RFQ automation for distributors: a practical guide

What does RFQ automation actually automate?

A distributor's RFQ process usually has six steps: receive the request, read it, figure out which part each line refers to, look up price and lead time, build the quote document, and send it. Only the last step requires judgment about the customer. The first five are repetitive, and that is where automation helps.

The hard part is step three. Customers rarely use your part numbers. They write "1/2 inch hex bolts grade 8, zinc", paste a competitor's code, or attach a spreadsheet with their own internal SKUs. Good RFQ automation handles that messy matching, and when it is not sure, it says so instead of guessing.

Which approaches exist, and where does each fit?

There is no single right tool. The table below compares the common approaches in general terms, so you can see which one matches your volume and catalog.

General comparison of categories. For named vendors, see our comparison of AI quoting tools.

Worked example: what 40 RFQs a week looks like

Take an illustrative distributor receiving 40 RFQs a week with an average of 10 lines each. That is 40 × 10 = 400 lines a week. If a rep spends about 2 minutes per line finding the part, checking the price break and typing it in, manual quoting costs 400 × 2 = 800 minutes, or about 13.3 hours a week.

Now assume, for illustration, that automation matches 85% of lines confidently and flags the other 15%. That is 400 × 0.85 = 340 matched lines and 400 × 0.15 = 60 flagged lines. Reviewing a matched line takes about 15 seconds (0.25 minutes), so 340 × 0.25 = 85 minutes. Resolving a flagged line still takes the full 2 minutes, so 60 × 2 = 120 minutes. Total: 85 + 120 = 205 minutes, about 3.4 hours.

The saving is 800 − 205 = 595 minutes, close to 10 hours a week. The match rate on your catalog will differ, which is exactly why you test on your own RFQs before deciding.

What should you prepare before automating?

• A price sheet you trust: part number, description, unit, quantity breaks, lead time. Excel or CSV is fine; see Excel price sheets.
• Customer-specific pricing in the same file or a second one, keyed by customer.
• One mailbox where RFQs arrive, including web-form notifications.
• Approval rules: the quote value, customer type or stock situation that should always get a second look.
• A baseline: your current median response time, so the improvement is measurable.

How do you keep control of pricing?

Insist on a clear split between what the AI decides and what is calculated. In Merlin's Quote Desk, the AI only chooses which catalog item matches a request line. Unit prices, quantity breaks, customer-specific prices, totals and lead times are calculated in code from your price sheet, so the model cannot invent a price.

Unclear, unmatched or missing-spec lines are flagged for a rep, never guessed. Quotes above an amount you set, first-time customers and low-stock items are flagged too. A human approves every quote before it is sent, and every draft, approval and send is logged.

How do you roll it out without disrupting the team?

1. Shadow mode for 1 to 2 weeks: drafts are created on real RFQs but nothing can be sent. Reps compare drafts with what they actually sent.
2. Fix what shadow mode reveals: missing items in the price sheet, outdated lead times, recurring customer wording that becomes learned aliases.
3. Pilot on one workflow for 30 days with one written success metric.
4. Add quote follow-up, order status and document requests once quoting is stable.

What does it cost?

Pricing models vary widely across vendors, from per-user seats to usage credits. Merlin uses flat monthly plans with no usage credits or overages, after a $3,500 pilot on the Quote Desk. Details are on the pricing page. If you serve distributors in a specific vertical, our page for industrial distributors covers typical RFQ patterns.

How do you measure whether RFQ automation is working?

Pick a short list of numbers and look at them every week. Keep the definitions fixed so the trend is honest. Our post on quote turnaround KPIs goes deeper.

• Median quote response time: from RFQ received to quote sent. This is the headline metric.
• Share of quotes sent within 24 hours: buyer expectations cluster around one day. In a 2020 Paperless Parts survey, 67% of part buyers said they expect a quote within 24 hours.
• Flag rate: the share of lines a rep has to resolve. It should fall as learned aliases build up.
• Edit rate: how often reps change a matched line before approving. High edit rates point to price sheet gaps.
• Quote-to-order rate: whether faster quotes are winning more business. Measure it over months, not days.