I have two Linux Foundation open source releases today. We connected through the EdgeX Foundry IoT platform. Then we discovered we had many common interests. One of the releases touches on a fundamental element of commerce and collaboration—trust. The Linux Foundation is an open source project. Open source is a powerful software development model. But, it takes many dedicated and talented people to accomplish the task. Why do people work on open source? LF conducted a survey to get an idea.
The Janssen Project Takes on World’s Most Demanding Digital Trust Challenges at Linux Foundation
New Janssen Project seeks to build the world’s fastest and most comprehensive cloud native identity and access management software platform
The Linux Foundation announced the Janssen Project, a cloud native identity and access management software platform that prioritizes security and performance for our digital society. Janssen is based on the Gluu Server and benefits from a rich set of signing and encryption functionalities. Engineers from IDEMIA, F5, BioID, Couchbase, and Gluu will make up the Technical Steering Committee.
Online trust is a fundamental challenge to our digital society. The Internet has connected us. But at the same time, it has undermined trust. Digital identity starts with a connection between a person and a digital device. Identity software conveys the integrity of that connection from the user’s device to a complex web of backend services. Solving the challenge of digital identity is foundational to achieving trustworthy online security.
While other identity and access management platforms exist, the Janssen Project seeks to tackle the most challenging security and performance requirements. Based on the latest code that powers the Gluu Server–which has passed more OpenID self-certification tests then any other platform–Janssen starts with a rich set of signing and encryption functionality that can be used for high assurance transactions. Having shown throughput of more than one billion authentications per day, the software can also handle the most demanding requirements for concurrency thanks to Kubernetes auto-scaling and advances in persistence.
“Trust and security are not competitive advantages–no one wins in an insecure society with low trust,” said Mike Schwartz, Chair of the Janssen Project Technical Steering Committee. “In the world of software, nothing builds trust like the open source development methodology. For organizations who cannot outsource trust, the Janssen Project strives to bring transparency, best practices and collective governance to the long term maintenance of this important effort. The Linux Foundation provides the neutral and proven forum for organizations to collaborate on this work.”
The Gluu engineering teams chose the Linux Foundation to host this community because of the Foundation’s priority of transparency in the development process and its formal framework for governance to facilitate collaboration among commercial partners.
New digital identity challenges arise constantly, and new standards are developed to address them. Open source ecosystems are an engine for innovation to filter and adapt to changing requirements. The Janssen Project Technical Steering Committee (“TSC”) will help govern priorities according to the charter. The initial TSC includes:
Michael Schwartz, TSC Chair, CEO Gluu
Rajesh Bavanantham, Domain Architect at F5 Networks/NGiNX
Rod Boothby, Head of Digital Trust at Santander
Will Cayo, Director of Software Engineering at IDEMIA Digital Labs
Ian McCloy, Principal Product Manager at Couchbase
Alexander Werner, Software Engineer at BioID
New Open Source Contributor Report from Linux Foundation and Harvard Identifies Motivations and Opportunities for Improving Software Security
New survey reveals why contributors work on open source projects and how much time they spend on security
The Linux Foundation’s Open Source Security Foundation (OpenSSF) and the Laboratory for Innovation Science at Harvard (LISH) announced release of a new report, “Report on the 2020 FOSS Contributor Survey,” which details the findings of a contributor survey administered by the organizations and focused on how contributors engage with open source software. The research is part of an ongoing effort to study and identify ways to improve the security and sustainability of open source software.
The FOSS (Free and Open Source Software) contributor survey and report follow the Census II analysis released earlier this year. This combined pair of works represents important steps towards understanding and addressing structural and security complexities in the modern-day supply chain where open source is pervasive but not always understood. Census II identified the most commonly used free and open source software (FOSS) components in production applications, while the FOSS Contributor Survey and report shares findings directly from nearly 1,200 respondents working on them and other FOSS software.
Key findings from the FOSS Contributor Survey include:
The top three motivations for contributors are non-monetary. While the overwhelming majority of respondents (74.87 percent) are already employed full-time and more than half (51.65 percent) are specifically paid to develop FOSS, motivations to contribute focused on adding a needed feature or fix, enjoyment of learning and fulfilling a need for creative or enjoyable work.
