‘Rethink Data’ Report Reveals That 68% Of Data Available To Businesses Goes Unleveraged

I first started hearing seriously about data ops last year at a couple of IT conferences. Then a group of former Kepware executives founded High Byte to point data ops specifically to the manufacturing industry. I told them I thought they had something there.

A representative of Seagate Technology sent me information about a study done with IDC about data in organizations. I haven’t had a relationship with Seagate for many years, but this is a timely report about enterprise data pointing out that 68% of data available goes unleveraged and that manufacturing is a laggard in this arena.

As enterprise data proliferates at an unprecedented pace – set to grow at a 42.2.% annual rate over the next two years – a new report from Seagate and IDC has revealed that the majority (68%) of data available to enterprises goes unleveraged, meaning data management has become more important than ever. 

Furthermore and somewhat surprisingly, the manufacturing sector shows the lowest level of task automation in data management, lowest rate for full integration of data management functions as well as low adoption of both multicloud and hybrid cloud infrastructures. 

The report also identifies the missing link of data management—DataOps—which can help organizations harness more of their data’s value and lead to better business outcomes.

The report, Rethink Data: Put More of Your Data to Work—From Edge to Cloud is based on a survey of 1500 global enterprise leaders commissioned by Seagate and conducted by the research firm IDC.

“The report and the survey make clear that winning businesses must have strong mass data operations,” says Seagate CEO Dave Mosley. “The value that a company derives from data directly affects its success.”

Some additional findings include:

  • The top five barriers to putting data to work are: 1) making collected data usable, 2) managing the storage of collected data, 3) ensuring that needed data is collected, 4) ensuring the security of collected data, and 5) making the different silos of collected data available.
  • Managing data in the multicloud and hybrid cloud are top data management challenges expected by businesses over the next two years.
  • Two thirds of survey respondents report insufficient data security, making data security an essential element of any discussion of efficient data management.

The missing link of data management is reported to be data operations, or DataOps. IDC defines DataOps as “the discipline connecting data creators with data consumers.” While the majority of respondents say that DataOps is “very” or “extremely” important, only 10% of organizations report having implemented DataOps fully. The survey demonstrated that, along with other data management solutions, DataOps leads to measurably better business outcomes. It boosts customer loyalty, revenue, profit, cost savings, plus results in other benefits.

“The findings of this study illustrating that more than two-thirds of available data lies fallow in organizations may seem like disturbing news,” said Phil Goodwin, research director, IDC and principal analyst on the study. “But in truth, it shows how much opportunity and potential organizations already have at their fingertips. Organizations that can harness the value of their data wherever it resides—core, cloud or edge—can generate significant competitive advantage in the marketplace.”

AI Research For Tomorrow’s Production

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.

Laser Micromachining Manufacturing Unveiled

As you add electronic sensing and control and networking to machinery, you can take a process to the next level. I’ve been impressed with the growing development of tighter tolerances and then better variety of materials for 3D printing (additive manufacturing). Here is an example of expanding the use of automated “subtractive” manufacturing—micro machining.

6-D Laser LLC was formed in 2018 as an affiliate of leading nanometer-level motion control specialist ALIO Industries, with the mission of integrating ultrafast laser processing with precision multi-axis motion systems. 6-D Laser offers Hybrid Hexapod-based laser micromachining systems for wide-range taper angle control, 5-Axis Laser Gimbal-based systems for laser processing 3D substrates, and unlimited field of view scanning solutions for laser processing large-format substrates.

Coming out of stealth mode and coinciding with its official launch in 2020, 6-D Laser has launched its website (www.6dlaser.com), and has also announced that the company will be showcasing its radical new approach to laser micro processing at the SPIE Photonics West event, booth 2149, 4-6 February in San Francisco, CA.

6D Laser’s central mission addresses limitations of existing laser processing systems which are largely due to sub-optimal positioning systems used by most system integrators. 6-D Laser tackles this problem by integrating ultra-fast laser material processing with the 6-D nanometer-level precision motion control solutions in which ALIO Industries specializes.

At the heart of 6-D Laser’s integrated ultrafast laser micromachining system is ALIO Industries’ Hybrid Hexapod, which takes a different approach to traditional 6 Degree of Freedom (6-DOF) positioning devices, and exhibits much higher performance at extremely competitive prices. Rather than 6 independent legs (and 12 connection joints) ALIO’s approach combines a precision XY monolithic stage, tripod, and continuous rotation theta-Z axis to provide superior overall performance.

The combination of serial and parallel kinematics at the heart of ALIO’s 6-D Nano Precision® is characterized by orders-of-magnitude improvements (when compared to traditional hexapods) in precision, path performance, speed, and stiffness. The Hybrid Hexapod® also has a larger work envelope than traditional hexapods with virtually unlimited XY travel and fully programmable tool center point locations. The Hybrid Hexapod® has less than 100 nm Point Precision® repeatability, in 3-dimensional space.

