HighByte Releases Industrial DataOps Solution with Native Connectivity to the Snowflake Data Cloud

A couple of IT companies introduced DataOps to me about ten years ago. I thought this looked like a ripe opportunity for the industrial market. Shortly thereafter I ran into a group of former Kepware people who had formed just such a company—HighByte. I then had an opportunity to talk with the Snowflake people at the Ignition Customer Community meeting last September. This Data Cloud company has some interesting technology. This news relates to a relationship and interoperability service.

If you have not explored the utility of DataOps, check out HighByte and also Snowflake.

HighByte announced in February 2024 the release of HighByte Intelligence Hub version 3.3 that offers new and improved interoperability with industry-leading cloud services, including the Snowflake Data Cloud and AWS IoT SiteWise. The latest release introduces two new native connectors for Snowflake supporting a broad set of use cases for industrial enterprises. 

The first new connector, Snowflake Streaming, utilizes the Snowflake Snowpipe Streaming API. This interface enables direct publishing to Snowflake tables without the need for staging files or third-party applications. This significantly reduces the compute, latency, and cost of frequently moving telemetry events into Snowflake. The second new connector, Snowflake SQL, enables HighByte Intelligence Hub users to directly query Snowflake tables. Rather than merely publishing to Snowflake, the Intelligence Hub can operationalize insights and context derived through the Snowflake Manufacturing Data Cloud by making this data available for industrial devices and applications. 

HighByte Intelligence Hub is an Industrial DataOps solution that contextualizes and standardizes raw industrial data at the edge, delivering usable information to cloud service partners. Receiving consistent, usable industrial data accelerates adoption and scale of these cloud services, helping industrial companies orchestrate digital transformation projects across their enterprise. The Intelligence Hub gives operational technology (OT) domain experts a no-code application to curate and contextualize industrial data according to standard data models.

Intelligence Hub version 3.3 also introduces tighter integration with AWS IoT SiteWise. The Intelligence Hub’s modeling engine and the IoT SiteWise connector have been refined and enhanced for working with hierarchical asset structures. These improvements simplify the user experience, reduce effort, and provide a single, no-code approach for composing and delivering asset-model hierarchies to IoT SiteWise as well as hydrating them with industrial data. 

Mitsubishi Corporation Invests in ThinkIQ to Drive Digital Transformation

I have been wondering where ThinkIQ is going to wind up. It’s a pretty cool startup in the smart manufacturing software space (aka, MES). The company has taken an investment by Mitsubishi Corporation and a collaboration agreement to jointly accelerate the growth of ThinkIQ’s digital manufacturing platform in Japan. Terms of the investment were not disclosed.

ThinkIQ has built its open platform working closely with U.S. and European government smart manufacturing and Industry 4.0 initiatives and global standards bodies.  The investment is further testament to ThinkIQ’s technology and will drive expansion leveraging Mitsubishi’s global presence.

ThinkIQ provides visibility to the manufacturing shop floor across each tier of complex supply chains. The SaaS platform securely connects to the physical world of legacy and smart equipment, IoT sensors, OT and IT systems to bring all relevant data into a single analytics platform that brings context, meaning and discoverability for all participants in supply chain and manufacturing operations. ThinkIQ Vision brings vision-processing software combined with powerful pre-packaged Machine Learning and Artificial Intelligence capabilities to turn standard cameras on the shop floor into sensors that eliminate blind spots across equipment, materials, and people to greatly enhance the available data for Continuous Intelligence.

Lack of Roadmap Biggest Hurdle for Manufacturers Looking for Digital Transformation

Once upon a time surveys were the purview of analyst firms and media. None were mathematically rigorous. Most do show trends and yield ideas for thought.

Digital transformation is top of mind for companies who develop and market software solutions but maybe not so much for customers. This survey is from iBase-t. I knew them as an MES supplier, but now the are the company “that simplifies how complex products are built and maintained.” In other words, MES. That’s OK. My background in that application goes back decades.

