Latest / Scaling Up with Fexingo: How Small Businesses Become Mid-Market Companies / How a Machine Shop Scaled Past 15 Million by Fixing Job Quotes
Transcript
- Lucas: So Dana Whitfield runs a precision machine shop outside Cleveland, and in 2022 she almost doubled her revenue and made almost no money doing it. Luna: That is the classic 'growth trap' — more revenue, same profit. What happened? Lucas: Right. She was bidding on everything. Her quote process was basically a gut-feel number based on what she thought the market would bear. She'd win a ton of work, and then the shop floor would eat her alive on setups and tolerances she didn't price for. Luna: So the fix was... pricing? Or was it something deeper? Lucas: It was both, actually. She rebuilt her quoting from scratch. And the core change was this: she started pricing every job based on what it actually cost to run on her machines, not what the customer might pay. Luna: That sounds obvious, but I bet it's rare. Lucas: In small manufacturing, it's almost the norm to guess. Dana told me she used to estimate based on 'how many hours I think this will take' and then multiply by a vague rate. She never tracked actual cycle times against her estimates. So she was flying blind. Luna: And when she started tracking, what did she learn? Lucas: She learned that her 'standard' rates were off by as much as 40 percent on certain jobs. Small batches with tight tolerances were eating up way more setup time than she ever estimated. And she was winning those jobs because her price was too low. Luna: So she was winning work that was losing money. That's the worst kind of win. Lucas: Exactly. So she built a job-costing template. Every quote now goes through a standardized sheet that breaks down setup hours, cycle time per part, material cost, tooling wear, and a risk factor for tolerance complexity. It takes her about 30 minutes per quote, but it forces her to think like her shop floor. Luna: Thirty minutes per quote — does that slow down her turnaround? Lucas: She made a rule: quote within 24 hours, no exceptions. Because in this industry, if you take three days to quote, the customer's already moved on. So she's faster and more accurate at the same time. Luna: But what about the jobs that still look attractive on the surface — the ones that are actually bad fit? Lucas: That's where the 'kill criteria' list comes in. Dana wrote down the five types of jobs that had historically lost her money. High tolerance, low volume, weird materials, customers who always push back on price, and jobs that require a new setup that eats a whole day. If a quote hits any of those, she either prices it at a premium or walks away. Luna: Walking away is hard for a small shop. You feel like every job is your only job. Lucas: It is. And Dana admits she lost two customers in the first year. But what happened was that her win rate actually went up on the jobs she did chase, because she was quoting realistic numbers and the customers who stuck around were serious about quality. Luna: So the revenue dip was temporary? Lucas: It wasn't even a dip, actually. She turned away maybe $2 million in bad-fit work, but she replaced it with better-fit jobs at higher margins. So revenue still grew from $8 million to $15 million over three years, and her profit margin went from single digits to around 18 percent. Luna: That's a massive jump. So the real lesson is: stop measuring success by revenue, start measuring by profit per machine hour. Lucas: That's exactly the metric she started tracking. Profit per machine hour. She knew which machines were her bread and butter, and she started quoting jobs to fill those machines first. Luna: How did she get her team on board? Because the shop floor probably hated the new paperwork. Lucas: She didn't introduce it as extra work. She sat down with her lead machinist and said, 'Help me build this template so I'm not pricing your work wrong.' And that actually got him invested, because he'd been frustrated for years that his jobs were under-priced. Luna: That's smart. You're turning the person who knows the actual constraints into your pricing partner. Lucas: Exactly. And she did the same with her sales guy. He was compensated on gross revenue, so he was incentivized to bring in any job. She changed his comp to be based on gross profit, and suddenly he was on board with the kill criteria too. Luna: That's a key move. Incentives drive behavior. So many small businesses forget that. Lucas: Right. And speaking of incentives, this show is powered by listeners like you. If you've gotten value from these conversations — even if it's just one idea that moves your business forward — you can support us at buy me a coffee dot com slash fexingo. It's a simple way to keep this show ad-free and independent. Luna: Yeah, it really does help us keep doing deep dives like this one. And it's just a coffee, not a subscription. Lucas: So back to Dana — the other thing she did was she started quoting faster than her competitors. She set that 24-hour rule, and she told me it became a selling point. Customers would tell her, 'Your quote was in my inbox before the other shop even returned my call.' Luna: That's a competitive edge you can't buy. So what's the takeaway for someone who runs a service business — say a marketing agency or a home contractor? Lucas: The same framework applies. First, know your true cost per hour — including your own time, overhead, and the pain-in-the-neck factor. Second, write down the types of projects that have historically lost you money, and create a kill list. Third, price based on profit per unit of your scarce resource — for an agency, that might be profit per account manager hour. Luna: And the 24-hour turnaround rule — that probably works in any industry. Lucas: Absolutely. Speed is a signal of competence. Dana's quoting process now takes 30 minutes, and she's able to do it while other owners are still digging through spreadsheets. It's not just about the math — it's about building a system that makes you faster and smarter. Luna: I love that. So the real story here isn't about machining at all — it's about using data to say no to the wrong work.