Stop Quoting Based on Gut Feel

You know the drill. A new RFQ lands in your inbox. You open the STEP file, spin the model, glance at the material, and a number pops into your head. “Call it $450 and 3 days.” You hit send.

Two weeks later the job ships. Actual cost: $620. Actual time: 5 days. The margin you thought you had? Gone.

This isn’t bad estimating. It’s estimating without a feedback loop. And it’s how most digital manufacturers still operate — even shops running modern equipment and solid CAM.

Why Gut Feel Persists

Quoting on experience feels fast. You’ve made thousands of parts. You know what a 5-axis setup costs. You know SLS nylon pricing. The problem isn’t your knowledge — it’s that your knowledge isn’t calibrated.

Every shop has “that one job” that looked simple but ate three extra setups. Or the nested sheet metal job where material utilization dropped 18% because of an odd geometry. Those outliers rewrite your margins, but they rarely rewrite your mental model.

Without systematic capture of actual vs. estimated, your next quote carries the same blind spots.

What a Feedback Loop Actually Looks Like

A quoting feedback loop has three components:

• Capture estimates at quote time — machine hours, setup count, material weight, post-processing steps, outsourced processes.
• Capture actuals at job close — same categories, same granularity.
• Compare and adjust — automatically, not in a spreadsheet you update quarterly.

When the delta between estimated and actual machine time on 3-axis work consistently runs +22%, your next 3-axis quote should reflect that. Not because you remember. Because the system applies it.

Where the Data Lives (And Why It’s Fragmented)

Most shops have the data. It’s just scattered:

• CAM software knows programmed cycle time.
• Machine monitoring knows actual spindle time.
• ERP knows material issued and labor booked.
• Shipping knows when it left the dock.
• The estimator’s spreadsheet knows what was quoted.

None of these talk to each other. The quote lives in one system. The reality lives in three others. Closing that gap doesn’t require a new ERP — it requires a quoting engine that ingests actuals from the systems you already run.

Building the Loop Without Drowning in Admin

You don’t need operators clocking in/out per operation. You don’t need a data scientist. You need:

1. Structured quote inputs — not a single price field. Break quotes into machine time, setup, material, post-process, outside services.
2. Automatic actuals collection — API pulls from machine monitoring, ERP labor, shipping timestamps.
3. Variance reporting by category — not just “job margin” but “setup variance on 5-axis” or “material utilization on nested sheet.”
4. Rule-based adjustment — when variance exceeds threshold, the quoting engine suggests (or applies) a correction factor for that process/material combo.

This turns every shipped job into a calibration event for the next quote.

What Changes When Quotes Are Calibrated

Shops that close the loop see three shifts:

• Fewer surprise losses. The jobs that used to bleed margin now either quote higher or get flagged for review before you send.
• Faster quoting. When the engine carries validated rates, estimators stop re-deriving them each time. The “gut check” becomes a sanity check, not the primary calculation.
• Confident pricing on new work. You can quote a process you’ve run 50 times with data-backed rates, not hope.

The estimator’s experience still matters — it guides DFM feedback, process selection, and exception handling. But it stops being the pricing engine.

Start With One Process

Don’t boil the ocean. Pick your highest-volume process — maybe 3-axis milling or MJF printing. Instrument the quote-to-actual path for just that process. Capture estimated vs. actual machine hours for 20 jobs. Calculate the variance. Apply a correction factor to the quoting engine.

Then move to the next process.

Within a quarter, your quotes reflect your shop’s reality, not your memory of it.

How Solvi Helps

Solvi’s quoting engine is built on structured, process-level inputs — not lump sums. It captures estimates by category, integrates with your MES and machine data to pull actuals, and surfaces variance by process so your rates stay calibrated automatically. The result: quotes that reflect what your shop actually costs, not what you hope it costs. See how it works.