This original survey of more than 100 discrete manufacturing executives in the U.S. found that a lack of a clearly defined roadmap is the biggest challenge for manufacturers looking to digitally transform their operations.

None of this surprises me. Many studies have found similar statistics. Upper management in manufacturing organizations “know” these problems. They don’t seem to know how to go about implementing solutions. Or, they don’t want to spend the money!

In brief, their study revealed:

  • 60% of manufacturers don’t have a clear understanding of the model-based enterprise
  • 67% of manufacturers say that less than half their operations are digital

A full 60% of respondents said they did not have a clear understanding of the model-based enterprise (MBE), which employs CAD systems, Product Lifecycle Management (PLM) systems and Manufacturing Execution Systems (MES) to help manufacturers fully digitize their operations.

Respondents confirmed that although paperless manufacturing and digital transformation are very important priorities, more than two-thirds (67%) of manufacturers reported that less than half of their operations are digital.

The survey found that more than half (54.5%) of respondents lack the interoperability across operations to adopt an MBE strategy. An additional 55% said that their manufacturing systems are not mature enough to support MBE.

Other Key findings:

  • According to the survey, 62% of total respondents said that they believe paperless manufacturing is “very important” to their organization.
  • The top four goals for manufacturers heading into 2024 are efficiency (66%), on-time delivery (66%), done-right first time (49%) and profitability (47%). An MBE strategy empowers manufacturers to reach all of these goals.

SCADA Survey Yields Interesting Results

I found this an interesting survey from Control Engineering (CFE Media). It ran a survey of its readers about SCADA. They received 135 responses from the USA. Some of the results were surprising. It should be noted that this survey is not statistically valid. It’s the opinions of those who cared to respond. Most were from the East Coast. (Thank you graduate school course on running statistically valid surveys which are definitely not run by journalists.)

Responding to “SCADA helps you to…” most responses were operational—maintenance and uptime. Not so many responded Industry 4.0 or IIoT.

Whose SCADA software do you use?

  • Siemens
  • Emerson and GE Digital (tied)
  • Inductive Automation
  • AVEVA (Wonderware?)
  • Advantech (hardware?)
  • Mitsubishi (Iconics)
  • 45 of the 135 picked Other.

Rockwell Automation didn’t get enough responses to get its own line on the graph.

Who would you prefer?

When asked who would you like to use Inductive Automation grew to second. Everyone else slipped. GE Digital slipping the most.

  • Siemens
  • Inductive Automation
  • Emerson
  • GE Digital
  • AVEVA
  • Advantech
  • Mitsubishi (Iconics)

I am not surprised at the growing preference for Inductive Automation. They have a solid product and the pricing model is outstanding. But given that Siemens has never been able to provide much competition for Rockwell Automation in the USA for control, how is it that its SCADA product leads the pack? Interesting.

Manufacturers Must Close the Digital Transformation Gap

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The headline of this article comes from a survey conducted by MES solution provider Parsec Automation Corp. The company spent several months surveying 530 manufacturing professionals in the United States and Canada. Every security company I know does surveys. These are becoming quite a popular marketing tool.

I’m interested partly because I wonder how much talk about digital transformation is simply marketing hype and how much refers to real work. I’ve concluded that although manufacturers in general have digitized many sources of data they mostly fall short of gathering sufficient data from important sources and using that data intelligently in order to make better decisions.

This survey suggests as much.

Survey Highlights:

  • Manufacturers are pleased with digital transformation but may need to go further, as 31% of “fully transformed” companies still collect most of their data using manual processes.
  • Supply chain issues remain top of mind, with 53% of manufacturers saying their organizations are “hardly” or “somewhat” prepared to weather a storm.
  • Manufacturers anticipate using AI/ML, but just one-third (34%) feel their businesses are prepared to leverage this advanced technology.
  • MES (manufacturing execution system) technology is facing a knowledge gap, with 75% of manufacturers who report being familiar with—but not yet using MES—saying they don’t know how the technology would benefit their operations.

