it’s OWL is working with 50 partners from 16 countries on various application scenarios for Big Data in production. it’s OWL focuses on predictive maintenance: thanks to the systematic collection and evaluation of machine data from a hydraulic press and a material conveyor system, it is possible to identify patterns in the production process in a pilot company. The Fraunhofer IEM has provided the technological and methodological basis. And successfully so: over the past two years the prediction of machine failures has been significantly improved in this specific application by means of machine learning methods. The Mean Time To Repair (MTTR) has already been reduced by more than 30 percent. The Mean Time Between Failures (MTBF) is now six times longer than before. A model of the predictive production line can be seen at the stand.
The digital twin is an important prerequisite for increasing the potential for efficiency and productivity in all phases of the machine life cycle. Companies and research institutes are working on the technical infrastructure for digital twins in an it’s OWL project. Digital descriptions and sub-models of machines, products and equipment as well as their interaction over the entire life cycle are now accessible thanks to interoperability. Requirements from the fields of energy and production technology as well as existing Industrie 4.0 standards and IT systems are taken into account. This is expected to result in potential savings of over 50 percent. At the joint stand, Lenze and Phoenix Contact will use typical machine modules to demonstrate how digital twins can be used to exchange information between components, machines, visualisations and digital services across manufacturers. Interoperability proves for the first time how the combination of data can be used to create useful information with added value for different user groups. For example, machine operators and maintenance staff can detect anomalies and receive instructions for troubleshooting.
Connect and get started – production optimization made easy
The cooperation in the Leading-Edge Cluster gives rise to new business ideas that are developed into successful start-ups. For example, Prodaso—a spin-off from Bielefeld University of Applied Sciences—has developed a simple and quickly implementable solution for the acquisition and visualization of machine and production data. The hardware can be connected to a machine in a few minutes via plug-and-play. The machine data is displayed directly in the cloud.
Prodaso has succeeded in solving a central challenge: Until now, networking machines from different manufacturers have been complex and costly. The Prodaso system can be retrofitted to all existing systems, independent of manufacturer and interface. In addition, the start- up also provides automated analysis and optimization tools. This enables companies to detect irregularities and deviations in the process flow at an early stage and to initiate appropriate measures. The company, founded in 2019, has already connected approximately 100 machines at companies in the manufacturing industry.
Digitization is on everyone’s lips these days. If you have not taken steps to implement and improve digital data flow, you are probably already behind. I receive information regularly from PwC and here is a new report on how digitization is reshaping the manufacturing industry. The report takes a look at 8 companies and showcase how they improved their efficiency, productivity and customer experience by ensuring they have the right capabilities central to their operating model and by matching them with strong skill sets in analytics and IT.
Pressure from the consumer, new regulations and advances in information technology are all reasons that are pushing manufacturing organizations to digitize so they can avoid falling behind the new breed of market-leading ‘digital champions.’ The report identifies 4 significant changes CEOs must implement to maximize the benefits of digitization.
1. Drive organizational changes that address new digital capabilities and digitalized processes – e.g., product and process design and engineering, end-to-end procurement, supply chain/distribution and after-sales – right from the top, because these are so new and different
2. Hire more software and Internet of Things (IoT) engineers and data scientists, while training the wider workforce in digital skills
3. Learn from software businesses, which have the ability to develop use cases rapidly and turn them into software products
4. Extend digitalization beyond IT to include significant operational technologies (OT) such as track and trace solutions and digital twinning
From the report, “Already, digitally ‘smart’ manufacturers are gaining a competitive advantage by exploiting emerging technologies and trends such as digital twinning, predictive maintenance, track and trace, and modular design. These companies have dramatically improved their efficiency, productivity, and customer experience by ensuring these capabilities are central to their operating models and by matching them with strong skill sets in analytics and IT. “
During 2018 and early 2019, PwC conducted in-depth digitisation case studies of eight industrial and manufacturing organisations in Germany, the US, India, Japan and the Middle East. Drawing on discussions and interviews with CEOs and division heads, we explored the key triggers for change these companies faced, assessed how digital solutions are being implemented and how digitisation is affecting key aspects of their operating models. We also compared our eight organisations with other publicly cited digitisation case studies, and leveraged PwC’s 2018 study Digital Champions: How industry leaders build integrated operations ecosystems to deliver end-to-end customer solutions and other ongoing PwC research.
