by Gary Mintchell | Jun 25, 2026 | Commentary, News
I’ve lost a day’s productivity today driving into Chicago and back home. The backlog of company news continues to stack. I’ll tackle it tomorrow. First some impressions.
I can’t believe how large the show was this year. Someone told me it was a result of consolidating shows—but that was inaccurate. Show officials told me growth was organic, some due to more international presence (both exhibitors and attendees).
Booths were packed with many interested conversations.
Twenty-eight years attending these as an editor/writer, and I’ve never had so many blown off appointments. Don’t know if this is a trend, but if so it is not a favorable one.
On the other hand, the appointments I had were uniformly useful and interesting.
A lot of robotics. Many wanted to talk AI, but those conversations were mostly too vague. The humanoid robotic pavilion was more akin to a toy or game area. Some day, maybe.
Next year will be May in Las Vegas. I doubt that I’ll make that trip. There will not be any compensation to cover expenses, and I can cover any news through press releases.
by Gary Mintchell | Jun 21, 2026 | Generative AI, Manufacturing IT, Podcast, Services, Software
Nothing in industrial technology news annoys me more than the hype around artificial intelligence—AI. I recorded this podcast on the eve of the Automate trade show and conference in June 2026.
Looking to for realistic use of Industrial AI, I’m bringing in an interview with a practitioner. Bryan DeBois is Director of Industrial AI at RoviSys, one of the largest independent system integrators. He has 20 years in MES, historians, and plant floor software. He leads teams that operationalize AI and data infrastructure in live plants, working with the C suite and ops to turn goals into running systems.
We look at definitions of AI. Then turn to the technology development from when RoviSys developed its AI practice in 2019 pre-LLMs. RoviSys took autonomous AI beyond predictive applications. Hiring deep manufacturing expertise, they can use AI to assist the human in the loop to make constrained decisions. DeBois discusses real-world applications. He then leads us through the beginning of a project.
Watch on YouTube.
Listen on your favorite podcast app or on my Website.
by Gary Mintchell | Jun 18, 2026 | Edge, Manufacturing IT, Operations Management
Rockwell Automation ’s software group has undergone substantial change over the past few years. It seems to have settled into a groove lately. I’d imagine that its story at this year’s Automation Fair in November in Boston should be interesting.
The TL;DR of today’s announcement—New product offers a unified execution architecture, bringing intelligence, resilience and enterprise scalability to modern manufacturing operations
Rockwell Automation announced June 18, 2026 the availability of FactoryTalk ResilientEdge, a next-generation execution architecture designed to support autonomous manufacturing operations across highly-automated environments.
Built on FactoryTalk Optix and integrated across Rockwell Automation’s portfolio, including Plex Manufacturing Execution System (MES), FactoryTalk ResilientEdge creates a single execution layer that spans machines, people and production systems. The platform delivers predictable, low-latency execution at the edge along with cloud capabilities that enable analytics, Artificial Intelligence (AI) training and enterprise orchestration. The combination of edge and cloud means that operations are continuous even if connectivity is lost.
The edge-to-cloud interaction comprises the bulk of such announcements lately. Connection points, of course, constitute the key interface for real action.
FactoryTalk ResilientEdge turns advanced manufacturing capabilities into a standard operating infrastructure by unifying plant models, connectivity, execution and intelligence into a single framework. Within FactoryTalk ResilientEdge, users will find a variety of innovative features: shared production model, native and interoperable connectivity, real-time edge execution with embedded business logic, cloud-scale analytics, and AI. The result is an execution system that eliminates the divide between Operational Technology (OT) and Information Technology (IT), dramatically reducing the complexity of deploying and evolving modern manufacturing operations.
“At a time when 95% of manufacturers are advancing AI and machine learning initiatives, FactoryTalk ResilientEdge enables a new class of manufacturing execution,” said Anthony Murphy, vice president of product management, Rockwell Automation. “Manufacturers can scale automation, intelligence, and autonomy across their operations while preserving the economic and scalability advantages of the cloud, helping manufacturers deploy faster and lower their total cost of ownership.”
Three key points
- Enabling AI-Driven Autonomy—Modern automation initiatives require reliable execution, structured data flow and scalable architecture as the foundation for advanced analytics and AI initiatives. FactoryTalk ResilientEdge delivers a resilient execution layer that supports advanced analytics, AI and closed-loop optimization without compromising plant-level performance.
- Secure, Interoperable and Built to Scale—FactoryTalk ResilientEdge helps manufacturers modernize operations by improving operational resiliency, optimized for Rockwell Automation ecosystems while remaining open and interoperable across heterogeneous production environments. The security, interoperability and scalability of the new offering is a testament to Rockwell’s elastic MES solutions.