There is a clear need to dedicate more effort to the security of FOSS, but the burden should not fall solely on contributors. Respondents report spending, on average, just 2.27 percent of their total contribution time on security and express little desire to increase that time. The report authors suggest alternative methods to incentivizing security-related efforts.
As more contributors are paid by their employer to contribute, stakeholders need to balance corporate and project interests. The survey revealed that nearly half (48.7 percent) of respondents are paid by their employer to contribute to FOSS, suggesting strong support for the stability and sustainability of open source projects but drawing into question what happens if corporate interest in a project diminishes or ceases.
Companies should continue the positive trend of corporate support for employees’ contribution to FOSS. More than 45.45 percent of respondents stated they are free to contribute to FOSS without asking permission, compared to 35.84 percent ten years ago. However, 17.48 percent of respondents say their companies have unclear policies on whether they can contribute and 5.59 percent were unaware of what policies – if any – their employer had.
The report authors are Frank Nagle, Harvard Business School; David A. Wheeler, the Linux Foundation; Hila Lifshitz-Assaf, New York University; and Haylee Ham and Jennifer L. Hoffman, Laboratory for Innovation Science at Harvard.
As a companion to my Amazon Web Services (AWS) product release post, several company spokespeople have discussed their applications of the new AWS predictive maintenance and quality products.
“Industrial and manufacturing customers are constantly under pressure from their shareholders, customers, governments, and competitors to reduce costs, improve quality, and maintain compliance. These organizations would like to use the cloud and machine learning to help them automate processes and augment human capabilities across their operations, but building these systems can be error prone, complex, time consuming, and expensive,” said Swami Sivasubramanian, Vice President of Amazon Machine Learning for AWS. “We’re excited to bring customers five new machine learning services purpose-built for industrial use that are easy to install, deploy, and get up and running quickly and that connect the cloud to the edge to help deliver the smart factories of the future for our industrial customers.”
Fender Musical Instruments Corporation is an iconic brand and the world’s foremost manufacturer of guitars, basses, amplifiers, and related equipment. “Over the past year we worked with AWS to help develop the critical but sometimes overlooked part of running a successful manufacturing business, knowing your equipment condition. For manufacturers worldwide, maintaining equipment uptime is the only way to remain competitive in a global market. Unplanned downtime is costly both in loss of production and labor due to the fire-fighting nature of breakdowns,” said Bill Holmes, Global Director of Facilities at Fender. “Amazon Monitron can give both large industry manufacturers as well as small ‘mom and pop shops’ the ability to predict equipment failures, giving us the opportunity to preemptively schedule equipment repairs.”
RS Components is a leading player in the industrial components and predictive maintenance space. “We are constantly trying to innovate how we serve the maintenance needs of our customers. With the emergence of IoT, we have seen our customers looking to bring real-time condition monitoring capabilities into the factory environment to reduce reactive maintenance and improve asset reliability,” said Richard Jeffers, Technical Director at RS Components. “We are excited to be working with AWS to bring Amazon Monitron to our customers because it allows them to deploy a cost effective, easy to use, continuously improving condition monitoring solution and enable predictive maintenance across a broader set of equipment in their asset base. Although we stock over 500,000 products from 2,500 different suppliers, this is the first end-to-end wireless vibration and temperature condition monitoring solution in our portfolio. We plan to make Amazon Monitron available to our customers via our e-commerce platform, and leverage it to deliver condition-based monitoring and reliability services through RS Monition, our data led, reliability services business. Working with AWS will enable us to support our customers’ efforts to adopt IoT and machine learning as emerging technologies and accelerate their Industry 4.0 strategies.”
GS EPS is a South Korean Industrial Conglomerate. “We have been generating data across our assets for over a decade now but have only been using physics and rules based methods to gain insights into our data,” said Kang Bum Lee, Executive Vice President of GS EPS. “Amazon Lookout for Equipment is enabling our plant operation teams to build models on our equipment with no ML expertise required. We are leading the transformation of our organization into a data-driven work culture with AWS and Amazon Lookout for Equipment.”
Doosan Infracore is a leading global manufacturer of heavy duty equipment and engines. “Leveraging AI is critical in advancing Doosan’s next generation equipment, so we are working with AWS to develop use cases where automated and scalable machine learning could be leveraged,” said Mr. Jae Yeon Cho, Vice President of Doosan Infracore. “Based on this, we are excited to continue to work with AWS to leverage Amazon Lookout for Equipment in our next generation IoT platform.”
