by Gary Mintchell | May 22, 2018 | Internet of Things, Manufacturing IT, Technology
A few of us gathered for a round table discussion of Internet of Things while I was at Dell Technologies World at the beginning of the month. I arrived a little early and had a private round table for several minutes before others arrive and the discussion became broader.
Ray O’Farrell, CTO of VMware and GM of IoT at Dell Technologies, said the focus of last 6 months since the new Internet of Things organization was announced included these three points:
1. Dell is 7 companies, trying to achieve one cohesive strategy across all; one organization when facing customers.
2. Best way is to work within the ecosystem, that is history of VMWare.
3. Building technology and leverage solutions. This is a complex undertaking as not all challenges within IoT are alike—there are few cookie cutter applications.
The evolution of Internet of Things within Dell to Dell EMC to Dell Technologies constitutes an upward spiraling path encompassing the greater breadth of technologies and organization reflecting the post-merger company. When I first came along, the concept was building an ecosystem around selling an edge device appliance. Now the strategy is much broader bringing the goal of IT/OT convergence closer to reality. As I’ve mentioned before, the IT companies are attacking that convergence from the IT side after years of manufacturing/production oriented suppliers trying to accomplish the same thing from the OT side. Maybe like the old country song we’ll meet in the middle someday.
Everyone talks Artificial Intelligence (AI) these days, and Dell Technologies is not exception. However, AI is not the science fiction doom and gloom predicted by Ray Kurzweil, Elon Musk, and others. Mostly it entails machine learning (ML) from detected patterns in the data.
Or as Dell Technologies says, it is applying AI and ML technology to turn data into intelligent insights, drive a faster time to market, and achieve better business outcomes.
News summary
• Dell EMC PowerEdge expands portfolio to accelerate AI-driven workloads, analytics, deployment and efficiency
• Deepens relationship with Intel to advance AI community innovation, machine learning (ML) and deep learning (DL) capabilities with Dell EMC Ready Solutions
• Dell Precision Optimizer 5.0 now enhanced with machine learning algorithms, intelligently tunes the speed and productivity of Dell Precision workstations.
• Dell EMC uses AI, ML and DL to transform support and deployment
14th generation Dell EMC PowerEdge four-socket servers and Dell Precision Optimizer 5.0 are designed to further strengthen AI and ML capabilities.
According to the recently released update of the Enterprise Strategy Group (ESG) 2018 IT Transformation Maturity Curve Index, commissioned by Dell EMC, transformed companies are 18X more likely to make better and faster data-driven decisions than their competition. Additionally, transformed companies are 22X as likely to be ahead of the competition with new products and services to market.
“The Internet of Things is driving an onslaught of data and compute at the edge, requiring organizations to embrace an end-to-end IT infrastructure strategy that can effectively, efficiently and quickly mine all that data into business intelligence gold,” said Jeff Clarke, vice chairman, Products & Operations, Dell. “This is where the power of AI and machine learning becomes real – when organizations can deliver better products, services, solutions and experiences based on data-driven decisions.”
Unlike competitors’ four-socket offerings, these servers also support field programmable gate arrays (FPGAs)3, which excel on data-intensive computations. Both servers feature OpenManage Enterprise to monitor and manage the IT infrastructure, as well as agent-free Integrated Dell Remote Access Controller (iDRAC) for automated, efficient management to improve productivity.
Dell EMC is also announcing its next generation PowerMax storage solution, built with a machine learning engine which makes autonomous storage a reality.
Leveraging predictive analytics and pattern recognition, a single PowerMax system analyzes and forecasts 40 million data sets in real-time per array4, driving six billion decisions per day5 to automatically maximize efficiency and performance of mixed data storage workloads.
The new Dell Precision Optimizer 5.0 uses AI to automatically adjust applications running on Dell Precision workstations to maximize performance by:
• Custom-optimizing applications: Dell Precision Optimizer learns each application’s behavior in the background and uses that data to employ a trained machine learning model that will automatically adjust the system to optimized settings and deliver up to 394% improvement in application performance.
• Automating systems configuration adjustments: Once activated and a supported application is launched, the software automatically adjusts system configurations such as CPU, memory, storage, graphics and operating system settings.
