By Marc Sanderson

Every CEO eventually faces an uncomfortable reality: the strategy that once made sense is no longer delivering results. Revenue stalls. Margins shrink. Your team works harder, but the business isn’t moving forward.

The instinct? Push harder. More sales activity. More accountability. More operational discipline. Maybe even make changes to the team. Sometimes that’s the answer. But sometimes you’re executing a strategy that no longer makes sense.

In woodworking manufacturing where capital investments are significant, skilled labor is scarce and competitive pressures are evolving, knowing the difference is critical.

The question isn’t simply, “Why isn’t our strategy working?” It’s “What did we get wrong, and what are we willing to change?”

Here are five moves every CEO should consider when their business strategy stops delivering results.

1. Challenge the context before changing the course.

Has the world changed around us?

When a strategy fails, leadership teams typically revisit the plan. They adjust priorities, reset targets and push harder on execution. The better move is to revisit the assumptions behind the plan. Strategy is built on context. When the context changes, the strategy may no longer be valid.

Earlier this year at INNERGY, we revisited our competitive environment through the lens of artificial intelligence (AI). Using Porter’s Five Forces framework, we examined how AI was changing customers, competitors, suppliers and potential new entrants.

One question stood out: What happens when customers can use AI to do more themselves? A customer who once needed a complete software solution may now use AI to fill certain gaps. That changes our value proposition—and potentially what customers are willing to pay.

The same principle applies to woodworking manufacturers. Automation changes production economics. Overseas competition reshapes pricing expectations. Technology expands what customers can do internally. Labor constraints challenge existing business models.

A strategy that made sense yesterday may not make sense tomorrow. Strategic planning isn’t an annual event. It’s a continuous response to reality. If the world changes in January, don’t wait until October’s planning retreat to respond.

Change can arrive suddenly (like AI) or be a slow boil (like overseas competition). The magnitude and speed of the change should determine the response. Major contextual changes require faster actions and extensive strategic review. Minor changes still require attention, but the moves tend to be more tactical.

2. Confront your assumptions before committing to ambitions.

Did we get it wrong from the start?

Sometimes the market hasn’t changed. We simply misunderstood it. Perhaps we overestimated our competitive advantage, misunderstood customer needs or pursued a market where the economics were never in our favor.

Consider a woodworking firm serving the hospitality industry. About 75% of its business comes from large hotel projects—competing against overseas manufacturers. The remaining 25% comes from specialized, high-end boutique properties. The company is one of the last domestic fabricators serving this market.

That sounds like a competitive advantage. But is it?

In standardized hotel projects, the fabricator faces Asian manufacturers with significant structural cost advantages. In boutique properties, however, customization, engineering, responsiveness—and the advantages of proximity and shorter lead times—create opportunities to differentiate. The problem isn’t necessarily only that the market changed. It did. It manifested slowly over 20 years. The real issue is the company failed to recognize the slow boil and distinguish where it could create and capture value.

The strategic question isn’t how to work harder at winning the 75%. It’s whether the company should shift toward segments where its capabilities truly matter. That requires validating the opportunity, rethinking its customer mix, aligning operations and potentially walking away from unprofitable work.

It’s difficult. We become attached to the businesses we’ve built and the investments we’ve made. It can feel personal. But, holding on isn’t a strategy. You cannot grab the next trapeze bar without letting go of the first.

3. Get closer to customers before claiming to know them.

Do we truly understand the problems our customers are trying to solve?

One of the biggest strategic mistakes is assuming we understand customers better than we do. In woodworking, we often default to four dimensions of value: cost, quality, delivery, and service. Of course they matter. But they’re starting points, not a differentiated value proposition.

What problem is the customer actually trying to solve? And how can we solve it better than the alternatives?

A cabinet manufacturer may believe its competitive advantage is quality, while its dealer’s biggest frustration is unpredictable lead times. A commercial millwork customer may emphasize price but place greater value on engineering support that prevents expensive field problems.

Talk directly with customers. Conduct interviews and focus groups. Understand their operations, frustrations and unmet needs.

At INNERGY, we reinforce customer intimacy through education, engagement and a high-level of direct executive participation. We bring customers into our events and create opportunities for leaders throughout the organization to build relationships.

Customer intimacy must become part of the organization’s DNA—not simply a sales department responsibility. You cannot build a differentiated strategy around customers you don’t deeply understand.

4. Run the numbers instead of relying on your gut.

Do the numbers validate our growth strategy?

Experienced leaders often rely on intuition—and for good reason. But gut instinct is no substitute for good math. I’ve seen companies pursue ambitious growth plans without answering basic questions about market size, competitive share or the capacity needed to achieve their goals.

You don’t need a sophisticated financial model. Sometimes one spreadsheet is enough.

At INNERGY, we distilled our long-term ambition into 10 key variables. That exercise clarified what the business could become over the next five to seven years. The numbers made the opportunity tangible—and exposed the constraints.

Imagine a woodworking firm planning significant growth within a 100-mile radius. Before investing in equipment or facilities, the CEO needs to understand the addressable market, competitive landscape, current market share and share required to reach the target. What if the local market simply isn’t large enough? That’s a strategic problem—not an execution problem.

I like using simple two-dimensional market maps to visualize segments, market potential, current position and growth opportunities. They reveal where to pursue new customers, cross-sell products or capture additional share. They often become our visual game board for growth.

The goal isn’t more spreadsheets. It’s finding simplicity on the other side of complexity. The numbers don’t need to be perfect. They need to be credible enough to expose assumptions, identify constraints and guide decisions.

They also make an ambitious strategy believable. People commit more readily when they can see a clear path to the goal. The biggest mistake isn’t using imperfect numbers. It’s refusing to use them at all.

5. Turn strategic choices into sustainable success.

Are we capturing the value we create—and aligning the organization to deliver it?

Sometimes the strategy is sound, customers see the value and the market opportunity exists. Yet the economics still don’t work.