This paper is the result of ongoing collaboration between PwC and the Global Manufacturing and Industrialisation Summit (GMIS). GMIS provides a forum for industry leaders to interact with governments, technologists and academia in order to navigate the challenges and opportunities brought about by the digital technologies of the Fourth Industrial Revolution. PwC has been a knowledge partner with GMIS since 2016.
The eight case studies in this report make clear how far the role of digital technology goes beyond traditional IT systems. It also encompasses OT and data and analytics technologies. Full integration and linkage among these different technologies, and the ecosystems they are part of, are essential to a successful digital transformation. Yet success is impossible without a digitally smart workforce that is familiar with Industry 4.0 skills and tools.
These challenges are the subject of the second part of the report Digital Champions: How industry leaders build integrated operations ecosystems to deliver end-to-end customer solutions, which will be published in January 2020.
The report will elaborate further on the emerging theory of digital manufacturing and operations, in which successful, digitised industrial organisations will increasingly have to act like software companies in response to four key factors:
- The connected customer seeks a batch size of one, necessitating greater customisation of products and delivery time, improved customer experience, use of online channels and outcome-based business models.
- Digital operations require both engineering and software abilities to enable extensive data analysis and IoT-based integration, as well as digitisation of products and services.
- Organisations need augmented automation, in which machines become part of the organisation via closely connected machine–worker tasks and integrated IT and OT.
- Future employees will be ‘system-savvy craftspeople’ with the skills to use sensors in order to collect and analyse accurate data, as well as design and manage connected processes.
About the authors
Anil Khurana is PwC’s global industrial, manufacturing and automotive industry leader. He is a principal with PwC US.
Reinhard Geissbauer is a partner with PwC Germany based in Munich. He is the global lead for PwC’s Digital Operations Impact Center.
Steve Pillsbury is a principal with PwC US and the US lead for PwC’s Digital Operations Impact Center.
Next to acquisitions, partnerships are driving actions among major digital industrial supplier players. With today’s announcement, Aras, who labels itself “the only resilient platform provider for digital industrial applications,” announced a strategic partnership with ANSYS that includes the licensing of the Aras platform technology to enable the next generation of digital engineering practices.
When we last saw ANSYS on this blog, Rockwell Automation had announced a partnership to enhance its digital twin and simulation offering.
ANSYS will leverage the underlying Aras platform technologies such as configuration management, PDM/PLM interoperability, API integration, and add simulation specific capabilities to deliver highly scalable and configurable products that connect simulation and optimization to the business of engineering — creating new ways of exploring and improving product performance.
Organizations increasingly expect to leverage simulation throughout the product lifecycle to interoperate with their existing PLM, ALM, and ERP applications. Additionally, customers must address scale and complexity challenges with data and process management, traceability and availability of simulation results across the lifecycle.
ANSYS is leveraging Aras’ resilient platform services combined with its simulation domain expertise and technology for new product offerings to improve productivity and maximize business value from simulation investments. ANSYS will deliver commercial offerings for simulation process and data management, process integration, design optimization, and simulation-driven data science.
“With our open ecosystem approach, this unique collaboration combines the strengths of ANSYS’ industry-leading multiphysics portfolio and the resilient platform from Aras for digital connectivity to dramatically enhance customer value,” said Navin Budhiraja, vice president of cloud and platform business unit, ANSYS. “As simulation technologies impact every product decision, we see the ability of ANSYS solutions to interoperate and link with heterogeneous systems as an important step to accelerate the digital transformation for our customers.”
“We believe that simulation is essential to developing tomorrow’s next generation products, and that better data and process management of simulations is required to enable the digital processes of the future which will support these products,” said Peter Schroer, president and CEO, Aras. “We see the ANSYS and Aras partnership as a potential game changer in connecting simulation to engineering processes for traceability, access and reuse across the product lifecycle.”
I give up. To me, the end of the decade is next year figuring there was not a year 0, then the beginning of the new calendar was year 1 and the end of the first decade was year 10. Oh, well, mainstream media just can’t wait to jump into wrap-up frenzy. So, me, too.
