Most shop owners believe slow quotes lose jobs, but few can prove it. RFQs come in faster than the estimating bench can return them, the best ones get a quote eventually, and nobody measures how eventually - or whether the slow ones lose. This dashboard tracks the RFQ-to-quote pipeline: how many RFQs come in, how many get a quote back, how fast, by which estimator, and whether faster quotes win more.
What this dashboard answers
It answers "are we quoting fast enough, and is it costing us?" The scorecard shows RFQs Received, Quotes Sent, Quote Response Rate % (the share of RFQs that even get a quote back), Same-Week Quote % (returned within five business days), and total turnaround days paired with quotes sent so you can read average days per quote. A second page directly relates turnaround speed to win rate - the chart that settles the "do fast quotes win?" argument.
The metrics that matter
Response rate is the first leak: RFQs that never get quoted are lost before pricing ever enters the picture, often because the estimating bench is swamped. Same-Week Quote % is the speed read most customers actually feel. The Win-Rate-by-turnaround-bucket bar is the payoff metric - if win rate falls as turnaround stretches, you've quantified the cost of slow quoting, and the per-estimator views show where the throughput constraint sits.
Why this data is trapped
The RFQ-in and quote-out timestamps live in the quoting module and the inbox; turnaround is the difference between them, by estimator, which the ERP - JobBOSS (E2 Shoptech), ProShop ERP, Global Shop Solutions, or ECI M1 / Epicor - stores as fields but never reports as a throughput-and-speed analysis. The link between turnaround and win outcome requires joining quote timing to quote results, the kind of join that only ever happens in a one-off spreadsheet.
How to read it
If Quote Response Rate % is well under 100%, your first problem isn't price - it's that RFQs are dying on the bench, and the fix is estimating capacity or triage, not discounting. Read Same-Week Quote % by estimator to find the bottleneck, then read the Win-Rate-by-turnaround-bucket chart: a clear decline as days stretch is hard evidence that speed is costing orders, and it justifies the estimating investment the win/loss anecdotes never could.
The sample uses realistic synthetic RFQ and quote-timing data - no real customers or estimators.
Metrics it tracks
| Metric | What it means |
|---|---|
| RFQs Received | Number of incoming RFQs in the period. |
| Quotes Sent | RFQs that actually got a quote returned. |
| Quote Response Rate % | Quotes sent divided by RFQs received - share of incoming RFQs that got a quote back. |
| Same-Week Quote % | Share of sent quotes returned within five business days. |
| Total Turnaround Days | Sum of turnaround days over quoted RFQs - pair with Quotes Sent to read average days per quote. |
| Quotes Won | RFQs that converted to an order. |
Used by: Owners and estimating managers who suspect slow quoting costs orders
Frequently asked questions
Does quoting speed actually affect win rate?
Often, yes - and this report is built to test it directly with the win-rate-by-turnaround-bucket chart. When win rate declines as turnaround stretches, you've quantified the cost of slow quoting in lost orders, which is the evidence that justifies adding estimating capacity.
What is quote response rate?
It's the share of incoming RFQs that actually get a quote returned. A response rate well under 100% means RFQs are dying on the estimating bench before pricing is ever decided - a throughput and triage problem, not a pricing one, and usually a bigger leak than the win rate itself.
Can I build this on my own RFQ data?
Yes. Export your RFQs with received and quoted dates, the estimator, and the outcome, then use this as a template. We model it into a Power BI report. The sample is fully synthetic, so there's no real customer or estimator data in what you see here.
Build this report on your own data
Clone this Machine / job shops template — describe it and we’ll generate sample data so you can try it free, or upload your own export. You get a fully modeled, branded Power BI project that opens in Power BI Desktop.
Use this as a template →