Speaking of partners and collaboration, Dell Technologies and Microsoft join forces to build secure, intelligent edge-to-cloud solution featuring Dell Edge Gateways, VMware Pulse IoT Center, and Microsoft Azure IoT Edge
News summary
• Joint IoT solution helps simplify management, enhances security and help lowers cost of deployment at the edge
• Built on innovative analytics applications, management tools and edge gateways to enable network security from edge devices to the cloud
• Accelerates IoT adoption in industry verticals key to economic growth and development
The joint solution offers an underlying IoT infrastructure, management capabilities, and security for customers looking to deploy IoT for scenarios like predictive maintenance, supply chain visibility and other use cases. The solution will deliver:
• Intelligence at the edge with Microsoft Azure IoT Edge: This application extends cloud intelligence to edge devices so that devices can act locally and leverage the cloud for global coordination and machine learning at scale
• Management and monitoring of edge devices with VMware Pulse IoT Center: This provides more secure, enterprise-grade management and monitoring of diverse, certified edge devices including gateways and connected IoT devices, bios and operating systems. This ecosystem will be built over time involving deeper integration and certification to support customer requirements.
• High-performance, rugged Dell Edge Gateways: IoT devices with powerful dual-core Intel® Atom™ processors connect a variety of wired and wireless devices and systems to aggregate and analyze inputs and send relevant data to the cloud
VMware Pulse IoT Center will serve as the management glue between the hardware (Dell Edge Gateways or other certified edge systems), connected sensors and devices and the Microsoft Azure IoT Edge. Initially, Pulse will help to deploy the Microsoft Azure IoT Edge to the requisite edge systems so that it can start collecting, analyzing and acting on data in real-time.
by Gary Mintchell | May 21, 2018 | Internet of Things, Manufacturing IT, Software
Hannover Messe was the place to learn the latest about all things digital—digital twin, Industry 4.0, Industrial Internet of Things (IIoT). SAP was one of the many stops in my itinerary advancing the trend.
My contact at the SAP booth at Hannover wasn’t around when I arrived for my appointment, so I left—only to get a text a half-hour later that he had arrived. But I was off to another appointment by then. However I did glean this information from the company at and following the show.
SAP enters the digital twin era
SAP SE has introduced SAP S/4HANA Cloud for intelligent product design, a new solution for collaborative research and development.
The solution, which is built on SAP Cloud Platform using SAP’s latest digital twin technology, is one of the building blocks for a network of digital twins to enable new business models.
Powered by SAP Leonardo and integrated with business processes in the digital core, SAP S/4HANA Cloud for intelligent product design enables customers to accelerate product design and development with requirement-driven systems engineering and instant collaboration across an extended network of suppliers and partners.
“The solution provides shared views of digital twin information for customers to gain live insights on new products and to store, share and review engineering documents with internal and external participants,” said Bernd Leukert, Member of the Executive Board of SAP SE, Products & Innovation.
SAP’s network of digital twins synchronizes the virtual, physical, conditional, and commercial definitions of assets and products in real time to accelerate innovation, optimize operating performance, predict service requirements, improve diagnostics and enhance decision-making. It enables new levels of collaboration among manufacturers of products, operators of assets, suppliers and service companies. The approach combines digital twins with manufacturing solutions from SAP, cloud networks and SAP Leonardo capabilities, including machine learning, blockchain and Internet of Things (IoT), to optimize the product lifecycle with:
• Digital representation: SAP synchronizes digital twin business data, product information, asset master data and IoT-connected data from both on-premise and cloud solutions enabling companies to represent the world digitally. Solutions including SAP Predictive Engineering Insights, SAP Predictive Maintenance and Service and the SAP 3D Visual Enterprise applications provide access to rich data processing capabilities and live configuration, state, condition and control information.
• Business process: Rich enterprise-grade data processing capabilities allow customers to create, access and update digital twins to support business processes. SAP solutions provide an integrated data model from design, production and maintenance to service, including packaged integration to existing systems for computer-aided design, ERP, and product lifecycle management. Offerings providing end-to-end process support for manufacturers and operators include SAP S/4HANA, the SAP Engineering Control Center integration tool, SAP Hybris Service Cloud solutions, and the SAP Manufacturing Integration and Intelligence and SAP Manufacturing Execution applications.