OSIsoft is a manufacturer of application software for real-time data management, called the PI System. “Today, there are more than 2 billion sensor-based data streams inside OSIsoft PI Systems, and thousands of customers relying on the PI System daily to run their operations. These customers are constantly looking for methods to easily serve up insights for improving their competitiveness. OSIsoft products can be integrated with AWS services to help customers unlock additional value from their data. Amazon Lookout for Equipment expands the scope of services and insights available to customers by delivering automated machine learning built specifically for equipment monitoring,” said Michael Graves, Director of Strategic Alliances at OSIsoft.
“Every month, millions of trucks enter Amazon facilities so creating technology that automates trailer loading, unloading, and parking is incredibly important,” said Steve Armato, VP Middle Mile Production Technology at Amazon.com. “Amazon’s Middle Mile Products & Technology (MMPT) has begun using AWS Panorama to recognize license plates on these vehicles and automatically expedite entry and exit for drivers. This enables safe and fast visits to Amazon sites, ensuring faster package delivery for our customers.”
BP is a global energy company, providing customers with fuel for transport, energy for heat and light, lubricants to keep engines moving, and the petrochemicals products used to make everyday items as diverse as paints, clothes, and packaging. The organization has 18,000 service stations and more than 74,000 employees worldwide. “Our engineering teams here at bpx are working very closely with AWS to build an IoT and cloud platform that will enable us to continuously improve the efficiency of our operations,” said Grant Matthews, Chief Technology Officer at BP America. “One of the areas we have explored as part of this effort is the use of computer vision to help us further improve security and worker safety. We want to leverage computer vision to automate the entry and exit of trucks to our facility and verify that they have fulfilled the correct order. Additionally, we see possibilities for computer vision to keep our workers safe in a number of ways, from monitoring social distancing, to setting up dynamic exclusion zones, and detecting oil leaks. AWS Panorama offers an innovative approach to delivering all of these solutions on a single hardware platform with an intuitive user experience. Our teams are excited to work with AWS on this new technology and expect it to help us address many new use cases.”
Siemens Mobility offers intelligent and efficient mobility solutions for urban, interurban, and freight transportation. “Siemens Mobility has been a leader for seamless, sustainable, and secure transport solutions for more than 160 years. The Siemens ITS Digital Lab is the innovation team in charge of bringing the latest digital advances to the traffic industry and uniquely positioned to provide data analytics and AI solutions to public agencies,” said Laura Sanchez, Innovation Manager, Siemens Mobility ITS Digital Lab. “As cities face new challenges, municipalities have turned to us to innovate on their behalf. Cities would like to understand how to effectively manage their assets and improve congestion and direct traffic. We want to use AWS Panorama to bring computer vision to existing security cameras to monitor traffic and intelligently allocate curbside space, help cities optimize parking and traffic, and improve quality of life for their constituents.”
ADLINK Technology offers hardware/software platforms enabling customers to implement edge AI solutions for real-time delivery of actionable data in industrial markets such as manufacturing, transportation, healthcare, energy, and communications. “The integration of AWS Panorama on ADLINK’s industrial vision systems makes for truly plug-and-play computer vision at the edge,” said Elizabeth Campbell, CEO at ADLINK USA. “In 2021, we will be making AWS Panorama-certified ADLINK NEON cameras powered by NVIDIA Jetson AGX Xavier available to customers to drive high-quality computer vision powered outcomes much, much faster. This allows ADLINK to deliver ML digital experiments and time to value for our customers more rapidly across logistics, manufacturing, energy, and utilities use cases.”
INDUS.AI is the world’s most advanced construction intelligence solution that enables real estate investors, owners, developers, and general contractors to have real-time visibility and actionable insights into all activities, productivity, and risks at their construction sites. INDUS.AI seeks to make construction sites and projects safer, more efficient, and completely transparent. “Construction zones are dynamic environments. At any given time you’ve got hundreds of deliveries and subcontractors sharing the site with heavy equipment and it’s changing every day. INDUS.AI is focused on delivering construction intelligence for general contractors,” said Matt Man, CEO of INDUS.AI. “Computer vision is an especially valuable tool for this because of its ability to handle multiple tasks at once. We are looking forward to delivering real-time insights on job-site management and safety in a SaaS-like experience for AWS Panorama customers.”