One telling thing is the lack of knowledge about the benefits of MES. MESA, the trade association, has been working alongside both solution providers and active users for many years to promote the benefits of the technology. Trainers told me 15 years ago that the greatest interest came from manufacturers in Asia followed by Europe. They had trouble filling classes in North America. This survey of manufacturers in North America seems to confirm what my current sources tell me—we are still falling behind over here.

Overall, nearly three-quarters (73%) of manufacturers have begun the digital transformation process, with 40% reporting significant progress or completion. However, more than one-third (35%) still report relying on paper-based data collection, which suggests a significant gap among manufacturers’ willingness and preparedness to embrace today’s technology.

“Although manufacturers are steadily advancing towards digital transformation, there remains a significant scope for progress,” commented Eddy Azad, Founder and CEO of Parsec. “It’s heartening to note that over half (53%) of the survey participants are utilizing enabling technologies like MES. This insight underscores the need for technology providers to not only effectively showcase the benefits of their solutions, but also to furnish the requisite tools and education for the seamless and sustainable adoption of these transformative technologies.”

In perhaps the understatement of the report, Parsec’s survey findings suggest that manufacturers may be underutilizing technology or underestimating its capabilities. Among the respondents whose companies have reportedly “completed” their digital transformations, nearly one-third (31%) still collect most or all of their data using non-digital processes.

When it comes to MES platforms, which leverage IIoT technologies to automate and optimize nearly every facet of manufacturing operations—from receipt of raw materials, through production, to shipping, warehousing, and distribution—more than one-quarter (27%) of respondents said they had never heard of MES before.

All is not lost, though:

Those who have adopted MES, however, are pleased with their results. An impressive three-quarters (75%) of respondents who are actively utilizing an MES platform said they were “very” or “extremely satisfied” with the product. These active users said they adopted the advanced technology to increase efficiency (73%), improve quality (57%), and reduce operating costs (47%).

I’ve become increasing interested in the power of various AI technologies. Here the report agrees.

Across the industry, manufacturers are keenly aware of the trajectory of today’s technology, with more than half (52%) agreeing that enterprise software solutions should include capabilities for AI and ML.

At the same time, just one-third (34%) feel their business is prepared to leverage this advanced technology. When asked about the barriers standing in their way, respondents cited lack of knowledge (46%), lack of trust in the technology (39%) and implementation costs (33%).

“Manufacturers need to adopt advanced technology to propel the industry forward,” Azad elaborated. “Contemporary software solutions must be developed with enhanced accessibility and exceptional user experience in mind. It is imperative for technology providers to proactively engage with manufacturers, address their apprehensions, and offer guidance to fuel their success.”

Parsec is the developer of TrakSYS, a proven operations management software application and solution platform designed to significantly improve manufacturing processes. Parsec is committed to providing best-in-class products and solutions to our worldwide community of clients to assist them in optimizing their manufacturing operations. There are thousands of TrakSYS licenses in use around the globe in a wide variety of Industries.

Integrated Policy Engine for MQTT Data

MQTT seems to be still growing as a favorite light-weight data transport for industrial data. HiveMQ, whom I had a chance to chat with briefly at the Inductive Automation ICC event in September, has released a new MQTT product.

HiveMQ Data Hub, an integrated policy engine within the HiveMQ broker designed to enforce data integrity and quality, helps to detect and manage distributed data and misbehaving MQTT clients with the ability to validate, standardize, and manipulate data in motion.

HiveMQ Data Hub is available now and provides the following capabilities:

  • Create a schema policy in JSON or Protobuf formats
  • Define policy actions for data that fails validation
  • Store schema registries locally for faster access and data processing in a single system
  • Define behavioral policies to determine how devices work with the broker and log bad actors
  • Visualize the data in tools like Grafana with an API

HiveMQ Data Hub’s policy engine allows users to script policies and transform data into the right format as it moves through the broker. Creating and defining schema policies for validation and transformation enables users to add context and quality control to data to ensure consistency for reporting and analytics.

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