• Business networks: With leading network offerings such as SAP Ariba solutions, SAP Asset Intelligence Network, and the SAP Distributed Manufacturing application, SAP is uniquely positioned to provide a virtual platform for collaboration on products and assets. The network of digital twins enables secure data access, sharing and governance on a global scale.
• Networks of digital representation: SAP enables twin-to-twin connections in systems within a specific asset and on an asset-to-asset level. SAP solutions such as SAP Asset Intelligence Network provide semantic and industry-standards support in an asset core modeling environment to enable live enrichment during the product or asset lifecycle.
Digital Manufacturing Cloud
SAP Digital Manufacturing Cloud helps companies optimize performance, elevate production quality and efficiency, and ensure worker safety.
Drawing on SAP’s expertise in the Industrial Internet of Things (IIoT), predictive analytics and supply networks, the solution enables manufacturers to deploy Industry 4.0 technologies in the cloud.
The new cloud solution extends and complements the digital manufacturing portfolio of on-premise solutions from SAP and is available in different bundles to serve manufacturers of varying sizes in both discrete and process industries and roles within their respective organizations.
SAP customers can choose from the SAP Digital Manufacturing Cloud solution for execution, which provides all solutions in the manufacturing cloud portfolio, or the SAP Digital Manufacturing Cloud solution for insights, which focuses on performance management and predictive quality.
“Manufacturers in the era of Industry 4.0 require solutions that are intelligent, networked and predictive,” said Leukert. “Our manufacturing cloud solutions help customers take advantage of the Industrial Internet of Things by connecting equipment, people and operations across the extended digital supply chain and tightly integrating manufacturing with business operations.”
SAP Digital Manufacturing Cloud includes the following:
• SAP Digital Manufacturing Cloud for execution: Industry0-enabled shop floor solution features “lot size one” and paperless production capabilities. It integrates business systems with the shop floor, allowing for complete component and material-level visibility for single and global installations.
• SAP Digital Manufacturing Cloud for insights: Centralized, data-driven performance management enables key stakeholders to achieve best-in-class manufacturing performance and operations.
• Predictive quality: This helps manufacturers gain valuable insights to conform to specifications across processes and streamline quality management. It also allows manufacturers to apply predictive algorithms that can reduce losses from defects, deficiencies or variations, and recommend corrective actions.
• Manufacturing network: The network provides a cloud-based collaborative platform integrated with SAP Ariba solutions connecting customers with manufacturing service providers, such as suppliers of 3D and computer numerical control (CNC) printing services, material providers, original equipment manufacturers (OEM) and technical certification companies.
Also at Hannover Messe 2018, SAP announced SAP Connected Worker Safety, a solution designed to reduce risks, costs and protect employees. Information from wearables and other sensor-enabled equipment can help companies react immediately to a hazardous situation or incident while proactively managing worker fatigue and other hazard inducers. Real-time information allows monitoring of compliance at all times against regulatory and other parameters.
by Gary Mintchell | Apr 19, 2018 | Asset Performance Management, Automation, Internet of Things, Operations Management
Artificial Intelligence, always known as AI, along with its sometime companion robots leads the mainstream media hype cycle. It’s going to put everyone out of jobs, destroy civilization as we know it, and probable destroy the planet.
I lived through the Japanese robotic revolution-that-wasn’t in the 80s. Media loved stories about robots taking over and how Japan was going to rule the industrialized world because they had so many. Probing the details told an entirely different story. Japan and the US counted robots differently. What we called simple pick-and-place mechanisms they called robots.
What set Japanese industrial companies apart in those days was not technology. It was management. The Toyota Production Method (aka Lean Manufacturing) turned the manufacturing world on its head.
My take for years based on living in manufacturing and selling and installing automation has been, and still is, that much of this technology actually assisted humans—it performed the dangerous work, removing humans from danger, taking over repetitive tasks that lead to long-term stress related injuries, and performing work humans realistically couldn’t do.