Dafgards is a household name in Sweden, manufacturing a broad assortment of foods. One of their most successful brands is Billys Pan Pizza, a microwaveable pizza baked and packed at a speed of 2 pizzas per second. “To uphold our brand and deliver the freshest and tastiest customer experience, we want to ensure that all our pizzas are adequately covered in cheese and with the correct toppings. Previously, we installed a machine vision system to detect proper coverage of cheese across a pizza’s surface. While this system served well for our original inspection requirement, it was unable to detect defects on new product types that include multiple toppings,” said Fredrik Dafgård, Head of Operational Excellence and Industrial IoT at Dafgards. “Amazon Lookout for Vision automates and scales inspection of diverse product types such as a cheese pizza with vegetables. We successfully expanded our quality assurance for new product types with minimal impact to operations.”
GE Healthcare is a leading global medical technology and digital solutions innovator that develops, manufactures, and distributes diagnostic imaging agents, radiopharmaceuticals, medical diagnostic equipment, including CT and MRI machines, and intelligent devices supported by its Edison intelligence platform. “Today, we use human inspection to verify the quality of our medical equipment. To uphold our brand and deliver best-in-class products trusted by healthcare professionals, we’re excited about the possibility of using Amazon Lookout for Vision to programmatically improve the speed, consistency, and accuracy of detecting product defects across our factories in Japan and potentially in other plants globally in the near future,” said Kozaburo Fujimoto, Operating Officer, General Manager, Manufacturing Division, Plant Manager at GE Healthcare Japan.
Nukon, a SAGE Group company, is a digital transformation consultancy and delivery company that delivers custom-designed solutions which combine strategy, analysis, and technology to give visibility into key business processes so they can be optimized. “We are excited about how Amazon Lookout for Vision will help us detect product defects in real-time within the manufacturing facility for our sister company, SAGE Automation, in line with their stringent quality control program. We are excited to now apply this technology to other manufacturers and integrate it into their quality systems,” said Rafael Amaral, Chief Technology Officer at Nukon.
As usual, there is more than one conference this week. I have two more to report on. This one is a bit of a surprise. I was providing thoughts about potential disruption of the mainstream automation market from below when I received this news from Amazon Web Services (AWS). This is a lengthy report, because there is a lot of news here. And it’s worth pausing to consider. I worked with another IT company a few years ago about predictive maintenance as a possible market. That company did not see the sales possibilities and bailed.
AWS is not just a remote server farm for cloud services. It has packed tons of tech into the company. Imagine your possibilities using this.
Amazon Monitron provides customers an end-to-end machine monitoring solution comprised of sensors, gateway, and machine learning service to detect abnormal equipment conditions that may require maintenance
Amazon Lookout for Equipmentgives customers with existing equipment sensors the ability to use AWS machine learning models to detect abnormal equipment behavior and enable predictive maintenance
AWS Panorama Appliance enables customers with existing cameras in their industrial facilities with the ability to use computer vision to improve quality control and workplace safety
AWS Panorama Software Development Kit (SDK) allows industrial camera manufacturers to embed computer vision capabilities in new cameras
Amazon Lookout for Visionuses AWS-trained computer vision models onimages and video streams to find anomalies and flaws in products or processes
Axis, ADLINK Technology, BP, Deloitte, Fender, GE Healthcare, and Siemens Mobility among customers and partners using new AWS industrial machine learning services
This week at AWS re:Invent, Amazon Web Services Inc. (AWS), an Amazon.com company, announced Amazon Monitron, Amazon Lookout for Equipment, the AWS Panorama Appliance, the AWS Panorama SDK, and Amazon Lookout for Vision. Together, these five new machine learning services help industrial and manufacturing customers embed intelligence in their production processes in order to improve operational efficiency, quality control, security, and workplace safety.
The services combine sophisticated machine learning, sensor analysis, and computer vision capabilities to address common technical challenges faced by industrial customers. To learn more about AWS’s new industrial machine learning services, visit https://aws.amazon.com/industrial/
Companies are increasingly looking to add machine learning capabilities to industrial environments, such as manufacturing facilities, fulfillment centers, and food processing plants. For these customers, data has become the connective tissue that holds their complex industrial systems together. With these evolving needs and opportunities, industrial companies have asked AWS to help them leverage the cloud, the industrial edge, and machine learning together to get even more value from the vast amounts of data being generated by their equipment.
