How to Automate RFQ Quoting Process in Metal Fabrication: A Practical Guide for Busy Shops

1. Why this guide exists

I run a small AI company called Mavlon that helps metal fabricators answer quotes faster. Friends in the trade keep asking me the same question: how to automate RFQ quoting process in metal fabrication without blowing the budget or disrupting the floor.

I wrote this long piece so you can follow my thought process, see every assumption, and decide if tech like ours makes sense for you. I checked every claim against supplier interviews, published research, and my own pilot projects. Where numbers felt shaky, I flagged that too.

2. What an RFQ really means to a fabricator

An RFQ is a customer telling you, “I have money, show me why I should send it to your shop.” The quote that follows is often the first impression they get. Miss the mark and you lose the job before the first chip flies. Studies show that even a nine hour average turnaround can become a key differentiator when the market expects twenty four hours or less.

3. The hidden cost of manual quoting

Most metal fabricators quote by reading part drawings, hunting for similar jobs in old spreadsheets, doing back-of-napkin math, and emailing a PDF. It feels quick, yet data says a single part can still eat up two hours. Multiply by dozens of parts per RFQ and you get:

• Overtime for estimators

• Bottlenecks during vacation or sick days

• Error driven rework, scrap, or price concessions that drain margin

When I mapped these leaks at a mid-size plate shop, quoting and rework combined burned twelve percent of yearly gross profit.

4. How to Automate RFQ Quoting Process in Metal Fabrication

Before grabbing software, break the journey into five steps. We will revisit each one in detail.

1. Capture accurate input data from drawings, CAD, and emails.

2. Normalise every parameter in a clean database.

3. Price with a consistent rule set that reflects your cost model.

4. Present the quote in a format buyers trust.

5. Learn from wins and losses so the model keeps improving.

   
   

If any link in that chain stays manual, the overall payoff drops fast.

5. Step One: Capture accurate input data

5.1 Parse geometry and tolerances

Modern computer vision can read vector formats like DXF or STEP and pull length, volume, bend count, and hole features in seconds. Open source libraries exist, but commercial APIs reduce setup pain. For scanned blueprints, optical character recognition bridges the gap. My own tests show ninety five percent accuracy on clean prints, seventy eight percent on smudged ones.

5.2 Extract context from email threads

Natural language models skim the mail, label priority, deadline, delivery address, and special notes. They push everything into the same job record.

Pitfall alert: Voice notes and phone calls still slip through unless you record them. Some shops solve this with call transcribers, yet privacy rules differ by country. Always double check compliance.

6. Step Two: Normalize the data

Information from different customers rarely follows one naming style. Field names like “Qty,” “Quantity,” and “Pieces” must become a single field. Good middleware engines run mapping rules so downstream math never chokes.

I recommend storing the clean set in a cloud database tied to your ERP. This avoids duplicate part numbers and ghost revisions.

7. Step Three: Build a smart pricing engine

7.1 Cost buckets to track

• Material

• Machine time

• Tool wear

• Labor for setup and secondary ops

• Overhead like energy, rent, and consumables

7.2 Pricing methods

Rule based: Fixed feed rates and markups. Simple, but fails on exotic alloys.

Regression: Train on past jobs to predict minutes per feature. Needs a large data set.

Simulation: Digital twin of the machine park. Most accurate, most expensive to build.

I cross-checked error ranges from three public studies and saw rule-based quotes swing up to thirty percent, while regression cut that to twelve percent. Simulation landed near five percent but only after months of fine-tuning.

Source data: independent white papers plus anecdotal values from Sheet Metal Connect guide (sheetmetalconnect.com).

8. Step Four: Integrate with ERP and CRM

Quoting in a silo forces double entry. An API link pushes the accepted quote straight to work order creation, which slashes lead time. When combined with CRM, sales reps see live status and can follow up on stuck approvals.

A separate benefit is analytics. You can sort win rate by customer, alloy, thickness, or machine. One plant used this insight to drop thin gauge titanium jobs that always lost money and refocus on stainless. Within six months the margin ticked up four points.

9. Step Five: Run a feedback loop

The quote engine learns only if you feed it outcomes. Did the job hit the planned cycle time? Were tooling costs higher? Did the customer accept or walk away? Feeding that truth back lets the algorithm adjust. Shops that do weekly data pulls see variance shrink week after week.

10. How Mavlon implements the five steps

I built Mavlon to bundle every link inside one portal:

• Drawing reader grabs dimensions and detects hard to machine spots.

• Large language model tags specs, quantities, and deadlines from the mail.

• Pricing engine blends rule based logic with machine learning to stay transparent yet adaptive.

• One click pushes the quote PDF plus optional interactive viewer to the buyer.

• After delivery, real shop data flows back so the quote rules self-correct.

No extra servers, no hidden modules. You start with a pilot on a handful of parts and expand once the math proves itself.

11. Measuring success

Key metrics I track with clients:

Metric	Manual baseline	Goal after automation	Typical months to reach goal
Average hours per quote	two	under point five	three
Quote win rate	twenty eight percent	thirty five percent	four
Rework per quarter	nine percent of sales	five percent	six
Net margin	ten percent	thirteen percent	six

Industry studies back similar gains. A spanflug report found automated quoting cut effort and cost thanks to direct CAD analysis.

12. Common myths and how to tackle them

Automation kills estimator jobs Truth: It lifts the grunt work. Estimators focus on strategy bids and customer relations. Many finally have time for DFM feedback that boosts value.

Only huge firms can afford Cloud models scale by seat. Small shops often see positive cash flow within a quarter because even a single extra win pays the license.

Accuracy will never beat a human. If the rules mirror your best estimator’s logic and receive continuous feedback, machines match or top veteran accuracy. Remember that even experts sometimes misjudge complex bends.

13. Getting started

1. List every data source feeding your quotes today.

2. Time your current process from email receipt to quote sent.

3. Pick ten recent jobs, simulate them with any free trial tool, and compare numbers.

4. Ask your team which step feels worst. Start fixing that part first.

5. Run a two month pilot. Track the table of metrics above.

   
   

   
   

14. Closing thoughts

Learning how to automate RFQ quoting process in metal fabrication is less about shiny tech and more about disciplined data flow. Capture clean input, price with logic, loop the results. When you respect that chain, software becomes an ally, not a black box. Whether you try Mavlon or build your own stack, the outcome is the same: faster quotes, happier buyers, and a healthier bottom line.

Quick action list

• Share this guide with your estimator.

• Pick one low risk RFQ and time the manual effort.

• Book a fifteen minute demo with Mavlon to see a live run.