RFQs arrive in bursts and every delay compounds. Missing fields trigger back‑and‑forth emails, PDFs need manual interpretation, and the quote ends up in spreadsheets or ERP screens. That is slow, error‑prone, and hard to scale.
Why it matters for decision makers
Quote speed isn’t just operational—it directly impacts win rates and revenue predictability. When your response is slow, you lose opportunities to faster competitors. When your response is rushed, errors slip in and margins erode. That’s a double loss: missed deals and painful rework on the deals you do win.
A common reality: manual quoting for complex orders can take weeks because data is scattered across emails, PDFs, and spreadsheets. (aPriori, 2025) That is time you can’t bill and a bottleneck you can’t scale.
What changes with automation
Automation doesn’t replace sales or engineering. It pre‑drafts a quote from unstructured RFQ data, validates key parameters against ERP/CRM rules, and flags exceptions for expert review. The goal is speed + quality, not reckless automation.
Example workflow (in practice)
- Ingest RFQ emails and attachments (PDF, Excel, specs).
- Extract structured fields (customer, part, quantity, delivery, tolerances).
- Validate against ERP/CRM pricing rules and approved materials.
- Draft a quote with calculated pricing and lead times.
- Flag exceptions (missing MPNs, out-of-range tolerances, special tooling).
- Review & approve by sales/engineering before sending.
Expected impact
You should expect tangible wins within one quarter:
- 30–50% faster quote turnaround for standard RFQs.
- Fewer pricing errors (rule-based validation catches mistakes early).
- More quotes handled per week without hiring.
- Higher win rate from faster response times.
Implementation path (4–6 weeks)
Week 1: Process mapping & data access
- Identify RFQ intake channels (email inboxes, portals).
- Map required fields and approval thresholds.
- Clarify ERP/CRM rules and pricing logic.
Weeks 2–3: Extraction + validation
- Build document ingestion and parsing.
- Normalize fields and validate against ERP rules.
- Define exception categories.
Weeks 4–5: Drafting + review loop
- Generate quote drafts with pricing and delivery.
- Route exceptions to sales/engineering.
- Capture feedback to improve rules.
Week 6: Rollout + training
- Pilot with a subset of RFQs.
- Train team on review workflow.
- Measure cycle time and accuracy improvements.
What can go wrong (and how to avoid it)
- Incomplete RFQs: Most incoming requests lack key details. Mitigation: auto‑request missing data and flag unknowns.
- Edge‑case pricing: Custom tooling or unusual tolerances can break rules. Mitigation: route to expert approval.
- Data quality in ERP: Inconsistent master data undermines automation. Mitigation: add lightweight validation + correction loop.
FAQ
Will this replace our sales team? No. It speeds up the draft and removes data entry. Sales and engineering still approve and own the relationship.
How fast can we see impact? Most pilots show measurable improvements within 4–6 weeks.
What if our RFQs are messy? That’s the norm. The system is designed to extract partial data, flag gaps, and auto‑request missing fields.
If quoting is your bottleneck, we can map one RFQ flow and show the impact in weeks—not quarters.
Source: aPriori — A Guide to Responding Faster to Manufacturing Customer RFQs