Amazon Monitron and Amazon Lookout for Equipment enable predictive maintenance powered by machine learning
In order to make predictive maintenance work, companies have historically needed skilled technicians and data scientists to piece together a complex solution from scratch. This included identifying and procuring the right type of sensors for the use case and connecting them together with an IoT gateway. Companies then had to test the monitoring system and transfer the data to on-premises infrastructure or the cloud for processing. Only then could the data scientists on staff build machine learning models to analyze the data for patterns and anomalies or create an alerting system when an outlier was detected.
Some companies have invested heavily in installing sensors across their equipment and the necessary infrastructure for data connectivity, storage, analytics, and alerting. But even these companies typically use rudimentary data analytics and simple modeling approaches that are expensive and often ineffective at detecting abnormal conditions compared to advanced machine learning models. Most companies lack the expertise and staff to build and refine the machine learning models that would enable highly accurate predictive maintenance. As a result, few companies have been able to successfully implement predictive maintenance, and those that have done it are looking for ways to further leverage their investment, while also easing the burden of maintaining their homegrown solutions.
Here’s how the new AWS machine learning services can help:
For customers who do not have an existing sensor network, Amazon Monitron offers an end-to-end machine monitoring system comprised of sensors, a gateway, and a machine learning service to detect anomalies and predict when industrial equipment will require maintenance. Amazon Monitron detects when machines are not operating normally based on abnormal fluctuations in vibration or temperature, and notifies customers when to examine machinery in order to determine if preventative maintenance is needed. The end-to-end system includes IoT sensors to capture vibration and temperature data, a gateway to aggregate and transfer data to AWS, and a machine learning cloud service that can detect abnormal equipment patterns and deliver results in minutes with no machine learning or cloud experience required. Amazon Monitron also includes a mobile app for a customer’s onsite maintenance technicians to monitor equipment behavior in real time https://aws.amazon.com/monitron.
For customers that have existing sensors but don’t want to build machine learning models, Amazon Lookout for Equipment provides a way to send their sensor data to AWS to build models for them and return predictions to detect abnormal equipment behavior. To get started, customers upload their sensor data to Amazon Simple Storage Service (S3) and provide the S3 location to Amazon Lookout for Equipment. Amazon Lookout for Equipment can also pull data from AWS IoT SiteWise, and works seamlessly with other popular machine operations systems like OSIsoft. Amazon Lookout for Equipment analyzes the data, assesses normal or healthy patterns, and then uses the learnings from all of the data on which it is trained to build a model that is customized for the customer’s environment. Amazon Lookout for Equipment can then use the machine learning model to analyze incoming sensor data and identify early warning signs for machine failure. https://aws.amazon.com/lookout-for-equipment.
AWS Panorama uses computer vision to improve industrial operations and workplace safety
Many industrial and manufacturing customers want to be able to use computer vision on live video feeds of their facility and equipment to automate monitoring or visual inspection tasks and to make decisions in real time. However, the typical monitoring methods used today are manual, error prone, and difficult to scale.
Customers could build computer vision models in the cloud to monitor and analyze their live video feeds, but industrial processes typically need to be physically located in remote and isolated places, where connectivity can be slow, expensive, or completely non-existent. This type of video feed could be automatically processed in the cloud using computer vision, but video feeds are high bandwidth and can be slow to upload. Most customers end up running unsophisticated models that can’t be programmed to run custom code that integrates into the industrial machines. Here’s how AWS can now help:
The AWS Panorama Appliance provides a new hardware appliance that allows organizations to add computer vision to existing on-premises cameras that customers may already have deployed. Customers start by connecting the AWS Panorama Appliance to their network, and the device automatically identifies camera streams and starts interacting with the existing industrial cameras. The AWS Panorama Appliance is integrated with AWS machine learning services and IoT services that can be used to build custom machine learning models or ingest video for more refined analysis.
