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.
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.
John Gruber nails it in a recent Daring Fireball post. He refers to a news item quoting incoming Apple CEO John Ternus saying Apple has always shipped products, not technologies. Steven Levy writing in Wired ripped Ternus for not laying out a plan to ship AI. But AI is a technology. Apple will continue to add AI into products that we will find useful and enjoyable.
Check my preceding blog post about “Physical AI” humanoid robots at the upcoming Automate show. Looking at that market, A3 president Jeff Burnstein told me that the developers are throwing out a technology hoping users will find a use. The major AI companies are doing the same thing. Flailing around searching for a market to justify billion-dollar investments.
I was taught, and still believe, that product development means looking for potential user pain points and solving them with a product probably using the latest technologies.
Market research firm, Interact Analysis, sent this analysis of factory construction in the United States, Why has US reshoring not translated into meaningful factory construction? Written by senior analyst Matthieu Kulezak, the research notes that following a wave of investment from 2020 to 2024, the momentum is clearly fading “with leading indicators pointing to a sharp slowdown of new project activity.”
One of the clearest signals comes from the Index of Business Applications for Manufacturing Facilities. While applications fluctuated at elevated levels throughout 2024, momentum weakened significantly in early 2025. Total applications fell by 39.1% year-on-year in May 2025, for example.
As one of several indicators informing our forecast, the slowdown in new project applications points to a weaker pipeline of upcoming factory builds. We have, therefore, revised down our outlook for new factory construction in the US. The slowdown in new project applications signals reduced momentum in greenfield development, which is now feeding through into our forecast. As a result, our Q1 2026 forecast shows a much sharper decline in activity, with indexed growth falling to 76.0 in 2026, compared to 105.9 in our previous Q4 2025 view.
The first Trump administration made a concerted effort to force companies to return manufacturing to the US—or, at least, move from China. The Biden administration had similar goals using different tactics. The second Trump administration showed even more aggression in that regard through selective use of tariffs and personal “conversations” with prominent CEOs. Not to mention Harry Moser’s Reshoring Initiative that tried to use media to persuade company executives of the value of manufacturing here.
This sharp contraction in new manufacturing applications stands in contrast to the prevailing narrative around reshoring and near-shoring. While policy support and strategic intent remain strong, the data suggests that this has not translated into a sustained pipeline of new factory construction. Instead, companies appear to be delaying or scaling back new investments in response to macroeconomic uncertainty.
The rate of factory construction dipped significantly in 2025, with 2026 so far showing a similar trend. The inflation-adjusted index rose sharply from around 5,500–6,000 between 2017 and 2020 to a peak of 12,070 in December 2023, reflecting strong investment in large-scale factories. However, momentum reversed in 2024, with year-on-year declines exceeding 20% in multiple months. This weakness continued into 2025, with construction values falling 10 to 19% year on year and stabilizing at around 11,200–11,600, well below peak levels.
While factory application counts could suggest that fewer but larger facilities were still being built, the decline in total construction value shows that large-scale projects are also slowing. Because this metric reflects the total size and capital intensity of factories under construction, it confirms that overall manufacturing capacity expansion has weakened, not just small facility construction.
Rising demand for manufactured goods in the US is being met by higher utilization rates and brownfield expansion, not new factory construction.
The decline in new factory construction does not mean U.S. manufacturing is weakening. The U.S. already has a large and mature manufacturing base, so rising demand is increasingly met by expanding existing sites and increasing throughput rather than building new facilities. This is reflected in capacity utilization, which recovered from 62.5% in April 2020 to around 77–78% in 2021–2022. Although it softened in 2024, utilization stabilized and increased through 2025, rising from 74.5% in January to around 75.5% by December.
I’ve seen this following thought enacted by a few recent announcements of investment in current facilities.
An increase in production capacity or reshoring does not necessarily mean a new factory is being built. In many cases, what is described as a “new factory” is actually an expansion or repurposing of an existing site.
For example, John Deere’s announced excavator facility in Kernersville, North Carolina, is a $70M expansion of an existing campus, not a greenfield factory. The site brings production previously carried out in Japan into an existing U.S. facility and adds around 150 jobs, but it reflects capacity relocation and expansion rather than the creation of a new standalone factory. This distinction is important when analysing manufacturing growth. Output can increase through expansions, automation, or relocation without increasing the total number of factories. As a result, reshoring and investment announcements may signal higher domestic production, but they don’t always correspond to growth in the physical factory count.
