by Gary Mintchell | Mar 30, 2021 | Data Management, Manufacturing IT, Operations Management
Here’s some news from a new company using SaaS led by people I’ve known from other places in my past. They have an interesting take on manufacturing information systems.
ThinkIQ announced a suite of new solutions under its SaaS manufacturing platform, which features four new areas of data functionality, including ThinkIQ’s Visualize, Insight, Transform and Enterprise solutions.
This expanded functionality enables manufacturers to make sense of the data surfacing actions that enhance safety, reliability, and efficiency by leveraging a fact-based granular and a data-centric view of material flows and related provenance attribute data.
New platform components integrate with existing IoT infrastructure to help manage everything from supply chains to manufacturing processes and beyond. These added capabilities will continue to build upon ThinkIQ’s unprecedented material traceability and insight which helps manufacturers improve yield, quality, safety, compliance and brand confidence while reducing waste and environmental impact.
“The addition of the newest solutions within our platform will help manage the manufacturing process from supply chain to customer,” said Niels Andersen, CTO and CPO of ThinkIQ. “Having this truly transformative intelligence and insight into your supply chain helps organizations make smarter decisions about their processes which in turn makes them become more profitable and more competitive.”
The latest solutions offered on the ThinkIQ platform include:
- ThinkIQ Visualize – This functionality moves companies past raw data to being able to explore, compare, and be aware of the data – with standardized metrics and views to bring wide visibility and context to data. ThinkIQ Visualize takes the existing data stream and brings on-premise gateways and connectors to centralize the data. Organizations will be able to see a view of all your data on one screen, and at multiple locations.
- ThinkIQ Insight – This new feature uses advanced analytics to enable a material-centric view of operations. Deliverables include advanced visualizations, initial cause & effect identification, industry benchmarking, and cross-plant KPIs. Alerts and notifications bring problems to immediate attention, mitigation of recall risks, and potential yield improvements. It will give a material-centric view of operations, with insights unable to be seen earlier.
- ThinkIQ Transform – This feature utilizes the results of the earlier steps to supply transformational intelligence and uncover root causes and effects. Data is correlated to the most important metrics. From process engineer, through plant manager to CEO, they have an instant, intelligent view of operations – one which stretches from the beginning of the supply chain through the plant and beyond.
- ThinkIQ Enterprise – This functionality of the platform offers the ongoing benefits of Industry 4.0 manufacturing. Manufacturing process will now include traceability from raw materials to product delivery, as well as optimized supply chains and real-time contextualized data.
ThinkIQ’s SaaS Manufacturing cloud-based platform simplifies the creation of web-based applications and leverages the strengths of the Internet of Things, Big Data, Data Science, Semantic Modeling and Machine Learning. The platform is able to collect data inputs across supply chain (existing and IIoT sensors) and analyze with AI, ML to identify correlations and root causes. It creates a new set of value-added traceability data which is delivered with actionable insights and decisions to guide systems across the supply chain.
About ThinkIQ
ThinkIQ, a pioneer of Digital Manufacturing Transformation SaaS, delivers unprecedented material traceability and insight into ways to improve yield, quality, safety, compliance and brand confidence. Our transformational intelligence platform delivers fact-based granular and data-centric contextualized view of material flows and related provenance attribute data that integrates into existing IoT infrastructures and crosses supply chains to Smart Manufacturing processes and beyond. Our customers have saved $10’s of millions by identifying waste and underperforming assets, as well as reducing warranty reserves for quality and safety issues. ThinkiQ is a privately held company headquartered in Aliso Viejo, CA.
by Gary Mintchell | Mar 29, 2021 | Personal Development
This is my 2,500th post. I began this blog in 2003 as an experiment with a new form using one of the original blogging platforms, developed by Dave Winer, called Radio Userland. The blog was Gary Mintchell’s Radio Weblog. The sales people at Automation World wanted to capitalize somehow, and I changed the name to reflect my monthly column called FeedForward. When the market started to change and the magazine, too, I went digital only and changed the name to the present The Manufacturing Connection.