Consider a woodworking manufacturer investing significant estimating time into detailed, room-by-room quotations. Customers appreciate the transparency. But it also allows them to cherry-pick components and award the remaining work elsewhere. The company creates value but weakens its ability to capture it.

Before resetting the strategy, test how you package, price and deliver that value. Experiment with bid formats, bundled offerings and pricing. Measure win rates, margins and customer response. Sometimes the problem isn’t the strategy. It’s how you monetize the value proposition.

Even the right value proposition fails without organizational alignment. If customization is your competitive advantage but production is optimized for standardized runs, there’s a disconnect.

At Wilkie Sanderson, we focused on one operational measure: the percentage of work released by the first of the month. It aligned the organization around throughput and on-time delivery.

Translate strategic choices into measurable behaviors. Context drives strategy. Strategy drives structure. Structure drives behavior. And behavior determines results.

Context → Strategy → Structure → Behavior → Results

The Toughest Call: Knowing When to Let Go

Not every missed target requires a new strategy. Sometimes better execution, clearer priorities or a sharper value proposition will get you back on track. But you can’t execute your way out of a fundamentally flawed strategy.

As CEOs, we’re expected to have conviction. We set direction, make investments and ask people to follow us. Admitting we got it wrong takes courage. When your strategy stops working, challenge the context. Confront your assumptions. Get closer to customers. Run the numbers. Align your organization to create—and capture—value.

Above all, be willing to change.

The numbers provide a mirror. They may not show you what you want to see, but they’ll reveal what you need to confront. The objective isn’t to prove your original strategy was right. It’s to build a business that can keep winning when the world changes.

Frequently Asked Questions About Business Strategy

1. How do you know when a business strategy isn’t working?

A business strategy may need reassessment when revenue stalls, margins decline, growth targets are consistently missed or increased effort fails to produce better results.

The key is distinguishing poor execution from a flawed strategy. Start by examining whether the market, customer needs or competitive environment has changed—or whether your original assumptions were wrong. If the economics no longer support your business model, working harder won’t solve the underlying problem.

2. When should a CEO change strategy versus improve execution?

A CEO should focus on execution when the market opportunity remains attractive, customers value the offering, and the business model is economically sound—but organizational performance falls short.

A strategic change may be necessary when customer needs shift, competitive advantages erode, or the market cannot support profitable growth.

The distinction is critical: Execution problems require better performance. Strategic problems require different choices. Before changing direction, validate the assumptions, examine the numbers and determine whether the organization is aligned to deliver the strategy.

3. How can woodworking manufacturers improve their competitive advantage?

Woodworking manufacturers can strengthen their competitive advantage by identifying customer segments where their capabilities create meaningful differentiation.

Rather than competing solely on price, manufacturers should evaluate opportunities in customization, engineering expertise, delivery reliability, customer service and specialized markets.

or example, a domestic manufacturer competing against lower-cost overseas suppliers on standardized hotel projects may find greater opportunity in high-end boutique hospitality, where customization and responsiveness carry greater value.

The goal isn’t to compete everywhere. It’s to compete where your capabilities matter most—and where you can capture the value you create.

4. What financial metrics should CEOs use to validate a growth strategy?

CEOs should evaluate a focused set of financial and operational metrics that connect market opportunity to profitable growth.

Key measures include:
·       Total addressable market (TAM): Is the market large enough to support our growth ambitions?
·       Market share: What percentage of the available market must we capture to reach our targets?
·       Revenue growth: Are we gaining traction in our chosen customer segments?
·       Gross margin: Are we capturing sufficient value from the products and services we deliver?
·       Production capacity and throughput: Can our operations support the planned growth?
·       Win rates and profitability by segment: Are we pursuing the right customers and projects?

A simple spreadsheet connecting these variables can reveal whether growth targets are achievable before committing significant capital or resources. The numbers don’t need to be perfect. They need to be credible enough to expose assumptions, identify constraints and guide better decisions.

Last year’s benchmarking headline was simple. High-profit shops out-earned the industry average by a wide margin, and the interesting question was what they did differently. That relationship held in 2026. What changed is the ground both groups are standing on.

 

The multiple held. The floor dropped.

 

In the 2025 US report, the average shop posted 9.88% EBITDA and the top quartile posted 24.10%. In 2026, those numbers are 7.47% and 17.83%. Divide either pair and you get roughly 2.4x both years, so the ratio survived intact.

 

The absolute gap did not. It narrowed from 14.2 points to 10.4, because the top quartile fell almost three times as far as the middle did. Average shops gave up 2.4 points of margin. High-profit shops gave up 6.3.

 

The cutoff moved with it. Qualifying for the top quartile in the US took 14.47% EBITDA in 2025 and 11.53% in 2026. Worth remembering that the IIBT force ranks the top 25% every year, so high profit is a rank rather than a fixed standard. A shop that held margin flat may have moved up a quartile without changing a thing.

 

 

The best shops now win more of what they bid

 

Last year the data supported an appealing idea, that the healthiest shops bid selectively and won less of what they chased. US high-profit firms converted 34% of bid value against 37% for all firms.

 

In 2026 that flips hard. High-profit firms convert 46%, all firms convert 35%. Whatever the top quartile is doing on the estimating side, they are winning noticeably more of what they put out, not less.

 

Software satisfaction and AI interest both jumped

 

Engineering software satisfaction in the US moved from 50% to 64% of firms reporting satisfied or very satisfied, the largest single-metric jump anywhere in the report.

 

AI automation as a stated 12-month priority went from 30% to 42%. Still last on the list of seven improvement areas, and still nowhere near profitability at 97%, but a twelve point move in one year says the conversation has shifted from novelty to something shops are budgeting attention for.

