by Gary Mintchell | Jun 4, 2026 | Manufacturing IT, Software
Here is a new service—operational decision intelligence. Also a company new to me—SteelTree. They define an operational decision intelligence service as something designed to help industrial teams improve awareness, reduce friction, and coordinate action across fast-moving operations.
The information I could gather combined was very sparse. Not sure why it’s better or worth than anything out there already (unless the “.ai” means something new in AI. It could be worth checking just in case.
The company seeks to help teams move beyond disconnected dashboards, spreadsheets, reports, and silos to improve visibility, coordination, and execution across day-to-day operations. SteelTree enables teams to quickly identify changes, recurring issues, performance drift, coordination gaps, and priorities requiring attention.
The model consists of:
See → Decide → Execute → Learn
SteelTree helps industrial teams:
- See what’s happening
- Decide what matters most
- Coordinate and execute actions faster
- Continuously learn before small issues become larger problems
“Most teams are not lacking systems or data,” said Kanwar Arora, Founder of SteelTree. “What they’re lacking is continuous operational awareness across fast-moving environments. Teams still spend too much time moving between dashboards, spreadsheets, reports, and silos just to understand what requires attention. SteelTree reduces the friction between operational signals, decisions, and action.”
Unlike traditional BI, dashboarding, and reporting tools that often depend on analysts, dashboard development, and delayed reporting cycles, SteelTree is focused on helping teams maintain awareness and coordination without adding overhead.
The company believes many organizations still struggle with operational visibility and coordination despite significant investments in business systems and reporting tools.
“As the software industry races to embed AI across enterprise applications, many teams still struggle with a more fundamental challenge: maintaining awareness across fast-moving operations and coordinating action effectively,” said Peter Price, Founder of SteelTree. “SteelTree starts by helping teams see clearly, but visibility alone is not enough. The real value comes from helping teams decide faster, execute more effectively, and continuously learn across day-to-day operations.”
SteelTree’s launch is focused on industrial teams looking to improve operational awareness, decision-making, and coordination without the complexity typically associated with traditional enterprise analytics and reporting tools.
The service is available immediately with free access.
by Gary Mintchell | Jun 2, 2026 | Robots, Software, Technology
Manufacturers have lots of data. Every day brings new technologies for gathering and storing it. The right question probes into what specific problem can be solved. I sat in the world’s shortest press conference (not complaining, even though they blew off my question) with a company I don’t know who asked the question—how can we better integrate the myriad details required to build the best robotic workcell.
The company is called Robotiq. Based in Quebec City, Canada, introduced IQ, an AI-enabled platform designed to make robotic Workcell integration faster, more predictable, and easier to scale. IQ captures unstructured automation project data, coordinates engineering workflows, and helps partners generate validated Workcell designs based on real customer inputs and historical deployment data from thousands of previous factory installations.
“AI” can be a generic marketing buzzword. I asked for a definition, but the press conference closed before they got to it. Reading through the press release, the definition apparently involves machine learning algorithms. Fair enough.
“Automation does not scale when integration remains manual,” said Samuel Bouchard, CEO of Robotiq. “With IQ, we are moving from manually engineering robotic systems one project at a time to automatically generating Workcells from real customer inputs, Robotiq components, AI, and proven know-how from thousands of past projects. For manufacturers, this means a clearer path to automation: fewer surprises, faster decisions, more predictable performance, and better financial justification, including in many 1-shift operations.”
Robotic Workcell integration depends on thousands of small details. Customer requirements, production constraints, factory floor layouts, site measurements, throughput targets, product variants, and local installation realities all affect whether a project succeeds. When that data is incomplete, fragmented or siloed, engineering teams experience project delays during the discovery and design revision phases.
The IQ solution includes:
- Automated data capture: Extract technical requirements via voice notes, legacy file uploads, and 3D site scanning.
- AI-enabled project coordination: Machine-learning models align manufacturer specifications, partner capabilities, and Robotiq application engineering expertise.
- Simulation and design validation: 3D environment scans are converted into digital twin models, matching customer cycle times and application data against standardized engineering rules to validate Workcell performance before physical deployment.
IQ is available today for robotic palletizing applications, where Robotiq has already standardized the hardware components, software workflows, and deployment knowledge needed to generate validated Workcell designs. Over time, Robotiq plans to extend the same Automatic Integration model to additional robotic applications.