Now for AI. This press release went out the other day, “With AI, humans and machines work smarter and better, together.” So, I was intrigued. How do they define AI and what does it do?
Sensai, an augmented productivity platform for manufacturing operations, recently announced the launch of its pilot program in the United States. Sensai increases throughput and decreases downtime with an AI technology that enables manufacturing operations teams to effectively monitor machinery, accurately diagnose problems before they happen and quickly implement solutions.
The company says it empowers both people and digital transformation using a cloud-based collaboration hub.
“The possibility for momentous change within manufacturing operations through digital transformation is here and now,” said Porfirio Lima, CEO of Sensai. “As an augmented productivity platform, Sensai integrates seamlessly into old or new machinery and instantly maximizes uptime and productivity by harnessing the power of real time data, analytics and predictive AI. Armed with this information, every person involved – from the shop floor to the top floor – has the power to make better and faster decisions to increase productivity. Sensai is a true digital partner for the operations and maintenance team as the manufacturing industry takes the next step in digital transformation.”
By installing a set of non-invasive wireless sensors that interconnect through a smart mesh network of gateways, Sensai collects data through its IIoT Hub, gateways and sensors, and sends it to the cloud or an on-premise location to be processed and secured. Data visualization and collaboration are fostered through user-friendly dashboards, mobile applications and cloud-based connectivity to machinery.
The AI part
Sensai’s differentiator is that it provides a full state of awareness, not only of the current status, but also of the future conditions of the people, assets and processes on the manufacturing floor. Sensai will learn a businesses’ process and systems with coaching from machine operators, process and maintenance engineers. It will then make recommendations based on repeating patterns that were not previously detected. Sensai does this by assessing the team’s experiences and historical data from the knowledge base and cross checking patterns of previous failures against a real-time feed. With this information, Sensai provides recommendations to avoid costly downtime and production shutdowns. Sensai is a true digital peer connecting variables in ways that are not humanly possible to process at the speed required on a today’s modern plant floor.
About the Pilot Program
Participation in Sensai’s pilot program is possible now for interested manufacturers. Already incorporated throughout Metalsa, a leading global manufacturer of automotive structural components, Sensai is set to digitally disrupt the manufacturing industry through AI, including those in automotive, heavy metal and stamping, construction materials, consumer goods and more.
Porfirio Lima, Sensai CEO, answered a number of follow up questions I had. (I hate when I receive press releases with lots of vague benefits and buzz words.)
1. You mention AI, What specifically is meant by AI and how is it used?
Sensai uses many different aspects of Artificial Intelligence. We are specifically focused on machine learning (ML), natural language processing (NLP), deep learning, data science, and predictive analytics. When used together correctly, these tools serve a specific use case allowing us to generate knowledge from the resulting data. We use NLP to enable human and computer interaction helping us derive meaning from human input. We use ML and deep learning to learn from data and create predictive and statistical models. Finally, we use data science and predictive analytics to extract insights from the unstructured data deriving from multiple sources. All of these tools and techniques allow us to cultivate an environment of meaningful data that is coming from people, sensors, programmable logistics controllers (PLCs) and business systems.
2. “Learn processes through operators”—How do you get the input, how do you log it, how does it feed it back?
Our primary sources of data (inputs) are people, sensors, PLCs, and business systems. In the case of people on the shop floor or operators, we created a very intuitive and easy to use interface that they can use on their cellphones or in the Human Machine Interfaces (HMIs) that are installed in their machines, so they can give us feedback about the root causes of failures and machine stoppages. We acquire this data in real-time and utilize complex machine learning algorithms to generate knowledge that people can use in their day-to-day operations. Currently, we offer web and mobile interfaces so that users can quickly consume this knowledge to make decisions. We then store their decisions in our system and correlate it with the existing data allowing us to optimize their decision-making process through time. The more a set of decisions and conditions repeats, the easier for our system is to determine the expected outcome of a given set of data.