- Faster Deployment and Lower Lifecycle Cost—By reducing integration complexity, centralizing monitoring and supporting modular scalability, FactoryTalk ResilientEdge can lower lifecycle costs and accelerate deployment. FactoryTalk ResilientEdge capabilities can be deployed as needed, supporting companies who phase their modernization strategy.
Click on the Follow button at the bottom of the page to subscribe to a weekly email update of posts. Click on the mail icon to subscribe to additional email thoughts.
by Gary Mintchell | Jun 17, 2026 | Business
PwC sends regular merger and acquisition and related reports. This update seems timely as I’ve heard from a number of companies restructuring. It’s been some time since I attended a Hexagon conference where they announced spinning off manufacturing software now known as Octave. Or GE Vernova spinning off manufacturing software to private equity who also picks up ThingWorx and Kepware from PTC then spinning that combination off. I just saw news from the Honeywell conference that detailed its divestitures.
Industrial manufacturing M&A has entered a transformative phase, with deal values hitting a record $173 billion over the past year, a 28% increase over FY25’s $135 billion. This surge is driven by the convergence of AI infrastructure, grid modernization, and defense/resilience, all pulling capital toward a shared industrial supply base. Mega-deals, scope-driven acquisitions, and strategic buyers are deploying capital like never before, while macroeconomic uncertainty has become a permanent feature, not a headwind.
Key findings:
- Mega-deals are dominating. Transactions above $5 billion now make up 56% of deal value, up from 18% in FY24, with the largest deals capability-driven rather than scale-focused.
- Value growth is widespread. Average deal size, excluding mega-deals, grew 31% from FY24 to $169 million.
- Convergence is concentrating value. Power equipment, thermal management, automation and controls, and advanced components are attracting outsized valuations.
- Strategic buyers are dominating. Strategic acquirers account for 86% of the last twelve months’ (LTM) deal value, the highest concentration on record.
- Corporate divestitures are accelerating. Conglomerate simplification is generating a rich pipeline of carve-outs.
Average deal size tells a compelling two-year story: $155 million in FY24, $288 million in FY25, and $375 million in the latest annual period—a 139% increase reflecting buyers consistently paying up for transformative capabilities rather than incremental scale. A single $35 billion transaction in April 2026 accounted for roughly half of the increase over FY25, underscoring the outsized influence of a few key transactions, though rising valuations are not solely a mega-deal phenomenon.
I’m not surprised by this analysis of concentrating value.
Convergence is concentrating value into a narrow set of assets. Industrial manufacturing sits at the crossroads of AI infrastructure, grid modernization, and defense and resilience spending, all drawing on the same constrained supply base of power equipment, thermal management, automation and controls, and advanced components. From 2021 to 2025, industrial manufacturing accounted for 155 convergence deals and $532 billion in transaction value, more than any other industrial subsector.
AI must make an appearance. Usually this technology for our market solves specific problems eschewing the hype of the AI companies competing for hype and investment dollars rather than solving specific problems.
AI and automation are now central to investment diligence. Investors increasingly demand evidence of AI impact in the income statement through productivity improvements, labor cost offsets, and predictive maintenance savings, before committing to premium valuations.
Private equity, while more disciplined than in prior cycles, remains active in the upper mid-market, deploying capital into buy-and-build platforms in areas such as test and measurement, flow control, filtration, and thermal management. Strategic acquirers, however, are dominating at scale, accounting for 86% of LTM deal value and 86% of YTD 2026 volume, the highest concentration on record.
Corporate divestiture activity is broad-based. Conglomerate simplification, exemplified by Honeywell’s three-way separation, is generating a rich pipeline of carve-outs across automotive-exposed, advanced materials, and non-core industrial assets as companies realign portfolios toward electrification, software, and defense-related manufacturing.
The macroeconomic backdrop has become a permanent structural feature rather than a cyclical headwind. Cross-border deal value has reached 56% of the LTM total, up from 30% in FY22, driven by global supply chain reconfiguration and reshoring investments, with US-targeted deal value nearly doubling in FY25 to $72 billion. Tariffs, geopolitical friction, interest rate volatility, and AI-driven infrastructure demands are now constant factors, fueling M&A rather than suppressing it.
Key M&A trends set to influence industrial manufacturing
Uncertainty is the new operating environment—and the market rewards action over hesitation.
Two forces will define industrial manufacturing M&A in the second half of 2026.
The first is convergence. AI infrastructure, grid modernization, and defense/resilience spending are pulling capital toward the same constrained industrial supply base: power equipment, thermal management, automation and controls, and advanced components. This is concentrating value in assets that serve multiple demand streams simultaneously, and premiums reflect it: 15 to 30% above sector medians, peaking in AI compute and data center-exposed assets. Dealmakers who have not yet organized their acquisition thesis around this overlap are already behind.
