Quoting at industrial and B2B suppliers is slow because the information a quote needs lives in three or four places, and a person has to carry it between them by hand. You speed it up by automating that assembly (pulling price, stock, lead time and approval into one draft) while a rep still reviews and sends every quote. The spreadsheets and systems can stay exactly where they are.
How do you speed up quoting at an industrial supplier?
Start by separating the two jobs hidden inside every quote. One is judgement: whether this customer deserves a sharper price, whether the lead time is realistic, whether the spec makes sense. The other is assembly: finding the right price row, checking stock, confirming the lead time with planning, getting a manager to approve a discount, and pasting it all into a template. Judgement is where your rep earns their salary, and assembly is where the days go.
Most suppliers try to speed up quoting by asking reps to work faster or by buying a new CPQ platform. The first doesn’t work because the rep isn’t the bottleneck, the waiting is. The second often stalls because it asks the business to migrate its pricing logic before anyone has seen a faster quote. The quicker path is to leave the sources alone and automate the assembly between them.
Why quoting takes two weeks when the work takes two hours
Walk through a typical request. A customer emails asking for 400 units of a machined part with delivery in six weeks. The rep opens the price list in Excel or Google Sheets and finds the base price and the volume break. Stock and lead time live in the ERP (TOTVS, Omie or Bling in Brazil, MYOB or a manufacturing add-on in Australia), so the rep either logs in or asks someone in planning. If the price falls below a margin threshold, a manager has to approve it by email. Then the rep copies everything into a Word or PDF template and sends it.
None of those steps is hard, and added up the hands-on work might be an hour or two. The problem is that each handoff waits in someone’s inbox. If planning answers the next morning, the manager approves two days later and the rep is juggling twenty other requests, a quote that needed two hours of work can easily arrive ten working days after the request. That’s a worked example rather than a measurement, but anyone who has run sales at a supplier will recognise the shape: the delay is queue time between handoffs, not processing time.
That distinction tells you where automation pays off. Speeding up any single step barely moves the total, while removing the handoffs moves almost all of it.
What an automated quoting path looks like
The rebuilt path has four parts, and none of them requires replacing your pricing sheet or your ERP.
- Intake. Quote requests arrive in one place, whether that’s a shared inbox, a web form or WhatsApp. A model such as Claude or ChatGPT reads the request, extracts the part numbers, quantities, delivery date and customer, and writes them into a structured record in your CRM (HubSpot or Pipedrive) or a Google Sheet.
- Lookup. A workflow in n8n, Make or Zapier takes that record, queries the price list for the base price and volume break, and queries the ERP for stock on hand and current lead time. Where the ERP has no usable API, a scheduled export into a sheet is good enough to start.
- Rules. The workflow applies the rules your team already carries in their heads, such as a minimum margin, which customers get contract pricing, or the fact that any discount above 5% needs sign off. Anything inside the rules goes straight to draft, and anything outside them goes to a manager as a one-click approval in Slack or email with the numbers already laid out.
- Draft. The system fills your existing quote template and puts it in the rep’s queue with the sources visible, so they can see where every number came from.
The rep then does the part only they can do. They check the draft, adjust anything that needs judgement, and send it.
The slow part of quoting is rarely the decision. It’s carrying information between systems that were never connected.
Why a person still reviews and sends every quote
It’s tempting to let the system send quotes on its own once the drafts look good, and we’d advise against it, at least at first. A quote is a commitment on price and delivery, and the cost of one wrong number (a stale lead time, a missed freight charge, a customer on special terms) is far higher than the few minutes a review takes. The review also keeps the rep close to the customer, which is where the next order comes from.
Keeping a person in the loop has a second benefit. Every time the rep edits a draft, that edit shows you where the rules are incomplete. Log the changes for a few weeks and you’ll have a clear list of what to fix in the logic, which is a far better guide than guessing up front.
Where to start this week
Pull the last twenty quotes your team sent and, for each one, write down when the request arrived, when the quote went out, and every person or system it touched in between. You’ll usually find that two or three handoffs account for most of the waiting. Pick the one that appears most often (frequently the stock and lead time check) and automate that single lookup first. If you want a second pair of eyes on the map, that’s the kind of work JARA does, but the exercise is useful on its own.
FAQ
Do we need to replace our ERP or pricing spreadsheet to automate quoting?
No. The automation sits between the systems you already have and reads from them. A clean API makes it easier, but a scheduled export from the ERP into a sheet is enough to build and test the first version.
Is a CPQ platform a better option than building a workflow?
A CPQ tool makes sense for complex configurable products with large catalogues and a team that can maintain it. For most small and mid-sized suppliers, a workflow over the existing price list delivers faster quotes sooner and with less change to how the team works.
Can AI set prices on its own?
It shouldn’t. Use AI to read requests and assemble drafts, and keep pricing in explicit rules your team agrees on. The model extracts and organises information, while people and rules decide the number.
How long does it take to build a first version?
A focused first version covering intake, price lookup and drafting for your most common product lines is a project measured in weeks rather than months, because it reuses the sources you already have. The scope then grows based on what the reps edit most.