3. Pattern? What patterns? How is it derived? Where did the data come from? How is it displayed to managers/engineers?
We create “digital fingerprints” (patterns) with ALL the data we are collecting. These “patterns” allow us to see how indicators look before a failure occurs, enabling us to then predict when another failure will happen. Data comes from the machine operators, the machines or equipment, our sensors, and other systems that have been integrated to Sensai’s IIOT hub.
We trigger alerts to let managers and engineers know that a specific situation is happening. They are then able to review it in their cellphones as a push notification that takes them to a detailed description of the condition in their web browser where they can review more information in depth.
4. What specifically are you looking for from the pilots?
We are not a cumbersome solution, for us is all about staying true about agility and value creation. We look for pilots that can give us four main outcomes:
– Learn more about our customer needs and how to better serve them
– A clear business case that can deliver ROI in less than 6 months after implementation and can begin demonstrating value in less than 3 months.
– A pilot that is easy to scale up and replicate across the organization so we can take the findings from the pilot and capitalize them in a short period of time.
– A pilot that can help Sensai and its customers create a state of suspended disbelief that technology can truly deliver the value that is intended and that can be quickly deployed across the entire organization.
by Gary Mintchell | Sep 15, 2017 | News, Operations Management, Software
Whether it is the Industrial Internet of Things or Industry 4.0 or Smart Manufacturing no benefits are garnered at the end without a superb analytics engine. Recently I talked with Humera Malik, CEO of Canvass Analytics, about a new analytics company and product that brings Artificial Intelligence (AI) and Machine Learning (ML) to the field.
Early in my management career we called accounting “ancient historians” because reports only came out 10 days following a month end. That is too late to be what we call these days “actionable information.”
Turns out that a similar problem has existed in the predictive analytics field. OSIsoft and others have provided tools to capture huge amounts of industrial and manufacturing data. To get anything out of it you needed to establish a project, bring in a bunch of data scientists, and try to glean some trends or fit some models.
What was needed was a powerful engine that can use this data closer to real time, fit it into a model (selecting one from among several), and give operators, maintenance technicians, engineers, and others information in a useable time frame without bringing in a bunch of data scientists. These data scientists it turns out need to reside in the software. The entire process must be transparent to the user.
Enter Canvass Analytics, a provider of AI-enabled predictive analytics for Industrial IoT, which just announced a partnership with OSIsoft, a global leader in operational intelligence, that will enable Industrial companies to accelerate the return on investment of their IoT initiatives.
Malik commented, “Predictive and automated analytics gives operations teams the insights to answer questions such as, how can I increase yield, how can I reduce downtime and how can I reduce my maintenance costs? Canvass’ AI-enabled analytics platform accelerates the delivery of predictive insights by automating data analysis and leveraging machine learning technologies to adapt to data changes in real-time. For operations teams, this means they have the latest intelligence in order to make critical operational decisions.”
The combination of OSIsoft’s methodology to collect, store and stream data from any Industrial IoT source with Canvass’ AI-enabled automated analytics platform brings a new approach to creating predictive models that continually retrain themselves. With the resulting insight, Industrial companies have the potential to reduce plant maintenance by up to 50 percent and optimize plant operations by 30 percent.
“We are enthusiastic about the value that we see companies like Canvass Analytics extracting from the vast amounts of IIoT and other streaming data that we collect in our role as the single source of the truth,” said J. Patrick Kennedy, founder and CEO of OSIsoft.
by Gary Mintchell | Jun 13, 2017 | Asset Performance Management, Automation, Operations Management
This is “TechED” week for Rockwell Automation with “Shelby” the chat bot collaboration tool featured prominently at the opening keynote presentations–a software startup within the organization.
TeamONE was one of two new products featured this week. The other an appliance to help manage an EtherNet/IP network.
More than 2,000 people gathered in Orlando for the 20th edition of this distributor and customer learning event. Most of the sessions were deep dives into product and technology. I’ve sat in a few sessions, and they reminded me of how much I miss the deep dives into how to use products and technology instead of the usual marketing overviews that I receive.
The goals seem to be offering new workforce additions tools that they’ll be familiar with, enabling quicker setup and troubleshooting, and supporting teams.
One other maintenance-oriented product I’ll touch on is a support service for predictive maintenance.