 

The bottom line

 

The rankings held their shape while the whole field slid down. That makes 2026 a year where your quartile position is a weaker signal than the margin behind it, and where clearing the high-profit bar says less than it did twelve months ago. If you want one number to work on before the next survey, make it contribution margin, where the 2026 US spread still sits at 38.00% for all firms against 41.59% for the top quartile. These metrics only scratch the surface of the data covered in the IIBT, if you want to have the full report sent to you next year, please make sure you participate!

 

See the 2025 vs 2026 comparison in one graphic, with every movement above, US and Canada side by side, on a single page.

How do we make money? Are we doing better than last year? And what should we be measuring against to know the business is actually healthy? Every shop owner runs on some version of those three questions, and none of them can be answered in isolation. A score only means something when there’s another score next to it.

That’s the whole point of the IIBT (INNERGY Industry Benchmarking Tool). It’s free, it’s anonymous, and it’s built specifically for millwork and custom woodworking businesses. This year set a record: 148 shops across the U.S., Canada, and Australia/New Zealand submitted their 2025 numbers. In the U.S., median revenue came in at $6.5 million, down slightly from last year, with a median of 31 years in business. Here’s what stood out.

 

Trend 1: High-profit shops earn almost 2.5x the margin, and it has nothing to do with wages

 

The median U.S. shop keeps $7.50 of every $100 in operating EBITDA. The top 25% keep $17.80.

 

The easy explanation would be that high-profit firms pay their people less, but the data says no. Across every role surveyed, pay between average and upper quartile firms lands within about 4% of each other, which is noise. The gap comes from productivity: how much work moves through the shop with the team they already have.

 

Trend 2: Most shops still lose money on installation. The best shops profit from it

 

For the median U.S. firm, installation brings in $12.70 of every $100 in revenue and costs about $13 to deliver. That’s a 3% loss on every install. Installation has historically run at break-even for this industry, so most shops aren’t even losing sleep over it.

 

Upper quartile firms are running install at nearly 23% margin. Not break-even. Profitable. The data doesn’t say the top firms found some install trick nobody else knows about, but it does prove install can carry margin instead of eating it. If install is still the part of the job nobody wants to look at, that’s exactly where to look.

 

Trend 3: Revenue per employee separates the shops that are keeping up from the ones falling behind

 

Here’s a 30-second self-check. Multiply your headcount by $226,000 and compare it to your actual revenue. That’s the U.S. benchmark for a 40-person shop (the number climbs slightly as firms get larger), and upper quartile firms run about 13% above it per person.

 

One more thing the data killed: the idea that bigger shops are automatically more productive. Doubling in size only adds about 2.5% in revenue per person, so if the number above surprised you, the fix probably isn’t hiring.

 

Want to see where your shop stands?

 

The 2026 IIBT goes deeper than three trends: income statements by shop type, performance by revenue quartile, and where profit is actually made across plant-processed work, buyout, and install. The shops in this data get to answer those three questions at the top with real numbers instead of gut feel. When the next IIBT opens, participate. Less than an hour of your time buys you a benchmark you can’t get anywhere else.

By: Marc Sanderson

 

Transparency is one of the few competitive advantages available to every leader. It doesn’t require venture capital, breakthrough technology or a patent—yet remarkably few companies consistently practice it.

 

We all value transparency when we receive it. We expect it from our leaders, our colleagues and the companies we buy from. But valuing transparency and practicing it—especially when it’s uncomfortable—are two very different things.

 

In business, transparency feels risky because it exposes imperfection. Yet, that’s precisely when it matters most. At INNERGY, transparency isn’t just something we try to do. It’s one of our core values. We expect every employee to pursue it—with one another and with our customers. We’ve learned that transparency doesn’t weaken relationships; it strengthens them by reducing uncertainty, building trust and accelerating learning.

 

Over time, I’ve come to think about transparency through three distinct forms of trust:

 

1. Operational Trust: People believe you’ll tell them what’s happening.

2. Relational Trust: People believe it’s safe to tell you what’s happening.

3. Strategic Trust: People understand why decisions are being made.

 

Those three forms of trust reinforce one another and become a lasting competitive advantage. Here’s how they play out practically:

 

1. Build Operational Trust: Show the bruised side of the apple.

 

Every company celebrates wins. Few celebrate bruises. Yet bruises are where learning happens. Our team often talks about showing the “bruised side of the apple.” If leaders only share victories, people quickly learn that mistakes aren’t acceptable. Problems get hidden instead of solved.

 

I intentionally admit my own mistakes, big and small, because every time a leader goes first, someone else gains permission to speak up. Research by Harvard Business School professor Amy Edmondson found that the best-performing teams weren’t making fewer mistakes, they were reporting them more openly. Their advantage wasn’t perfection. It was faster learning: fail fast, fix fast.

 

The bruises aren’t the problem. Hiding them is.

 

2. Build Relational Trust: Go first with vulnerability.

 

Transparency requires vulnerability, and someone has to take the first step. Make it you.

 

Many people don’t struggle with transparency because of the current experience, they struggle because of their previous one. They’ve worked in organizations where admitting mistakes meant embarrassment, blame or even termination. They bring that baggage with them.

 

I’ve learned that building a transparent culture isn’t simply telling people it’s safe. It’s proving it over and over again. I seek to model it consistently, in all aspects of my life. I protect people when they tell difficult truths. I don’t punish honesty (bad news doesn’t get better with age). Over time, people believe what they experience instead of what they hear.

 

When leaders go first, innovation follows because people feel safe enough to keep swinging.

 

3. Build Strategic Trust: Make your motives known.

 

People rarely question decisions as much as they question intentions. Every decision tells a story. If you don’t explain the “why,” people will often write their own version.

 

I recently spent an hour with a customer who was convinced we intended to raise prices simply because we could. He was new to our company through an acquisition and didn’t yet understand who we are. I explained that we can demonstrate that an average customer using our platform operates with about 4.5 fewer employees than similar businesses in the industry.