Topics have changed over time. I’m still interested in technology and people development. But many things have changed. There’s not so much control and automation anymore. Most of the tech news I come up with is software and Internet of Things. In other words, connections and data handling. Back then, leadership and productivity (Getting Things Done) were important topics. Writing about leadership now is everywhere with not much useful being added. GTD is still important, but writing about it became redundant.
I thought I’d pass along a thought I found some time ago showing the difference between successful people and unsuccessful people.
- Successful people:
- Read every day
- Compliment
- Embrace Change
- Forgive others
- Talk about ideas
- Continuously learn
- Accept responsibility for their failures
- Have a sense of gratitude
- Set goals and develop life plans
- Unsuccessful people:
- Watch TV every day
- Criticize
- Fear change
- Hold a grudge
- Talk about people
- Think they know it all
- Blame others for their failures
- Have a sense of entitlement
- Never set goals
Strive for the best and go make a difference.
by Gary Mintchell | Mar 26, 2021 | Internet of Things, Manufacturing IT, Operations Management
Another example of industrial technology companies working with Microsoft Azure. The Cloud race is heated.
IOTech, the edge software company, announced the launch and availability of Edge XRT, its time-critical edge platform for Microsoft Azure Sphere. Designed and optimized for resource-constrained environments, Edge XRT delivers out-of-the-box device connectivity and edge intelligence for microcontroller units (MCUs), gateways and smart sensors at the IoT edge. It reduces time-to-value from weeks to hours.
Azure Sphere is a secured, high-level application platform with built-in communication and security features for the connected devices. It comprises a connected crossover microcontroller unit, a custom Linux-based operating system, and a cloud-based security service.
“We’re delighted to collaborate with Microsoft and its partners to deliver real-time IoT edge capability for low-profile, yet powerful, devices,” said Keith Steele, CEO of IOTech. “The availability of Edge XRT for use with Microsoft Azure Sphere is an important step to accelerate the deployment, and even more importantly, dramatically reduce the time-to-value, for both greenfield and brownfield IoT edge solutions.”
Edge XRT for Azure is fully compatible with Azure Sphere-certified chips and Azure Sphere OS. An Azure Sphere device is designed to integrate securely with the Azure Sphere security service running in the cloud. The security service ensures the integrity of the device and provides the secure channel used by Microsoft to automatically install Azure Sphere OS and customer application updates to deployed devices.
Edge XRT reduces the time-to-value for Azure Sphere, delivering securely connected device service deployments from weeks to hours. In addition, by moving specific workloads to the edge of the network, devices spend less time communicating with the cloud. The result is devices react more quickly to local changes and operate reliably, even in extended offline periods.
Edge XRT simplifies connectivity to sensors and devices at the edge by configuration versus coding. This enables connectivity to Azure Sphere devices using a range of standard industrial protocols such as Modbus, BACnet, EtherNet/IP and others. It allows ready access to edge data, which can be sent securely to and from its digital twin running on Azure IoT Hub.
Edge XRT can also host edge intelligence applications for Azure Sphere devices. It allows users to create edge applications that can be downloaded and updated securely over the air via the Azure Sphere security service throughout the life cycle of the device.
“Microsoft is pleased to collaborate with IOTech to enable the integration of device data with Microsoft Azure Sphere deployments,” said Galen Hunt, Distinguished Engineer and Managing Director of Azure Sphere, Microsoft. “Edge XRT software helps reduce device integration configuration time and deployment, helping customers and partners realize value from IoT solutions rapidly and at scale.”
by Gary Mintchell | Mar 26, 2021 | Automation, Technology
The new A3 organization (motion/vision/robotic associations) held its annual show virtually over five days this week. I was busy, but I did tune in for some keynotes and panel discussions. I also browsed the trade show.
The platforms are getting better all the time. I was blown away by all the cool things today’s keynoter was able to pull off. But they still can’t quite get the trade show experience up to expectations.
Today’s keynote was given by Andrew Ng, CEO of Landing AI, a machine vision AI company. His talk was a low-key, effective explanation of AI and how to implement a successful AI-enabled vision inspection project. I’d almost call this “beyond hype”.
Here are a few key points:
75% of AI projects never go live.
Vision inspection has gone from rules-based to deep learning (aka, AI, ML), learn automatically.
