<strong>Illustrative example.</strong> This engagement is fictional and exists to show format and tone. Replace it with a real case study, with written client permission, or relabel this section before publishing.
Headline: Quote turnaround from six days to four hours At a glance: Precision components manufacturer · 240 staff · Sales Cloud + CPQ · 16 weeks
The problem
Every quote for a configurable assembly passed through one applications engineer. He knew which options were compatible, which combinations breached lead-time commitments, and where the real margin sat. Nothing was written down. Quotes took an average of six working days, and roughly one in nine went out with an error that had to be corrected after the customer had already seen it.
The commercial impact was measurable in lost deals. Distributors bidding on short-turnaround projects routinely went elsewhere because a competitor could quote same-day. Sales leadership estimated the cost, conservatively, at low single-digit millions of annual revenue — but couldn’t prove it, because unquoted enquiries were never logged.
An earlier attempt to fix this with a spreadsheet configurator had made things worse: four versions circulated, and pricing drifted from the ERP within a quarter.
What we found
- 640 sellable configurations reduced, on inspection, to 41 base products with 12 option families.
- Compatibility rules existed in three places: the engineer’s memory, a 2019 PDF, and a spreadsheet with broken formulas.
- Pricing lived correctly in the ERP but was manually re-keyed into quotes.
- 71% of approval requests were auto-approvable under the discount policy as written — they were queuing for no reason.
- No record of enquiries that never became quotes, so the biggest loss was invisible.
Product modelling — 41 base products with option families, compatibility and exclusion rules encoded as product and validation rules. Pricing — ERP as the source of truth, synced nightly plus on-demand, with contracted distributor rates applied by rule. Approvals — A matrix routing on margin threshold and lead-time exception. Anything inside policy auto-approves; only genuine exceptions reach a human. Documents — Branded quote generation from the record, with the technical configuration summary the engineer used to write by hand. Enquiry capture — Lightweight intake so unquoted enquiries finally became data.
01 · Weeks 1–3 — Rule extraction. Sessions with the applications engineer, two sales managers and the commercial director. The genuinely hard work: converting tacit knowledge into rules, including 23 exceptions the team initially insisted didn’t exist. 02 · Weeks 4–6 — Catalogue build. Product and option modelling, compatibility rules, first configurator prototype. 03 · Weeks 7–11 — Pricing and approvals. ERP integration contract, price sync, discount schedules, approval matrix, quote templates. 04 · Weeks 12–13 — Validation. 200 historical quotes re-run through the configurator and compared against what was actually sent. 11 discrepancies; nine were errors in the original quote. 05 · Weeks 14–15 — Enablement. Role training for sales and distributor-facing staff, plus a short session for the engineer, whose role shifted to rule governance. 06 · Week 16 — Launch and hypercare.
Results (90 days post-launch)
| Metric | Before | After |
|---|---|---|
| Median quote turnaround | 6.2 days | 4.1 hours |
| Quotes requiring correction | 11% | 1.4% |
| Approvals requiring human review | 100% | 29% |
| Quotes issued per month | 148 | 226 |
| Enquiries logged but not quoted | not measured | 63/month (now visible) |
The single-point dependency was removed: the applications engineer now maintains the rules instead of executing them, and took two weeks’ leave in the quarter after launch for the first time in three years.
What we’d do differently
We under-scoped rule extraction by about a fortnight. The exceptions surfaced steadily rather than in one workshop, and we’d now plan a longer discovery with a written exception log from day one.
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