 

Share the Journey (Not Just the Victory)

 

Relational trust is the hardest to build and sustain. People can handle slow progress. They struggle with hidden progress. Transparency isn’t simply announcing the breakthrough. It’s about being open about the process and what it takes to improve.

 

I recently spent significant time in Australia meeting with customers because of technical support challenges. Those conversations led us to identify an AI solution that will dramatically improve support with the focus on achieving RAVING FANS. It would have been easy to announce the exciting part and stop there.

 

Instead, we also need to explain the harder truth. Deploying AI is only one step. Fully solving the problem requires planning, implementation, change management—and time. Transparency sometimes means asking customers or colleagues for patience while you earn back their confidence.

 

The Trust Equation

 

We experienced this firsthand during an unexpected company-wide outage. What stood out wasn’t the outage—it was our culture. Before anyone assigned responsibilities, employees instinctively ran toward the problem. One person launched a communication channel, another rerouted our support line, many others contacted customers, and we published updates every ten minutes.

 

We didn’t need a detailed disaster recovery playbook. We had a shared commitment to creating RAVING FANS, and everyone knew that taking care of customers came before protecting appearances.

 

During our planned product release two business days later, we addressed the outage head-on. Customers thanked us—for communicating openly, acting quickly and demonstrating that we cared. They knew we were running to the problem and respected our transparency.

 

That experience reinforced an important lesson: Transparency isn’t valuable because it makes people feel better. It’s valuable because it reduces uncertainty. And when uncertainty declines, trust grows. Every transparent conversation is another deposit into the trust account.

 

Trust behaves like compound interest. Every transparent conversation adds a small amount. Every mistake that’s hidden withdraws a much larger amount. Over time, organizations—and leaders—with high trust move faster because they spend less time overcoming skepticism.

 

Transparency isn’t always comfortable. It isn’t always convenient. But it’s always worth it because it is a competitive advantage.

 

Marc Sanderson is the CEO of INNERGY and former President/Owner of Wilkie Sanderson. He began in the millwork industry 20 years ago with a small cabinet shop and built it into one of the most profitable operations in the country. A Harvard MBA and 2024 Wood Industry Market Leader, Marc is known for transforming complexity into clarity through data-driven strategy and culture first leadership. Equal parts analytical operator and motivator, he’s just as comfortable refining strategy as he is rallying a room around a shared vision.

Every shop produces sawdust. The ones pulling ahead are producing something more valuable: data they actually use.

 

That was the core idea behind INNERGY’s recent webinar, Data Is the New Sawdust, where our team sat down with two operators who have made data a central part of how they run their businesses — Casey Schlaegle, owner of Schlaegle Design Build Associates (SDBA) in Pittsburgh, PA, and Jason Neff, COO of Mission Bell Manufacturing in California.

 

Between them, they represent two very different shops: SDBA runs about 45 employees and $10M in revenue doing commercial architectural woodwork nationally; Mission Bell operates at roughly 250 employees and $70M in revenue focused on commercial architectural millwork and casework. Different in scale. Very similar in what they’ve learned.

 

Here’s what stood out.

 

Most Shops Are Flying Blind — Until the Job Is Done

 

Casey put it plainly: before INNERGY, his team used QuickBooks and could see where they landed at the end of a project. But they never knew in real time where they were.

 

“We kind of just hoped for the best. We would do a guesstimate of cost to complete to see where we thought a project would end. And then at the end of the day we would evaluate it and say, was that a win or a loss?”

 

The problem with that approach: you can’t fix a job that’s already finished. And you can’t improve your estimates if there’s no reliable feedback loop from actuals back to your bidding process.

 

The shift for Casey came when he started tracking shop floor labor against work orders daily, so he could see estimate versus actual in real time — not after the fact.

 

 

The Three Numbers That Change Everything

 

The team walked through what INNERGY calls the foundation of operational data: three numbers at the work order level.

 

1. Revenue released — what you’re billing for that work order
2. Actual labor — what it actually cost you in hours and dollars
3. Actual material — what you spent on materials

 

These three numbers form what he called the “three-legged stool.” Together, they give you a real picture of job health and contribution margin. The industry benchmark for contribution margin in woodworking is 40%. If you’re not tracking these three at the work order level, you don’t have enough information to know whether you’re hitting it — or why you’re not.

 

For more mature shops, we introduced the concept of the “six column game” — adding work order number, project name, and description to the three financial columns above. It’s a simple structure, achievable in a spreadsheet, that creates an analytical foundation for better decisions down the road.

 

The Capacity Revelation at Mission Bell

 

One of the most compelling moments in the conversation came from Jason. His team made a significant shift in how they think about the business: away from chasing revenue numbers and toward filling capacity.

 

“We used to rely heavily on our revenue number. We were not focused on filling the plane to capacity.”

 

When Mission Bell started measuring labor utilization daily and weekly, something surprising happened: they had more capacity than they thought. And by managing work flow and work-in-process more intentionally, their effective capacity grew 30 to 50 percent — without adding headcount.

 

Casey added a detail worth sitting with: every percentage point of labor utilization above your estimate translates into real margin improvement. If you’re estimating jobs at 85% utilization and your team actually runs at 90%, the difference goes straight to contribution margin. “It’ll blow your mind,” Casey said. “And it’ll make you want to keep increasing that.”

 

Data Doesn’t Fix Itself: The Change Management Problem

 

Both operators were honest about the messiest part of getting here: getting consistent data into the system in the first place.

 

Casey’s approach at SDBA was direct. He started auditing specific data points daily — were employees clocking in to the right work orders? Were materials being job-costed correctly? And then he sent targeted messages to whoever was responsible when something was off.

 

“I would check that every single day and I would send a message, whether it was an @ or a chat, to whoever is responsible and say, ‘hey, this is off.'”

 

Over time, he built a simple scorecard for employees — a 1 or 0 each day for data accuracy — that was posted publicly each week. No one was called out by name, but everyone could see how the shop was performing. Timekeeping accuracy improved steadily until they reached a point where they actually trusted the data.