Ng polled his audience about experiences with AI projects with the key responses:
- Lack of data
- Unreal expectations
- Use case not well defined
- Hype—perception of AI as futuristic
Challenges
- Not sufficiently accurate
- Insufficient data
- More than just initial ML code needed
- System able to learn continuously
AI Systems = Model + Data
Improving the system depends upon improving either Model or Data; experience in manufacturing shows best results come from improving data.
One Landing AI partner estimated 80% of his work was on preparing data (data processing) and only 20% on training a model.
AI Project Lifecycle
Scope Collect Data Train Model Deploy in Production
Train Model feedback to Collect Data
Deploy feedback to train model and also feedback to collect data
Common problem—is data labeled consistently? E.g. are defects consistently defined?
Common data issues: inconsistent label; definition between two defects ambiguous; too few examples
Final advice:
- Start quickly
- Focus on data
- End-to-end platform support (lifecycle)
Coincidentally, Ng was Interviewed at MIT Technology Review and I received an email notice today. I’ve included a link, but you may need a subscription to get in.
Karen Hao for MIT Technology Review: I’m sure people frequently ask you, “How do I build an AI-first business?” What do you usually say to that?
Andrew Ng: I usually say, “Don’t do that.” If I go to a team and say, “Hey, everyone, please be AI-first,” that tends to focus the team on technology, which might be great for a research lab. But in terms of how I execute the business, I tend to be customer-led or mission-led, almost never technology-led.
A very frequent mistake I see CEOs and CIOs make: they say to me something like “Hey, Andrew, we don’t have that much data—my data’s a mess. So give me two years to build a great IT infrastructure. Then we’ll have all this great data on which to build AI.” I always say, “That’s a mistake. Don’t do that.” First, I don’t think any company on the planet today—maybe not even the tech giants—thinks their data is completely clean and perfect. It’s a journey. Spending two or three years to build a beautiful data infrastructure means that you’re lacking feedback from the AI team to help prioritize what IT infrastructure to build.
For example, if you have a lot of users, should you prioritize asking them questions in a survey to get a little bit more data? Or in a factory, should you prioritize upgrading the sensor from something that records the vibrations 10 times a second to maybe 100 times a second? It is often starting to do an AI project with the data you already have that enables an AI team to give you the feedback to help prioritize what additional data to collect.
In industries where we just don’t have the scale of consumer software internet, I feel like we need to shift in mindset from big data to good data. If you have a million images, go ahead, use it—that’s great. But there are lots of problems that can use much smaller data sets that are cleanly labeled and carefully curated.
by Gary Mintchell | Mar 25, 2021 | Internet of Things, Manufacturing IT, Operations Management
Here are announcements from the Industrial Internet Consortium (IIC) regarding two white papers released. One deals with IioT Models and the other with innovation processes of digital transformation. A lot of thinking went into these.
The Industrial Internet Consortium (IIC) announced the publication of the Characteristics of IIoT Models White Paper. Interoperability between applications, subsystems, and devices in Industrial Internet of Things (IIoT) systems requires agreement on the context and meaning of the data being exchanged, or semantic interoperability, which is typically captured in an information model. The new white paper addresses the challenge of integrating subsystems in IIoT systems that use different information models and examines how standardized information models that use a descriptive or semantic approach enable interoperability and ultimately digital transformation.
The variety of digital data and information systems is an indispensable attribute of the modern world of IIoT. In each industrial vertical, one way or another, work is underway to reach agreements between stakeholders through the development of standards and data schemas. Our white paper provides a simple definition of the characteristics and properties of information models, which can be useful in the design of IIoT systems and, which is especially important, for multiple systems to work seamlessly with each other.
“Semantically based information models can share data across domain boundaries using a descriptive approach (instead of a translational approach) as the data has meaning in both domains, and the full fidelity of the original data are maintained,” said Kym Watson, Co-chair of the IIC Distributed Data Interoperability and Management Task Group, an author of the white paper and Scientist at Fraunhofer IOSB. “Our intent in this white paper is to survey a subset of information models that are relevant to the IIoT and characterize those information models using a meta-model developed for this purpose. With this we capture commonalities and can begin to address the challenge of integrating subsystems that use different information models.”