​6D Laser vertically integrates all of the sub-systems required for precision laser micro-processing, and it does this by forming strategic partnerships with key component and subsystem suppliers that are required to achieve the goals of demanding precision applications. In addition to its association with ALIO, 6-D Laser has also partnered with SCANLAB GmbH, which together with ACS Motion Control, has developed an unlimited field-of-view (UFOV) scanning solution for coordinate motion control of the galvo scanner and positioning stages called XLSCAN. 6-D Laser has also partnered with NextScanTechnology to provide high-throughput scanning systems that take advantage of the high rep-rates in currently available in ultrafast lasers, and Amplitude Laser, a key supplier of ultrafast laser systems for industrial applications.

Dr. Stephen R. Uhlhorn, CTO at 6-D Laser says, “Introducing an integrated ultrafast laser micromachining system that combines the positioning capabilities of the Hybrid Hexapod®, with high-speed optical scanning leads to a system that can process hard, transparent materials with wide-range taper angle control for the creation of high aspect ratio features in thick substrates, without limitations on the feature or field size.”

Ultrafast laser ablative processes, which remove material in a layer-by-layer process, result in machined features that have a significant side wall taper. For example, a desired cylindrical hole will have a conical profile. Taper formation is difficult to avoid in laser micromachining processes that are creating deep features (> 100 microns). Precision scanheads can create features with near-zero angle side walls, but they are limited to small angles of incidence (AOI) and small field sizes by the optics in the beamline.

Uhlhorn continues, “6-D Laser’s micromachining system controls the AOI and resulting wall taper angle through the Hybrid Hexapod® motion system, and the programmable tool center point allows for the control of the AOI over the entire galvo scan field, enabling the processing of large features.”

About 6-D Laser LLC
6D Laser, LLC, an affiliate of ALIO Industries, Inc, was founded in 2018 by C. William Hennessey and Dr. Stephen R. Uhlhorn. ALIO Industries is an industry-leading motion system supplier, specializing in nano-precision multi-axis solutions. 6D Laser was formed with the mission of integrating ultrafast laser processing with precision multi-axis motion systems, including ALIO’s Patented Hybrid Hexapod. The integration of ALIO True Nano motion systems with key sub-system suppliers, through strategic partnerships with Amplitude Laser, SCANLAB, and ACS Motion Control, enables a new level of precision and capability for advanced manufacturing.

www.6DLaser.com ​​​​​
www.microprm.com

Manufacturers Wish For Open Additive Manufacturing Ecosystems

Manufacturers Wish For Open Additive Manufacturing Ecosystems

Originally 3D printing, aka additive manufacturing, seemed more a Maker’s machine and novelty with possible future applications. “Printers” were developed for one material, and one company sold the package. I did not think deeply about the machines but continued to watch developments.

The first constraint I discovered for widespread manufacturing adoption was holding tolerances. Researchers and engineers have tackled that problem.

A recent survey of manufacturers revealed that virtually all (99%) manufacturing executives surveyed believe an open ecosystem is important to advance 3D printing at scale. While 85% of manufacturers reported that industrial-scale AM has the potential to increase revenue for their business.

However, the research sponsored by 3D printing / additive manufacturing company Essentium and said to be conducted by an independent global research firm also reported that 22% said their 3D printing efforts have resulted in vendor lock-in that limits flexibility. Note that Essentium manufactures open systems. I have witnessed and written about the value of open ecosystems as a fulcrum for fostering innovation. I don’t know enough to endorse Essentium, but I do endorse the concept.

According to Essentium, the industrial AM market has been dominated by closed systems where customers are locked into vendors’ hardware, processes and materials. As the technology obstacles around economics, scale, strength and speed of production fall away, the number of manufacturers using 3D printing for full-scale production has doubled compared to last year (40% in 2019; 21% in 2018). Manufacturers are now demanding open ecosystems to overcome system inflexibility and use the materials of their choice – 50% of companies said they need high quality and affordable materials to meet the growing demand for industrial 3D printed parts.

An open additive ecosystem will see more partnerships focused on giving customers greater control of their innovation, more choice in materials, and industrial-scale production at ground-breaking economics. Market demand for Essentium’s open 3D printing ecosystem, developed in collaboration with multinational chemical company BASF and 3D software developer Materialise NV, is a clear indication that an open ecosystem approach is addressing unmet needs in the industrial additive market.

Blake Teipel, CEO and Co-founder, Essentium, said: “At Essentium, we strongly believe that an open ecosystem will be key to the evolution of Additive Manufacturing. Being locked into proprietary solutions that limit flexibility and choice is no longer an option if 3D printing is to become a serious contender as an industrial process for end-use products. An open market focused on developing new materials and better and faster machines is the only way for manufacturers to unlock new applications and new business opportunities. With this approach, the future belongs to the customer, not to the OEM.”

162 managers and executives from large manufacturing companies across the world completed the survey on their current experiences, challenges and trends with 3D printing for production manufacturing. Participants included a mix of roles and were from companies across industries including aerospace, automotive, consumer goods and contract manufacturing.

Manufacturers Wish For Open Additive Manufacturing Ecosystems

A Decade of Digitalization

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.

Innovation

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.

Digital Everything

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.

IT/OT

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.

New Entrants

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.

Strategy

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.

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