 

The principle he described: identify the problem, identify the data point that reveals it, audit that data until the behavior changes, then move to the next one. He started with about 20 audit points; he’s now back down to about five, with most of them automated.

 

Jason’s approach at Mission Bell centered on a daily labor utilization rate. The target: above 85%. The metric was tracked daily, reviewed weekly, and used to drive continuous process improvement. They also track Cost of Quality (COQ) — rework and remake — separately, so it doesn’t contaminate the productivity numbers.

 

What Data-Driven Decisions Actually Look Like

 

Both Jason and Casey shared examples of decisions that data made possible.

 

For Jason, the clearest example came at the end of 2024 and into early 2025. Looking at work on the books plus high-probability forecasted projects, Mission Bell could see they had a constraint coming in their finishing team. Not a gut feeling — visible data.

 

“We made the decision to spend a couple hundred thousand dollars in CapEx [on a new finishing booth and infrared cure ovens]. It’s paid off tremendously. Finishing has traditionally been a bottleneck in our organization. We’re in a place right now where we can turn things around in finishing within a day or two.”

 

That decision was made using three to five years of historical data plus the forward-looking work pipeline. Without that visibility, it would have been speculation.

 

For Casey, data has become a tool for continuous improvement at the process level. When labor hours started trending in the wrong direction, he compiled the data, identified the specific areas of the shop where efficiency was slipping, built a targeted improvement plan, and implemented it.

 

“Now we’re trending under estimated hours in those areas.”

 

He also used data to evaluate a capital equipment decision: variable-speed versus fixed-speed compressor. He ran the numbers using AI, factoring in runtime, location, and a 15-year horizon. It’s a small example, but it illustrates something important: when your data is clean and your processes are analytical, every decision gets better.

 

AI Amplifies Good Data (and Can’t Fix Bad Data)

 

Both operators have internal AI task forces. Both are building dashboards, automating workflows, and using AI to aggregate and analyze operational data faster than any manual process could.

 

Casey described an automated daily view for his warehouse team that surfaces every work order coming in the next two weeks, with materials and kitting requirements, refreshed at 5:00 AM every morning.

 

“They can’t even break it. If they delete it, the next day it’ll repopulate.”

 

Jason described layering AI over the data in INNERGY to get more precise views at both the job level and the business level.

 

But both were clear about the limits. “AI does not replace the human, and it definitely does not replace bad data. Hygiene still requires really good data and it still requires that creative, subject matter expertise to really make it all come to life.”

 

The principle is simple but easy to forget: AI is a multiplier. If your data is accurate and your processes are disciplined, AI gives you meaningful leverage. If the underlying data is a mess, AI gives you garbage faster.

 

Where to Start

 

The session closed with a practical framework for shops that are just beginning this journey.

 

Step one: Start with the three numbers. Revenue released, actual labor, and actual material — tracked at the work order level. Build that habit before you try to do anything more complex.

 

Step two: Move to the six column game. Add work order number, project name, and description to create a foundation for trend analysis and estimating improvement.

 

Step three: Find your sweet spot. Analyze which types of projects drive your best contribution margin per production hour. Identify what fits in that upper-right quadrant of right-fit, right-margin work — and get disciplined about pursuing more of it.

 

Casey’s advice for anyone starting from scratch: begin with job costing at the work order level.

 

“That gives you such rich data of where you’re succeeding, where you’re failing, where you need to basically know what you need to do.”

 

And his reminder on data hygiene: “Garbage in, garbage out. That’s easier said than done. But I would urge everybody to figure out a way to audit.”

 

The Bottom Line

 

Your shop produces data every single day. It lives in the hours your team clocks, the materials you purchase, the work orders you close. The question is whether you’re capturing it consistently, trusting it enough to act on it, and using it to make better decisions.

 

The shops that win long-term are the ones that treat that data as a business asset — not an afterthought.

 

If you want to see how INNERGY helps woodworking and millwork shops get control of their operational data, schedule a conversation with our team.

 

Casey Schlaegle is Owner of Schlaegle Design Build Associates in Pittsburgh, PA. Jason Neff is COO of Mission Bell Manufacturing in California.

Everyone can price the obvious part of rework. Recut material, extra labor, maybe a rush delivery to get replacement parts to the site. Those costs matter, but the mistake itself is usually cheap to fix.

 

The expensive part is the disruption. That was the core argument of our June 3 webinar with Matt Parisher, account executive at INNERGY, and Jennifer Giffin, Senior Developer, at Mission Bell Mfg, Inc.

 

The costs nobody tracks

 

When a problem comes back from the field, the whole operation reacts. Project managers start working the phones. Engineering stops what it’s working on to revisit what went wrong. Production gets reshuffled, the shop builds gaps into its day to remake parts, and installers lose productivity. Then comes the finger pointing, and even the people who didn’t make the mistake have to help fix it.

 

Jennifer has watched a measurement that was a fraction of an inch off snowball into thousands of dollars of rework and a halted project timeline. The remake was the cheap part. The lost momentum, the overtime, and the erosion of trust between the shop and engineering never show up on a cost report, but they all come out of throughput you could have spent making money.

 

Rework starts before anything gets cut

 

Most people assume rework starts in drafting or on the shop floor. Fabrication is usually where it gets discovered, but it’s not where it begins.

 

It begins when assumptions replace information. As Matt put it, the challenge isn’t complexity, it’s ambiguity. Complex projects don’t scare an experienced team. Missing information does, because when information is missing, someone instinctively fills in the blank. A designer assumes, an engineer assumes, an installer assumes. Every assumption adds rework risk.

 

Jennifer sees the pattern constantly: errors caught at install almost always trace back to faulty or incomplete field dimensions, often because someone worked from early architectural drawings instead of certified field measurements. The casework comes out too large, too small, or misaligned with the structure it has to live in.