The last 10 years in industrial technology was busy with new buzz words—heavier on marketing than on substance in many ways. We breezed through Industrie 4.0 with its cyber-physical systems. Then we had Internet of Things borrowed from the consumer, largely iPhone, space. But borrowing from GE advertising of the “Industrial Internet”, the “Industrial Internet of Things” became originally the European counterpoint to Germany’s Industry 4.0 and then grew into general adoption.
Not finished with all this buzz, the industry discovered “digital”. We had digital twin (derived from cyber-physical systems). But these had to be connected with the digital thread. And all led into a digital transformation.
Let’s take a look at some specific topics.
Much of the foundation was laid in the decade before. Maybe I should say decades. The industry started digitizing in the 1980s. It’s been building ever since. Through the first decade of this millennium great strides were made in control technology, usability, sensors (both sensitivity and communication), networks moving from analog to digital and through field buses to Ethernet.
In this decade, most companies grew by acquisition of smaller, innovative companies and start-ups. The remaining automation giants pieced together strategies based on visions of which companies to acquire and what customer solutions were required. Looking ahead, I’m considering what additional consolidation to anticipate. I think there will be more as the market does not seem to be growing dramatically.
Most innovation came in the realm of data. Decreasing costs of memory, networking stacks, and other silicon enabled leaps in ability to accumulate and communicate data. Borrowing software advances from IT, historians and relational databases grew more powerful along with new types of data handling and analysis coming from the “big data” and powerful analytics technologies.
Another IT innovation that finally hit industrial companies was adoption of “cloud” with the eventual development of edge. Instead of the Purdue Enterprise Reference Model of the control/automation equipment being the gateway of all data from the processes, companies began to go sensor to cloud, so to speak, breaking down the rigidity of PERA thinking.
It is now old news that digital is everywhere. And, it is not a sudden development. It has been building for 30 years. Like all technology, it builds over time until it’s suddenly everywhere. The question is no longer what is becoming digital, nor is it speculating over marketing terms like digital transformation.
The question about digital everything is precisely how are we to use it to make things better for humans and society.
Sensors—At least by 2003, if not before, I was writing about the converging trends in silicon of smaller and less expensive networking, sensing, processing, and memory chips and stacks that would enable an explosion of ubiquitous sensing. It’s not only here; it is everywhere. Not only in manufacturing, but also in our homes and our palms.
Design—CAD, CAM, PLM have all progressed in power and usability. Most especially have been the development of data protocols that allow the digital data output of these applications to flow into operations and maintenance applications. Getting as-built and as-designed to align improves maintenance and reliability along with uptime and productivity. And not only in a single plant, but in an extended supply chain.
Networking—The emergence of fast, reliable, and standardized networking is the backbone of the new digital enterprise. It is here and proven.
Software—Emergence of more powerful databases, including even extension of historians, along with data conversion protocols and analysis tools provides information presented in an easily digestible form so that better decisions may be made throughout the extended enterprise.
Industry press have talked about IT/OT convergence until we are all sick of the phrase. Add to that stories of in-fighting between the organizations, and you have the making of good stories—but not of reality or providing a path to what works. As Operations Technology (OT) has become increasingly digital, it inevitably overlaps the Information Technology (IT) domain. Companies with good management have long since taken strides to foster better working relationships breaking the silos. Usually a simple step such as moving the respective manager’s offices close to each other to foster communication helps.
Speaking of IT and OT, the modification of the Purdue Enterprise Reference Modal to show data flowing from the plant/sensor level directly to the “cloud” for enterprise IT use has enticed new entrants into manufacturing technology.
If we are not forced to go through the control system to provide data for MES, MOM, ERP, CRS, and the like, then perhaps the IT companies such as Dell Technologies, Hewlett Packard Enterprise, and Hitachi Vantara can develop their compute platforms, partnerships, and software to provide that gateway between plant floor and enterprise without disturbing the control platform.
Therefore we are witnessing proliferating partnerships among IT and OT automation suppliers in order to provide complete solutions to customers.
Remember—it is all meaningless unless it gets translated into intelligent action to make the manufacturing supply chain more productive with better quality and more humane.