To commission robotic systems successfully, manufacturers need local system integration support, application expertise, and reliable service. Robotiq partners play that role. IQ provides partners with a repeatable digital workflow to capture project information, apply Robotiq deployment expertise, collaborate with customers and Robotiq experts, and support Workcells more consistently after installation.
“IQ does not replace partner expertise,” Bouchard added. “It amplifies this expertise to accelerate and scale projects. Manufacturers need local partners who understand their production reality and can provide the installation capacity and support needed to keep lines running. IQ gives those partners better information, better coordination, and a clearer path from opportunity to running system.”
by Gary Mintchell | May 27, 2026 | Generative AI
I’ve spent too many hours on AI. I notice statistics indicating that more people are interested in data interoperability than in AI. But this is timely. I thought I’d share this item from John Ellis News Items. It’s one of my favorite sources for a quick update on the news.
You may or may not realize that much of the media shouts from Silicon Valley are really religious in nature. Many out there subscribe to a “scientism.” Shunning traditional religion, they espouse radical rationalism fashioning a religion from reason and science.
First, no less a person than the Pope takes on the Silicone Valley religion head on.
When Pope Leo XIV presented a 42,300-word open letter to the world’s 1.4 billion Catholics on Monday, calling for protections against the rise of artificial intelligence, he was joined by Christopher Olah, a co-founder of Anthropic, which is one of the tech industry’s leading A.I. companies.
As Leo urged corporate executives, government regulators and other citizens of the world to safeguard humanity from the dangers of A.I., he included Mr. Olah as a symbol of the dialogue he hopes to foster between the leaders of the spiritual and technological worlds.
Was this really a discussion?
But for Jeremy Nixon, Monday’s gathering at the Vatican showed that those two worlds are far from aligned. While the pope said that A.I. was fundamentally not human, Mr. Nixon, a well-connected figure in the Bay Area’s frenetic A.I. scene, argued that Mr. Olah’s remarks seemed to hint at the opposite.
“They are not in dialogue,” Mr. Nixon said during an interview at A.G.I. House, a San Francisco “hacker house” with deep ties to many of the people who helped create the A.I. technologies discussed in the pope’s encyclical. “Their perspectives are distinct.”
Ah, the news reports drag in another philosophy—humanism.
The difference between the humanist’s view of A.I.’s risks and the technologist’s dream of what it could become is something that has long been discussed in Mr. Nixon’s community. “It is the reason the community exists,” Mr. Nixon said. “It is its underlying purpose.”
I listen to a podcast called Robot or Not, where podcaster and engineer John Siracusa answers listeners’ questions on definitions. In this case, looks like the question is “AI, Human or Not.”
Mr. Nixon, 33, is one of the founders of A.G.I. House, which is named for Silicon Valley’s headlong pursuit of “artificial general intelligence,” a hypothetical machine that can do anything the human brain can do.
Mr. Nixon said the papal encyclical might mean something to the world’s Catholics, but he doubted that it would have an effect on Silicon Valley. The only reason that Silicon Valley even paid attention to the event, he said, was that Leo invited Mr. Olah to speak.
Mr. Nixon is now founder and chief executive of a start-up called the Infinity Artificial Intelligence Institute, which is trying to automate the creation of A.I.
Even more grandiose that Human or Not, is it God or Not?
Mr. Nixon said he has met a generation of scientists who shunned traditional religion in favor of technology. After growing up with books like “The God Delusion” — in which the evolutionary biologist Richard Dawkins painted God as a false belief contradicted by empirical evidence — he and his peers saw A.I. as an alternative that was more real and far more powerful.
A.I. has started to crack math problems that humans struggled with for decades, he said, and it will soon cure diseases in the same way. “Practically speaking, it will achieve the outcomes that many religions claim their deities would be able to achieve,” he said.
This is an increasingly common belief among researchers in Silicon Valley. They insist they are on their way to building a more powerful species — or even a new God.
As a contemplative, my view of God comes from practice and experience, rather than logical argument. If you are on the rational argument side of things, go for it!
by Gary Mintchell | May 15, 2026 | Data Management, Manufacturing IT, Operations Management
This is one of those press releases that have come to me for years that a typical magazine would just republish and call it news but leaves me wishing for much more information. Like who, what, how. The story concerns implementing a Manufacturing Execution System (MES) that enabled doubling of production through the consolidation of data.