 

Heroics don’t scale. Process does.

 

When an ugly project lands on a desk, it goes one of two ways. The reactive hero rolls up their sleeves and saves the job. We’ve all done it, and those people deserve credit. The problem is that heroics don’t scale.

 

The structured alternative is the engineering loop: clarify the intent, validate the information, choose the right tools, verify before release, and repeat it the same way every time. Full kit before you start. If you don’t have the information, don’t begin, because you’ll just have to reset your station and relearn the project later.

 

Mission Bell builds that discipline into physical practice. Before committing to hardware that will repeat across a job, they build a mock cabinet and test the locks and pulls against it. The cost of one mockup is nothing next to recutting every door on the project.

 

The same logic applies to Microvellum’s solid modeling tools. They’re excellent when the intent is clear and risky when it isn’t, so save them for after the full kit is assembled.

 

Get the knowledge out of Steve’s head

 

Every shop has an Ask Steve problem: the one person who knows everything. The problem isn’t Steve. The problem is what happens when Steve is gone.

 

Tribal knowledge, like always drawing three-quarter material that’s actually .71 because of the plywood you buy, belongs in SOPs, drawing templates, and your Microvellum library. A library isn’t just a product catalog that boxes you in. It’s a configurable record of your construction methods, tooling rules, and engineering decisions, so every project starts as close to finished as possible and nobody has to assume anything.

 

Revision control deserves the same rigor. The classic failure: the shop is building from revision A, drafting is working in revision B, and revision C is sitting in someone’s inbox until the parts are already cut. The change wasn’t the problem. The communication was.

 

The takeaway

 

Rework is a process problem before it’s a production problem. Successful shops aren’t perfect. They catch issues earlier by reducing assumptions and improving visibility between departments.

 

Asked for the single highest-impact improvement, Jennifer didn’t hesitate: shop drawings. Errors in the drafting phase are the largest driver of fabrication and installation mistakes, which means measure twice, cut once starts before the raw wood is even touched.

 

The goal isn’t to survive the chaos. It’s to stop normalizing it. To learn more about Microvellum and see the software in action, click here. 

 

By Marc Sanderson

Most companies use data to explain results. The best use it to change them.

 

High performers don’t just ask, “What happened?” They ask, “What’s coming next and what do we do about it? They use context, leading indicators and disciplined decisions create an edge.

 

When I ran a mid-size millwork company, I could look at one number at the first of the month and predict—with remarkable accuracy—where profitability would land at month end. If that number was running at 60-70 percent, I knew we were positioned to hit our target. If it dropped to 30 percent, we had work to do—and time to intervene, recalibrate and protect profitability before the month was lost. The ultimate indicator was percentage of work released to the shop floor.

 

It sounds simple, but early signals lead to better moves. That’s the advantage: not more data, but using it to outperform. Here how companies turn insight into a strategic weapon:

 

ADVANTAGE 1:

Compare to Compete

Think of a scoreboard: If your team scores three goals, is that good? You don’t know until you see the other side: Did the opponent score zero or four goals? Your team’s number doesn’t change, but the meaning does.
Business works in the same way. A 42 percent win rate, a $5-million backlog, or an average wage all sound informative—until you compare them. When grounded in industry benchmarks, your strategy, or your targets, they either signal strength or expose risk.

One company believed it was performing well until it benchmarked against peers using industry tools and surveys. The comparison revealed they were underprepared for a downturn and misaligned on pricing. That insight changed their decisions immediately.

Without context, data is noise. With it, data becomes direction. This is why INNERGY offers industry benchmark reporting on both financial and operational performance in real-time – for free. (You can check it out here).

Here’s a look at the three lenses of context most often applied in business:

 

 

Lens	What It Answers	When to Use It	What It Tells You	Risk If Used Alone
Industry Benchmarks	How do we compare to others?	Start here to build data discipline Sanity-checking performance Identifying gaps vs peers	Where you stand relative to the market	Can drive wrong decisions if your strategy is different
Company Targets (Internal Benchmarks)	Are we improving?	Tracking progress over time Driving accountability Measuring execution against goals	Whether you’re getting better and hitting your plan	Can create complacency if targets are too low
Strategy Alignment	Are we winning at the game we chose?	Differentiated positioning High-complexity or niche work Heavy pre-sale investment models	Whether your model is working as intended	Can justify poor performance if not grounded in reality

The real advantage is using them together:

• Start with industry to understand the baseline.
• Track against targets to drive ongoing improvement.
• Adjust for your strategic focus to ensure relevance.

Takeaway: Benchmark your top 3 metrics across industry, internal targets and your strategic focus—then change one decision this week based on what you learn.

 

ADVANTAGE 2:

Move Up the Maturity Curve

Organizations want to use data better, but what does that look like? That’s where the data maturity curve comes in. It’s a simple way to understand how organizations evolve in their use of data—from reporting what happened to actually shaping what happens next and the impact they can have.

At the initial level, companies track results. More advanced organizations diagnose why those results occurred. The best go further—predicting what’s likely to happen and prescribing what to do about it.

A shop floor team began using real-time data to monitor workflow across stations and quickly spotted a pattern—work orders were stacking at one point in the process. Instead of waiting for delays to show up in missed deadlines, they acted immediately. They reallocated labor to the bottleneck, adjusted upstream inputs and workflow before it became a problem. The result was clear: lead times dropped, throughput increased, and the team avoided the typical cycle of firefighting.

Most companies operate at stage 1 or 2, but the real advantage comes when they can reach stage 4:

Stage 1: Descriptive  →  What happened?

Stage 2: Diagnostic  → Why did it happen?

Stage 3: Predictive  → What will happen?
Stage 4: Prescriptive  → What should we do?

Descriptive data explains the past. Prescriptive data drives the future.

Takeaway: Pick one recurring problem, trace it from “what happened” to “why,” then define one leading signal to monitor and one action to take before it happens again—and review it weekly.