The AWS Panorama Software Development Kit (SDK) enables hardware vendors to build new cameras that can run meaningful computer vision models at the edge. Cameras that are built with the AWS Panorama SDK run computer vision models for use cases like detecting damaged parts on a fast-moving conveyor belt or spotting when machinery is outside of a designated work zone. Customers can train their own models in Amazon SageMaker and deploy them on cameras built with the AWS Panorama SDK with a single click. Customers can also add Lambda functions to cameras built with the AWS Panorama SDK to be alerted to potential issues via text or email. AWS also offers pre-built models for tasks like PPE detection and social distancing and can deploy these models in minutes without doing any machine learning work or special optimizations.
https://aws.amazon.com/panorama.
Amazon Lookout for Vision offers automated fast and accurate visual anomaly detection for images and video at a low cost
One use case where AWS customers are excited to deploy computer vision with their cameras is for quality control. Machine learning-powered visual anomaly systems remain out of reach for the vast majority of companies. Here’s how AWS can now help these companies:
Amazon Lookout for Vision offers customers a high accuracy, low-cost anomaly detection solution that uses machine learning to process thousands of images an hour to spot defects and anomalies. Customers send camera images to Amazon Lookout for Vision in batch or in real-time to identify anomalies, such as a crack in a machine part, a dent in a panel, an irregular shape, or an incorrect color on a product. Amazon Lookout for Vision then reports the images that differ from the baseline so that appropriate action can be taken. Amazon Lookout for Vision also runs on Amazon Panorama appliances. Customers can run Amazon Lookout for Vision in AWS starting today, and beginning next year, customers will be able to run Amazon Lookout for Vision on AWS Panorama Appliances and other AWS Panorama devices so customers will be able to use Amazon Lookout for Vision in locations where Internet connectivity is limited or non-existent. https://aws.amazon.com/lookout-for-vision.
I have to admit, I’m much less tired than I’ve been this week relative to every other year beginning in 1997 at my first Automation Fair by Rockwell Automation. I sat in on a few Rockwell sessions and even squeezed in a robotic press conference from a different supplier. Busy day.
Bear with me a moment. One of my favorite philosophers is Pierre Teilhard de Chardin. He was a French Jesuit priest and a paleontologist. In one of his books, he used the metaphor of an ascending spiral to describe the history of evolution. Or, if theology is your hobby, try outlining John’s First Letter (from the Christian New Testament). It won’t come out Roman Numeral I with A, B, C and then Roman Numeral II, etc. That outline will also look like a spiral with each new idea ascending above the earlier one.
The reason I bring this up is that I listened to all the presentations and, with one filter in place, it sounded much like the same words as seven years ago. In fact, many of the ideas could date back 20 years. On the other hand, remove that filter and look at the presentations with a different filter, we see that everything is the same, but at a much higher level.
Each year, both the technologies and business contexts have grown over the year before until you realize that the seven-years-ago-me would not recognize much of the today-me.
The constant theme of several years returned in force this year—Connected Enterprise. And the Connected Enterprise does not work for customers unless the supplier brings partners. Rockwell Automation spokespeople prominently displayed this year’s premier partners—Microsoft, PTC, Emulate3D, Ansys, Kalypso.
Cloud is accepted as commonplace. It’s just one of the gang. Not a lot of discussion of Edge except for a short introduction of Microsoft Azure Edge technologies. Ethernet is now so commonplace that it was not mentioned. However, MES (the manufacturing execution software) received more mentions that a center midfielder in the English Premier League gets touches of the ball. Almost every case study mentioned it.
I went to the Milwaukee headquarters for the first time in the mid-90s for a week-long training class. It was brutal, by the way. But those of us smart enough to wait until we finished homework before we got our beer finished high on the list (I think I was 3rd in my class). One of the features was an automated manufacturing line for the new IEC-style contactors. Guess what? Featured this year was a brand-new automated assembly line making—contactors. It looked pretty good.
The contactor line was part of a Rockwell supply chain tour of plants in the US, Mexico, Singapore, and Poland exhibiting how Rockwell uses its own products plus those of its partners to maintain a robust internal supply chain.
The company has come a long way from the controller and contactor company I knew 30 years ago. They proved to me (not that I don’t have many other questions) that they are serious about the Connected Enterprise. It has progressed up the spiral.
Not to mention, this year I don’t have to travel on my Birthday, which is this week.