I’d love to know more, but this comes from Rockwell Automation—a company who tempts me with cool information but then never follows through. So, if you’re a Rockwell customer checking out their MES solutions, perhaps your account manager can supply you with a deeper look into what appears to be a promising application.
News in brief: Century-old Pacific Northwest co-packer consolidates nine systems into one, achieves 99% inventory accuracy, and wins 2025 Plex Transformer Impact Award
Rockwell Automation announced that Portland Bottling Company (PBC), a leading U.S. West Coast beverage co‑packer, has been named a recipient of the 2025 Plex Transformer Impact Award. PBC earned recognition after doubling its monthly production volume and achieving measurable operational improvements following its deployment of the Plex Smart Manufacturing Platform.
Founded in 1924 and based in Clackamas, Oregon, PBC specializes in ready-to-drink beverages manufactured exclusively in aluminum cans. As customer demand increased, PBC recognized the need for greater operational visibility and control. Prior to implementing Plex in 2020, the company relied on nine disconnected technologies with no single source of truth. Manual inventory processes introduced data errors, limited production insight and reduced the company’s ability to respond quickly to customer needs.
To address these challenges, PBC worked with Plex partner Revolution Group to implement Plex Manufacturing Execution System (MES) — including Plex MES Automation & Orchestration (A&O) and Plex Quality Management System (QMS). By consolidating its technology landscape into one connected platform, PBC provided every department, from production and quality to inventory and maintenance, access to the same real-time operational data, eliminating silos that had previously constrained growth.
“Tracking our customers’ inventory accurately is paramount to our business. Plex enables us to do this efficiently and easily report on-hand balances and warehouse charges,” said Robert Van Blake, IT director, Portland Bottling Company. “Real-time data is a key factor in operational productivity improvements. Prior to Plex, this data was cumbersome to collect and subject to human error.”
The impact of transformation was both immediate and sustained. PBC increased production volumes from 900,000 to two million case equivalents per month. Inventory accuracy reached 99%, while shipping accuracy hit 100%. Productivity improved by 10% and waste dropped by 20% and maintenance response times decreased—enabling the company to scale operations while maintaining quality and customer service.
“Plex MES extends beyond execution to help manufacturers bring greater coordination and visibility across quality, inventory and production,” said Michael Hart, head of industry strategy and growth, Rockwell Automation. “Portland Bottling Company’s transformation brings this to life, showing how a connected operational foundation can drive both efficiency and scalable growth.”
by Gary Mintchell | May 14, 2026 | Generative AI, Manufacturing IT, Operations Management, Software
I devoted three days in April to attend the Aras Community Event (ACE 2026) in Miami, FL. Even though I am not a specialized market analyst in that market, I’ve been involved with the application of product lifecycle management ever since I was “The Kid in Engineering” at a manufacturing company back when, well, I was just a bit older than a “kid.”
Our company (another company that designed and built automated assembly equipment) transitioned to computer-aided design (CAD) while I was in management. Later, I became involved with AutoCAD.
So, there are memories of the great advances in the technology and capabilities.
My first summary of my three days with the Aras community in Miami was recorded on my podcast and YouTube channels. As I wrote at the time, “These PLM events always return me to the time when I did this sort of work–manually. Then my first taste of computers digitizing the bill of materials as a first step in our data management journey.”
Aras product managers showed how LLMs trained on the data within the app along with proper governance worked with agents to perform a number of tasks. Tasks in many cases that would require days of pain-staking work from a human.
While I heard from an analyst in the market that they thought this was all painfully slow, I’d offer the thought that a company does not want to outpace its customers. Most will not want to jump into the deep end immediately.
Chatting with CTO Rob McAveney, I heard how the company is taking a balanced approach to introducing these new technologies assuring that they are bringing their customer base along laying out the progression of “agentification of PLM.” The vision includes turning Aras Innovator into an “enterprise nervous system.”
The pressure of digitalization and the so-called digital transformation of companies drives these developers and suppliers into trying to find solutions to the immense data problems they face. Aras’ core technology lies in the digital thread, a topic often referred to.