 

ADVANTAGE 3:
Shift from Lagging to Leading Indicators
How much time are you spending looking at lagging vs. leading indicators? In business, we naturally gravitate to lagging indicators, like EBITDA and revenue. Yet, these show history that you can’t change. Competitive advantage comes from leading indicators that show whether they’re on track and where to intervene.

A common example:

Rework or margin loss (lagging) → Percentage of work released to shop floor (leading)

When used correctly, leading indicators enable organizations to fix problems before they happen—turning unpredictable results into controlled outcomes. This is the leading indicator that predicted our profitability each month and I know it is the case for others.

At INNERGY, we have distilled down the “Perfect Release” to three elements: complete information, materials ready and time needed for the shop execute. When I survey CEOs and ask what happens if all three are consistently met,  64% say profitability would more than double. This shows the power of leading indicators—they don’t just improve operations, they make EBITDA more predictable.

Here’s a look at the leading indicators INNERGY’s insights have identified as the closest thing to a silver bullet in our industry and why they matter:

• Engineering Completeness at Release → Rework percentage, labor overruns
  Most downstream problems are rooted in the office and engineering is the choke point. Quality assurance becomes critical and allows you to prevent issues prior to fabrication.
• Material Readiness Before Production → On-time delivery, downtime
  Missing materials stop everything. This is one of the fastest ways to improve flow.
• Time for the Shop to Complete → Lead time, throughput
  This exposes bottlenecks early, before delays show up. It allows you to make sure the floor has the time needed to complete, in the midst of interruptions.

Start with these three and you will uncover leverage in the organization that you did not know existed.

As teams and organizations mature, they hone in on leading indicators more often and with greater depth. The result is that they can tell in the short-term—daily, weekly or monthly— what’s working and what needs attention.

Takeaway: Audit your dashboard this week—identify your top lagging metrics, then assign one leading indicator to each and start reviewing it daily with your team.

 

ADVANTAGE 4:
Make Data Your Identity
Culture is the multiplier. You can install systems to drive data, but high-performing organizations need to go further. They embed data into identity to make data a mindset and a part of the culture:

• Individuals know how they impact the bigger picture
• Teams understand how they’re performing
• Leaders act consistently on insights

Everyone needs to understand data and keep score so they can assess and improve. Without this, data becomes passive and decisions revert to instinct.

A mid-sized millwork company had the right systems in place, but results were inconsistent because data stayed in dashboards—not in daily decisions. They shifted to a simple mindset: everyone keeps score. Operators tracked labor vs. estimate, team leads reviewed performance daily, and leaders acted on issues in real time. Within months, labor overruns dropped, rework declined, and margins stabilized. The system didn’t change—the behavior did.

Takeaway: Make data everyone’s job—define the score, show the impact and act on it daily.

Time to Act
At scale, companies don’t compete on effort. They compete on clarity of information, speed of decision-making and alignment of execution. Data drives all three.

That leads to the ultimate truth: Two companies can have the same people, the same strategy and the same market. But the one with better data discipline will win. Not occasionally. Systematically.

Your 2026 IIBT Prep Checklist

 

The 2026 INNERGY Industry Benchmarking Tool is open, and the payoff is real: you’ll see exactly how your profit margins, labor costs, and business outlook compare to other shops across the industry.

 

The whole survey takes most people about 45 minutes, but only if you’ve got the right numbers in front of you before you start. Pull these together first and you’ll cruise right through it.

 

Section 1 — Your Company

The basics. Should take about two minutes.

 

✅ Company name, address, and contact info
✅ Your role
✅ Number of full-time employees (FTEs)
✅ Years in business
✅ Primary source of revenue
✅ Whether you’re an INNERGY ERP or Microvellum customer

 

Section 2 — Business Sentiment

No numbers here, just your honest read on the business. The CEO or owner answers a few dropdown questions about the past six months and the next six.

 

✅ Economic conditions
✅ Sales pipeline
✅ Cash flow
✅ Labor availability

 

Section 3 — Operations

A mix of numbers and quick selections. Pull these from your accounting system and estimating software:

 

✅ Bid win rate (by dollar value, past 12 months)
✅ Current backlog (contracted work not yet invoiced)
✅ Current AR and AP balances
✅ Total assets as of 12/31/25
✅ Total long-term debt

 

You’ll also answer a few quick questions about your software, what equipment you’re planning to buy, and where you want to improve in the next year. Dropdowns and checkboxes throughout.

 

Section 4 — Income Statement

This is the meatiest section. You need a full-year P&L from your accounting system. Here’s what to pull:

 

Revenue
✅ Plant-processed sales (casework, millwork, stairs, doors, panels, countertops)
✅ Non-plant sales (products you resell without touching on the floor)
✅ Installation sales

 

Direct Material
✅ Plant material cost
✅ Non-plant material cost

 

Labor — fully loaded (base pay + payroll taxes + benefits)
✅ Direct factory staff
✅ Installation wages (in-house and outsourced)
✅ Indirect factory staff (plant manager, purchasing, quality, maintenance)
✅ SG&A staff (CEO, estimators, sales, accounting, HR, admin, PMs, engineers)

 

Fixed Costs
✅ Other direct costs (bonding, insurance, sales tax, project general conditions)
✅ Overhead (rent, utilities, equipment leases, software, accounting fees, repairs)

 

Tip: The survey includes a position chart to help you sort each employee into the right bucket. If you don’t track benefits by department, just prorate your total benefits cost across the labor lines.

 

Section 5 — Workforce

Pull from payroll. For each role, you need headcount and average hourly rate:

 

✅ Estimator
✅ Engineer / Drafter
✅ Project Manager
✅ Machine Operator
✅ Journeyman / Custom Assembler
✅ Casegood Assembler / Production Worker
✅ Material Handler
✅ Finisher
✅ Installer
✅ Plant Manager / Foreman

 

You’ll also answer a few quick questions about turnover, what’s hardest to hire for, and what your team values most.