Rockwell Automation software announced a set of new capabilities for FactoryTalk InnovationSuite, powered by PTC. I have previously written about some other updates plus a renewed and expanded partnership between RA and PTC.
Enhancements to InnovationSuite include:
FactoryTalk Analytics now offers a comprehensive array of simplified data science capabilities for multiple personas—process engineers, data scientists, and citizen data scientists—and reduces analytics data preparation effort. New capabilities include end-to-end data orchestration with visual data modelling, open-stack connectivity, pre-built analytical templates, and self-service model deployment for a full-service customer journey.
The Rockwell Automation Digital Thread enables a collaborative workflow across product designers, production engineers, and OEM suppliers, optimizing the design process from the beginning. As data models born in the design phase are automatically used in advanced analytics applications, insights can be leveraged to achieve new levels of performance.
With the acquisition of Kalypso, Rockwell Automation now offers a full suite of consulting, data science, technology, business process management and managed services. With approximately 250 new InnovationSuite customers and a library of dozens of repeatable industrial use cases, the Rockwell Automation consulting and delivery services are a component of the InnovationSuite digital transformation journey to accelerate time to value.
Comments from executives:
“The need for digital transformation has increased significantly as our customers accelerate innovation, maximize workforce productivity, and optimize operations,” said Arvind Rao, director of product management at Rockwell Automation. “These new capabilities combined with our industry-leading partner ecosystem helps us extend our technology and solutions leadership, meeting our customers’ needs for simplicity, scale, and domain expertise.”
“We are pleased to be collaborating with the Rockwell Automation team on the next generation of our InnovationSuite offering,” said Don Busiek, senior vice president, strategic alliances, PTC. “As we help organizations realign their digital transformation goals to combat the current macroeconomic environment, we are confident that InnovationSuite offers the most comprehensive and effective way to optimize their people, products, and processes – empowering manufacturers to embrace their new normal.”
We saw something of this new release of Ignition HMI/SCADA by Inductive Automation at the Ignition Community Conference in September. The key element of the release is that it’s a long-term support release. But it also expands upon some of the ground-breaking features introduced last year in Ignition 8.
Inductive Automation released Ignition 8.1, which will help users develop Ignition projects more rapidly and use them more effectively. Ignition by Inductive Automation is an industrial application platform with tools for building solutions in human-machine interface (HMI), supervisory control and data acquisition (SCADA), manufacturing execution systems (MES), and the Industrial Internet of Things (IIoT). Ignition is used in virtually every industry in more than 100 countries and has been implemented at 54 of the Fortune 100 companies.
Ignition 8.1 can help users future-proof their systems for years to come. It’s a Long-Term Support release, so it will be supported by Inductive Automation for five years. While 8.1 is focused on performance and stability, it also includes several new features.
“Ignition 8.1 is all the innovation and power of the Ignition platform, refined into a high-performing, secure, and reliable package ready for the future,” said Carl Gould, director of software engineering for Inductive Automation.
Ignition 8.1 builds upon Ignition’s unlimited licensing model, cross-platform compatibility, modular approach, and use of open technology standards. Last year’s release of Ignition Perspective brought powerful new capabilities to mobile devices. It saved development time by allowing users to design a single Perspective application that displays properly on screens of any size. And it put full control of the plant floor on smartphones and tablets.
With Ignition 8.1’s new Perspective Workstation, users can deploy native applications to any HMI, desktop, workstation, and multi-monitor configuration without a third-party web browser. Screens can be displayed in full-screen kiosk mode with no distractions from the underlying operating system.
Perspective Symbols make it easier and faster than ever to create attractive HMIs. Symbols have dynamic data models, so binding them to process values is drag-and-drop. Each Symbol comes in three different styles, so users can customize to their preferences. Users can easily add animation and supporting text.
Perspective Power Chart allows users to easily create runtime-configurable time series charts from Tag Historian data. Users can quickly generate ad hoc charts within a Perspective session. Power Chart also adapts automatically for mobile screens.
Ignition 8.1 also includes several shortcuts to save time in development, a more powerful tag browser, and the ability to work with Docker Hub. For new users, Ignition Quick Start has several tools to help people quickly get going on creating projects. All of these new features save time for developers.
For more information on Ignition 8.1, visit Inductive Automation’s What’s New webpage.
