Data Is the New Sawdust: How Smart Woodworking Shops Are Turning Operational Data Into a Competitive Edge
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.
- Revenue released — what you’re billing for that work order
- Actual labor — what it actually cost you in hours and dollars
- 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.