I receive great value from the wisdom and generosity of Seth Godin. He thought out this progression of business value. As you create products and companies and personal value, consider this deeply and seriously—Create value by connecting people.
The first generation was built on large models, demonstrating what could be done and powering many tools.
The second generation is focused on reducing costs and saving time. Replacing workers or making them more efficient.
But you can’t shrink your way to greatness.
The third generation will be built on a simple premise, one that the internet has proven again and again:
Create value by connecting people.
We haven’t seen this yet, but once it gains traction, it’ll seem obvious and we’ll wonder how we missed it.
Create tools that work better when your peers and colleagues use them too. And tools that solve problems that people with resources are willing to pay for.
Problems are everywhere, yet we often ignore them.
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I have been involved with recycling since the mid-80s. I hate waste—whether as in Lean or as in throwing stuff away. This news came to me from a company called Woodchuck—a clever play on words since they recycle wood. Also a good example of effective use of AI.
Grand Rapids, Michigan – March 24, 2026 – Woodchuck, the AI-powered climate-tech startup redefining how construction and manufacturing industries handle wood waste, today announced a joint sustainability initiative with Walbridge, one of the nation’s top industrial and automotive constructors. The program supports Ford Motor Company’s construction waste-reduction efforts at its new manufacturing facility in Marshall, Mich.
In the first three months, the program has given teams a clearer view of the materials being discarded, diversion rates, cost reductions, and operational efficiency — already achieving 40% of the project’s projected materials-related savings. This early progress offers Walbridge a powerful solution to address customers’ waste management needs and lays the groundwork for a new standard operating procedure for future large-scale construction projects.
A Legacy Builder Confronts a Modern Waste Challenge
For more than a century, Walbridge has delivered some of the most complex automotive and industrial projects in North America. As Walbridge’s customers expand their sustainability commitments, construction waste management is a growing priority — particularly on megaprojects where the volume and variability of materials can shift daily.
On the Ford project, wood waste quickly emerged as one of the most unpredictable waste elements. Crating, dunnage, international shipping pallets, and custom rigging arrived in wide-ranging sizes and material types, creating a diverse and constantly changing waste stream.
These complexities revealed opportunities for innovation. Walbridge saw the potential to elevate efficiency, reduce hauling expenses, and strengthen alignment with Ford’s sustainability goals. The need for real-time visibility into container levels and the makeup of each load became a catalyst for adopting a smarter, data-driven solution — one that made waste handling more predictable, cost-effective and sustainable.
“Our partnership with Woodchuck is built on collaboration. Transparent and real-time communication allows our team to adapt quickly to changing material waste streams on the ground. Detailed information about each load provides complete visibility not only into what is diverted from a landfill, but also into its end destination and intended use, delivering transparency and enabling measurable sustainability outcomes,” said Sander Mathijs, Walbridge Sustainability Manager. “Another key program feature is its ability to scale, allowing us to calibrate capacity and scope to meet the waste‑diversion needs of the project.”
Woodchuck’s AI Platform Delivers Immediate, Scalable Impact
Woodchuck.ai leverages its AI platform across the Ford project to track, report and validate the diversion of wood, cardboard, plastic, and metal; all with minimal onsite labor and seamless integration into Walbridge’s existing workflows.
Walbridge saw meaningful improvements within the first quarter diverting thousands of tons of wood, cardboard, plastic and metal; reducing waste, reducing landfill dependency, and reducing costs. Over the course of the project, Woodchuck will divert 8,000 tons of wood and 1,000 tons of cardboard, plastic, and metal from landfills.
Woodchuck’s detailed reporting also strengthens accountability, giving Walbridge clear data documenting recycling and reuse for both internal tracking and customer sustainability documentation.
Because the Woodchuck platform is designed for large, multi-phase construction programs, the improvements seen at the Marshall project can be replicated at scale. Whether deployed on a single megaproject or rolled out across multiple sites, contractors gain the same visibility, control, and cost efficiencies, making the solution a powerful model for nationwide waste management and sustainability performance.
“Our partnership with Woodchuck has been a game-changer,” said Ross Linton, Group Vice President, Walbridge. “In just a few short months, they’ve helped us transform our waste process to one that’s measurable, trackable, and easily managed. Our team is empowered to plan ahead, driving efficiency and sustainability. We’re excited about the future possibilities this collaboration brings.”
Creating a New Standard for Future Walbridge Projects
Based on early results, Walbridge expects the Woodchuck-enabled process to become a foundation for future large-scale builds across automotive, manufacturing, technology, and advanced industrial sectors.