 

Tip: If payroll is outsourced, ask your provider for a headcount and average hourly wage report by role. If someone is salaried, divide their annual salary by 2,080 to get an hourly equivalent.

 

A few things worth knowing

 

This survey is administered by Inverra, an independent third party. INNERGY does not see your individual responses, only aggregated benchmark data. And everyone who completes the survey receives a $50 gift card from Inverra as a thank-you.

 

That’s the whole list. Once you’ve got these numbers gathered, the survey itself moves fast. And when results are published, you’ll have something most shop owners never get: a real, apples-to-apples look at how your business compares to the industry.

 

Take the 2026 IIBT →

By: Marc Sanderson

Most companies treat data like exhaust—something left over after the real work. That is backwards. The best companies run on data and it’s the difference between scaling and stalling.

 

In the late ’90s, I bought a woodworking company with only four computers—not one on my desk. It didn’t prevent us from understanding our data. We mapped the entire business on whiteboards: every form, every handoff, every step—all connected with string. What looked simple turned into a web.

 

The breakthrough wasn’t technology. It was visibility.

 

We followed a single data point—like a customer name—and saw it touch everything. That’s when it clicked. We weren’t just looking at information. We were seeing how the business actually worked—and where it broke.

 

That one exercise changed how our company thought about data, how our team used it day to day and quickly improved our processes. The result? We did not just meet industry benchmarks. We used data to outperform them.

 

Here’s where companies go wrong—and how to fix it:

 

Step 1: Measure What Actually Drives Outcomes 

Most leaders fall into the same trap: If data is valuable, more must be better. It’s not. I have learned this the hard way. Most companies today track too much—and trust too little.

The differentiator with data is not volume. It’s hierarchy. Recognizing the pecking order based on the DNA of your organization is essential to success.

Start with what directly drives profit:

 

1. Revenue accuracy at the work order level
   If what you sell doesn’t match what you build, nothing else matters.
2. Actual labor hours spent on that order
   This is where margin lives or dies.
3. Cost of materials used on that order
   This is the silent killer of profitability when unmanaged.

Forget budgets early on. Focus on actuals.

 

The lesson: Track a few key metrics that drive outcomes.

 

The Case: Cost of Tracking Everything

One company tracked a long list of data—machine utilization, micro-task timing  and dozens of operational metrics peppered across the organization. They could measure multiple parts of the process, but they trusted nothing. Why?

• Data entry became inconsistent.
• Teams didn’t understand why they were collecting it.
• Leaders couldn’t connect it to decisions.

Then, they scaled back to a handful of critical metrics—and performance improved. Clarity beats complexity.

 

Step 2: Use Data to Drive Decisions, Not Reports 

Data doesn’t create value. Decisions do. When looking at data at any level of an organization, the key question to ask is: What does this metric impact?

There are three layers at play in any given organization:

 

Layer	Typical Company	High-Performing Company
Data	Collects	Curates
Insight	Reports	Interprets
Action	Delayed	Immediate

 

The lesson: Data without action is useless.

 

The Case: Data-Rich, Action-Poor

A $40-million manufacturer had dashboards, reviews and an ERP system—yet margins slipped. The problem wasn’t access to information. It was action on the right data.

Then, they cut their metrics, focused on a few that mattered and pushed them to frontline teams. Teams reviewed daily and corrected issues mid-process, not after.

Within months, labor overruns dropped and margins stabilized. The shift was simple from reports to real-time decisions.

 

Step 3: Build Trust Through Discipline

Data integrity is rarely a systems problem. It’s a people problem. If team members do not understand why data matters, see how it’s used and trust leadership to act on it, then your data will degrade.

That’s why high-performers operationalize data:

• Operators input data daily
• Team leads review it daily
• Finance audits it weekly

This creates a feedback loop: Input → Review → Correction → Trust → Better Input

Break the loop and data decays.

 

The lesson: Audit data—or no one will trust it.

 

The Case: Reliability through Routine

A manufacturer invested in a new ERP system, expecting better visibility and control. Instead, leaders questioned every report. Numbers didn’t match what was happening on the floor, so teams defaulted back to gut decisions and spreadsheets.

The issue wasn’t the system—it was the lack of consistent auditing. Operators entered data inconsistently, supervisors reviewed it sporadically, and finance only checked it at month-end for their needs rather than for organization-wide, operational outcomes. Errors compounded and trust eroded.

Then, they implemented a simple discipline within the ERP system: daily input, daily review and weekly audits. Within a short time, discrepancies dropped and confidence in the data improved. Once leaders trusted the numbers, they started using them—and decisions got faster and better.

 

From Data to Control

Data is not a support function. It is the operating system of the business. When done right, it allows you to:

• Spot problems early
• Allocate resources precisely
• Eliminate hidden inefficiencies
• Scale without losing control

Without data discipline, growth breeds breakdown—more complexity, more noise, more bad decisions.

 

You can start taking steps today to build control—and see what I’ve seen with so many across the woodworking industry: faster execution, tighter control and more predictable performance.

 

What Comes Next

Control is only the beginning. At INNERGY, we’ve found the real advantage comes with tackling the harder question: Are you using data to actually outperform? Because the difference between average and elite isn’t better dashboards. It’s knowing what your numbers mean, what’s coming next and acting before everyone else does. In the next article, I will breakdown how to turn insights into advantage.

 

 

Marc Sanderson is the CEO of INNERGY and former President/Owner of Wilkie Sanderson. He began in the millwork industry 20 years ago with a small cabinet shop and built it into one of the most profitable operations in the country. A Harvard MBA and 2024 Wood Industry Market Leader, Marc is known for transforming complexity into clarity through data-driven strategy and culture first leadership. Equal parts analytical operator and motivator, he’s just as comfortable refining strategy as he is rallying a room around a shared vision.