“An information model is a representation of concepts, relationships, constraints, rules, and operations to specify data structures and semantics,” said Niklas Widell, Co-chair of the IIC Distributed Data Interoperability and Management Task Group, an author of the white paper and a Standardization Manager at Ericsson. “There are multitudes of information models available or under active development for a variety of application domains or industries. We focused on information models above the Industrial Internet of Things Connectivity Framework layer where semantic interoperability, including translation between different models, plays a key role.”
The white paper examines the following standardized information models (among others) that are widely applied in IIoT applications:
• Web of Things – a set of standards by the W3C for solving the interoperability issues of different IoT platforms and application domains.
• SensorThings API – an Open Geospatial Consortium standard providing an open and unified framework to interconnect IoT sensing devices, data, and applications over the Web.
• OPC UA – a machine-to-machine communication protocol for industrial automation developed by the OPC Foundation focusing on communicating with industrial equipment and systems for data collection and control.
• Asset Administration Shell – a key concept of Industry 4.0 used to describe an asset electronically in a standardized manner. Its purpose is to exchange asset-related data among industrial assets and between assets and production orchestration systems or engineering tools.
• IPSO Smart Objects – a lightweight design pattern and object model to enable data interoperability between IoT devices, building on the LwM2M IoT device management standard, specified by OMA SpecWorks
• One Data Model/Semantic Definition Format – an initiative to improve interworking across different ecosystems’ data models using an emerging standard from the IETF. The OneDM Liaison Group adopts and aligns IoT models contributed by participating organizations, so best practice models for desired features or purposes can be identified.
“Standardized information models with defined semantics and APIs are an essential foundation for any form of digital transformation,” said Andrei Kolesnikov, Co-chair of the IIC Distributed Data Interoperability and Management Task Group, an author of the white paper, and director of the Internet of Things Association IOTAS. “There must be a seamless integration across the system life cycles, especially engineering and operations for all data sharing technologies.”
IIC members who wrote the Characteristics of IIoT Models White Paper and a list of members who contributed to it can be found here on the IIC website.
IIC White Paper Identifies Innovation Process For Digital Transformation
BizOps for digital transformation in industry facilitates IT and OT integration with better business outcomes
Industrial Internet Consortium (IIC) today announced the publication of the BizOps for Digital Transformation in Industries white paper. The new white paper identifies the BizOps for Digital Transformation in Industry (BDXI) innovation process, offering examples of a BDXI framework as crucial for IIoT solutions operators undergoing digital transformation.
“Digital transformation is a huge topic influencing almost every department of a firm,” said Co-author of the white paper Kai Hackbarth, Business Owner Industrial at Bosch.IO. “Solutions operators must integrate IT and OT to achieve better business outcomes, especially in asset-driven industries such as agriculture, energy, health care, manufacturing, retail, smart cities, and transportation. This is not an easy task as the process is slow and likely to conflict with existing processes and management systems.”
“The BDXI process is a fast, open, and customer-centric innovation process that considers the constraints and complexity of IT/OT integration and the physical world,” said Co-author of the white paper Chaisung Lim, Group Chair of the IIC BizOps for Digital Transformation in Industry Contributing Group, Chairman of the Korea Industry 4.0 Association, and a professor of Konkuk University. “A BDXI process helps IIoT solutions operators manage the innovation process from idea to launch successfully.”
A BDXI process includes discovering customer needs, developing solutions, learning whether solutions are feasible, and putting them into action. This necessitates dialogue between IT and OT stakeholders who would otherwise be constrained by organizational silos, a customer-centric process of checking solution validity, and fast experimentation with minimum viable products and agile methods. The most common features of BDXI processes includes the adoption of the best innovation practices from design thinking, lean start up and agile methods, and BizDevOps (the integration of IT/OT). BDXI process must be supported by a BDXI framework that offers a guide for implementing BDXI process concretely.
The BizOps for Digital Transformation in Industry white paper delves into the most common features of BDXI processes, examples of BDXI processes and frameworks, conflicts between BDXI processes and management systems, and IIC initiatives to help guide BDXI processes. IIC authors and contributors to the BizOps for Digital Transformation in Industry white paper can be found here on the IIC website.