“Walbridge is demonstrating what it looks like when a contractor treats waste as a strategic input rather than an afterthought,” said Todd Thomas, CEO of Woodchuck. “By embracing real-time data, AI-enabled insights, and a commitment to measurable sustainability outcomes, they’re proving that smarter waste management isn’t just good for the environment — it’s good for productivity, cost efficiency, and project certainty. Their leadership on Ford’s Marshall project shows what’s possible when innovation becomes part of the construction workflow, and they’re setting the pace for how the industry will operate going forward.”
About Woodchuck
Woodchuck is a climate impact start-up dedicated to empowering contractors, manufacturers, and biomass energy producers by streamlining wood waste diversion and processing. We are committed to leveraging advanced AI technologies to transform waste into valuable resources, reduce landfill usage, and provide a steady, sustainable supply of biomass. Based in Grand Rapids, Michigan, Woodchuck is funded by an investor syndicate led by Mason Fink, Beckett Industries, NorthStar Clean Energy and Alloy Partners. For more information, visit https://woodchuck.ai/.
About Walbridge
Walbridge is one of America’s largest privately held construction companies founded in Detroit in 1916. The company offers construction management, engineering, and real estate services for customers in manufacturing, hyperscale data centers, automotive, defense, higher education, health care, and government. Walbridge employs more than 1,500 professionals in North America. Visit www.walbridge.com or connect with us on LinkedIn to learn more.
Process Description
Woodchuck uses AI in two fundamentally different—but tightly connected—ways: at the job site and in the data layer. Together, they turn what was once an opaque, manual waste process into a real-time, measurable system.
1. AI at the Job Site: Shifting Sorting to the Beginning. Traditionally, construction waste sorting happens after the dumpster is full—if it happens at all.
That process is:
Manual and labor-intensive
Expensive to perform at scale
Logistically inefficient
Often skipped entirely
The result? Most mixed construction debris—especially wood—ends up in landfills, even when it could have been reused or converted into energy.
Woodchuck flips this model.
Instead of waiting until the end, Woodchuck uses AI-enabled image recognition at the point of disposal:
As materials are placed into dumpsters, cameras and sensors identify what’s being thrown away
The system distinguishes wood from other materials in real time
It guides proper usage of containers and flags contamination early
This front-end sorting approach changes everything:
Reduces contamination before it becomes a problem
Eliminates the need for costly post-collection sorting
Increases diversion rates dramatically (from <30% to >95%)
Ensures clean wood streams that can be converted into renewable biomass
In short, AI moves sorting from a reactive, end-of-process activity to a proactive, in-the-moment decision.
2. AI in the Data Layer: Turning Waste into Intelligence
Once materials are collected, Woodchuck’s platform continues to track and analyze everything that happens next. This is where the second layer of AI comes in: data aggregation, modeling, and reporting.
Through its dashboard, Woodchuck provides construction companies, developers, and asset owners with full visibility into their waste streams, including:
Material tracking
Exactly how much wood was collected, where it came from, and how it was processed
End-of-life transparency
Clear documentation showing where the material went—whether to biomass facilities or other reuse pathways
Carbon impact metrics
Precise calculations of:
CO₂e emissions avoided from landfill diversion
Carbon benefits from renewable energy generation
Energy output conversion
How much renewable energy was produced from their waste (e.g., BTUs generated, equivalent homes powered)
Operational insights
Trends across projects, contamination rates, and opportunities to improve efficiency
This transforms waste reporting from a rough estimate into a verified, auditable dataset—something increasingly critical for:
ESG reporting
Regulatory compliance
Winning sustainability-driven bids
Internal performance benchmarking
3. From Waste Management to a Measurable System
What makes Woodchuck different is not just the use of AI—it’s where and how it’s applied:
At the edge (job site): AI drives behavior change and improves material quality in real time
In the platform (dashboard): AI converts operational data into financial, environmental, and strategic insights
The result is a closed-loop system where:
Waste is captured correctly from the start
Materials are tracked through their full lifecycle
Outcomes are quantified and reported with precision
Construction companies no longer have to guess what happened to their waste—or treat it as a cost center.
They can see it, measure it, and increasingly, use it as a source of savings, energy, and competitive advantage.
Check out the sidebar ad about the Carbon Almanac. Written and edited by a hundred volunteers, this book contains many ways to help solve the carbon waste problem.
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