The new products are FactoryTalk Analytics for Devices appliance and a FactoryTalk TeamONE Standard Edition app. These off-the-shelf offerings require minimal configuration and can help solve common maintenance problems faster, which keeps unplanned downtime at a minimum. The appliance and app quickly help improve reaction time for maintenance teams and assist decision makers with health and diagnostics analytics for industrial devices and systems.
“These offerings were built with ease-of-use as a primary goal,” said Michael Pantaleano, global business manager, Rockwell Automation. “We’ve worked closely with our customers on these solutions, proving that an instantly available app and a scalable analytical appliance that work out-of-the-box can deliver immediate value. At Rockwell Automation, we are committed to building tools that are approachable for our customers’ current teams. These two new offerings help maintenance teams easily discover the health of their devices and better collaborate in context with insightful information.”
These offerings are some of the first subscription offerings from Rockwell Automation. To further streamline the adoption process, a new e-commerce portal is used to manage the new offerings. All subscriptions and management can take place within a single, self-service portal.
FactoryTalk TeamONE App
Focused on reducing mean time to repair, Rockwell Automation is releasing a new edition, dubbed the Standard Edition, of the FactoryTalk TeamONE app. The new edition adds an alarm module, enabling teams to collaborate with live alarm details. This gives users the ability to easily view all active alarms. They can also view, share and post new details, delivering better team collaboration by adding context with alarm information. Alarms requiring immediate action can be shared with specific team members or posted to the entire team for group management and resolution.
As a smart node, the FactoryTalk TeamONE app requires no server, device to cloud gateways, or IT setup for manufacturers to realize value, which expands as they go through their digital transformation. The app is currently available on the Apple App Store and Google Play store, and a new user account takes just minutes to set up.
The new Standard Edition is a paid yearly subscription and expands the features available in the free edition of the app. Released in 2016, the FactoryTalk TeamONE Free Edition app provides near-instantaneous incident and device data to plant-floor maintenance teams that include engineering, trades and IT workers. The free edition removes the barriers for industrial teams to collaborate and quickly solve issues with contextualized plant-floor data like trends and device status. Modules in this free edition include Incident, Device Status, Teamboard, Knowledgebase, Pinboard, Chat and Trend modules. Within the FactoryTalk TeamONE app, customers can even have a mix of free and subscription users on their teams.
FactoryTalk Analytics for Devices Appliance
FactoryTalk Analytics for Devices is a hardened appliance that helps avoid costly downtime and improve productivity by proactively identifying device health. With just a connection to power and a local control system network, the appliance begins providing analytics within minutes.
After the connections are made, the application detects automation devices on the network without disrupting performance. Plant-floor teams then gain access to specific calls-to-action, instant device displays and an advanced machine-learning-based chat bot, which are all available from within the appliance. The appliance learns what is important to users by continuously analyzing the devices on the network and delivering recommendations to help maintenance and engineering teams prevent unplanned downtime and repair systems more quickly.
“Our customers are trying to figure out how to take their first steps toward analytics and the industrial Internet of Things,” said Pantaleano. “This appliance is an excellent start to their journey, with tangible results that can help our customers within minutes.”
The FactoryTalk Analytics for Devices appliance can detect and perform a basic analysis on any EtherNet/IP device. The appliance also has detailed analytics for over 2,000 Allen-Bradley devices. The first year’s subscription is included for each appliance. Subsequent subscriptions are encouraged for customers to progressively receive updated analytics, features and device support, including third-party devices.
Predictive Maintenance
I attended a session on predictive maintenance. The topic was a support service from Rockwell Automation–some of which is ready to go now and some still under development. It combines software tools and human experts.
Research has revealed that about 74% of total downtime is figuring out what the problem is and developing a workflow to fix it. The goal of this service by Rockwell Automation is to shorten this time through application of predictive analytics combined with expert analysis.
They have set up a system to collect device data into a Microsoft Azure cloud–aka FactoryTalk Cloud–where analytics apps reside and then port the information into the services organization.
The team has been developing a series of software agents to look at a variety of situations from pattern recognition to anomaly detection to machine learning to help customers get their predictive program up and running “in weeks, not years.”