Ironically, my discussions with Aras and some customers and prospects during the conference revealed an unhealthy fact that I’ve often heard in another software application market—MES. It seems that few users use the full complement of solutions offered by the vendors. This means that what could be a mature market is actually open for new solutions—meaning an innovative upstart like Aras has opportunity for market growth.
I researched the market using my favorite search engine—Claude.ai. The global PLM & Engineering software market reached $31.1 billion in 2024, growing 9.7% year-over-year, and is projected to hit $41.6 billion by 2029 at a ~6% CAGR. The top 10 vendors account for roughly 85% of the total market.
The leading suppliers include Siemens Digital Industries, Dassault Systèms, PTC, and Autodesk. Analysts report Aras Innovator is built for adaptability, offering a platform designed to evolve quickly with a low-code development environment and strong Digital Thread capabilities.
The four key development points for Aras agentic AI and LLMs, which were repeated often are:
- Trust
- Governance
- Observability
- Explainability
Shortly following the Aras event, I attended virtually the Siemens press conference from Hannover Fair.
Further research between the two revealed these thoughts from a variety of analysts.
Siemens Teamcenter Copilot is powerful but bounded. Siemens’ approach includes Teamcenter Copilot and AI Chat for natural language queries, RapidMiner for spotting quality issues, and AI extraction of procedures from static PDFs. Siemens describes it as “training AI in the language of engineering and manufacturing” — embedding domain-specific intelligence aligned with physics, lifecycle context, and operational constraints.
However, what Siemens is doing is focused, practical, and grounded in helping users navigate data Siemens already manages well. The copilots do not attempt to extend beyond Teamcenter — they do not ingest data from other PLM tools or external systems that influence product decisions, and the improvements remain confined to the boundaries of one platform.
Aras’s approach is architecturally more open. InnovatorEdge is designed so that product data, processes, and digital thread remain governed inside the core platform, while Edge services make them consumable everywhere else — enabling agents to link data across PLM, ERP, IoT, and documents.
One independent analyst commentary summarized the broader landscape bluntly: all four major PLM vendors — Siemens, Dassault, PTC, and Aras — are adding AI inside their products, but none of them are rethinking PLM architecture for an agent-native future. They are embedding assistants inside old systems rather than redesigning systems around the needs of agents. That said, Aras’s open, low-code, API-first architecture puts it structurally closer to an agent-ready foundation than Siemens’s more monolithic platform.
ACE attendees noted that while AI’s transformative potential was clear, discussions also centered on the need for human oversight, data governance, and addressing concerns about traceability and the dynamic nature of LLMs — suggesting customers are excited but appropriately cautious about full autonomy.
by Gary Mintchell | May 13, 2026 | Generative AI
The world of AI and Agents seemingly focuses on OpenAI and Anthropic thanks to their leaders’ bold dystopian statements. There are others—even ones who have application in manufacturing.
Google sent me a rare piece of news from it’s recent annual user conference, Google Cloud Next ’26, held in Las Vegas. Not to be outdone, Google has Gemini. During the conference GE Appliances announced it is using Gemini Enterprise to deploy over 800 AI agents across its manufacturing, logistics, and supply chain operations, putting AI into the hands of the people closest to the work.
OK, I just attended a conference where two active agents were demonstrated with several more on the way. Somehow 800 seems like more than can be managed. But, that’s what they say.
I may not have heard much about GE Appliances since my company built a helium mass spectroscopy testing machine for their “factory of the future” in 1985.
I keep telling people that I don’t want theory. Actual applications, no matter how small, are more interesting to me. Here are three examples provided by Google and GE Appliances:
- Manufacturing: GE Appliances embedded Gemini Enterprise into its Brilliant Factory manufacturing data platform, which has enabled agents to analyze shift data in minutes rather than hours, allow employees to talk to production data to diagnose issues quickly, and provide live view of line yields and equipment health to reduce downtime.
- Logistics: GE Appliances used Gemini Enterprise to build its Quality Insights AI tool for AI-assisted analysis which has yielded measurable results, uncovering millions of dollars in improvement opportunities across customer logistics and internal operations.
- Supply Chain: GE Appliances introduced a Supplier Collaboration Agent to manage communication with more than 600 suppliers. This agent automated order status inquiries, leading to a 25% reduction in backorders and allowing the team to focus on high-value strategic growth.