The second is the divestiture pipeline. Conglomerate simplification is accelerating. Among industrial companies executing $5 billion-plus acquisitions since 2021, nearly 69% also divested, rising above 86% for serial acquirers. The most attractive carve-outs in advanced materials, automation components, and energy transition assets will not wait for macro clarity.
What dealmakers should do now:
- Underwrite convergence, not single-theme exposure. Assets serving two or three demand streams command durable pricing power. Assets serving only one face selective competition. Diligence should stress-test capability density against converging demand, not cost takeout against a single end market.
- Demand measurable AI returns. Buyers now require evidence of productivity gains in the income statement, including throughput improvements, labor cost offsets, and predictive maintenance savings, before paying premium valuations. The era of paying up for AI narratives without quantifiable impact is ending.
- Move early on carve-outs. Sellers processing divestitures know exactly what they are funding next. Position ahead of the process. Inaction is its own strategic risk. The dealmakers capturing value in this environment are not waiting for certainty. They are underwriting the structural trends already visible in the data.
“Convergence is concentrating value into a narrow set of assets. The next six months will sharpen the divide between disciplined acquirers and everyone else.” – Michael Fiore, Industrial Products Deals Leader
The bottom line: What industrial manufacturing dealmakers should watch
The 2025–2026 acceleration is structural, not cyclical. Deal value has reached $164.0 billion, with convergence concentrating premiums into a narrow set of constrained infrastructure assets. Accelerating corporate divestitures are creating a rich pipeline of actionable assets. Macro uncertainty is a permanent feature, not a passing headwind, and cross-border deal value has surged to 56% of the current-year total, reflecting global supply chain reconfiguration. Dealmakers that align capital strategies with infrastructure resiliency, AI build-out, electrification, and defense spending will find significant opportunities in the second half of 2026. The market rewards action over hesitation.
by Gary Mintchell | Jun 16, 2026 | Business, Leadership, News, Productivity
Given my interests about manufacturing, Lean, strategy, leadership—and dealing with strong bosses with mental health issues—I couldn’t resist reading a promo copy of a book covering all of those. And, recommending it. Go, get the book, study it with your team. It’s that good.
Set up with an appointment by Meta’s Sheryl Sandberg, Jon McNeill met Elon Musk who said to him, “I have a problem with Tesla. I can’t get Model X doors to align.” Two hours later, Jon McNeill and Elon Musk were tackling the problem head-on and soon, Jon would join Elon as President of Global Sales, Marketing, Delivery & Service at Tesla (2015-2018).
McNeill, now CEO of DVx Ventures, who sits on boards at GM and Lululemon, says most companies are about to discover the same bottleneck Tesla hit: not enough skilled workers to execute complex processes. He explores this in his new book, THE ALGORITHM: The Hypergrowth Formula That Transformed Tesla, Lululemon, General Motors, and SpaceX (Portfolio; March 24, 2026). He details both the things he learned from Musk as well as the difficulties of working for someone with mental health issues.
The book is packed with stories showing (not telling) how leaders attack team problem solving with out-of-the-box thinking.
Try this on for size. At Tesla, the body shop required scores of robots and a team of robotics engineers to keep them synchronized. It was expensive, complex, and fragile. Jon’s team questioned whether the entire body shop could be eliminated (almost three football fields of warehouse space). The answer: casting. By reducing the chassis from 300 welded parts to 3 cast parts, Tesla simultaneously cut manufacturing costs 50% AND made the process more resilient (fewer dependencies, fewer failure points) by questioning requirements.
Gathering my thoughts, what more perfect time to publish this review than immediately following the Initial Public Offering of SpaceX (which includes xAI and X-Twitter). The trillion-dollar valuation also propels Elon Musk into the stratosphere of finance making him humanity’s first trillionaire.
Musk takes big risks on audacious goals. Enough pay off big to make up for the failures.
- The Algorithm? The five steps contain similarities to Lean and the Toyota Production System:
- Question requirements: Do we need this level of complexity?
- Delete steps: What can be eliminated before we relocate?
- Simplify: Can we redesign for fewer dependencies and lower skill requirements?
- Accelerate: What’s the constraint in a U.S. context? (Often: specialized labor)
- Automate last: Only after simplification should we consider technology (VERY IMPORTANT STEP TO NOT DO THIS FIRST!).
Here are a couple of relevant notes from the book:
Much of the genius in Musk’s companies comes from the legions of smart people empowered by the Algorithm. They’re chasing stretch goals with free license to question everything and innovate boldly.
The Algorithm features a set of best practices, yet one overarching idea infuses them all: Question the status quo.
