AI for quote-to-cash in manufacturing and job shop ERPs

For a job shop, contract manufacturer, or EMS provider, the RFQ inbox is where growth and margin risk both start. A new RFQ arrives with a drawing, a quantity, sometimes a target price, and someone on the estimating team has to figure out whether this part resembles something the shop has quoted or run before, what it should cost to make, and what price wins the business without giving away margin. On a good day, that estimator has institutional memory of a similar job from eighteen months ago. On a busy day, with RFQs stacking up, that memory does not get consulted, and the quote either takes too long to turn around or gets built from a generic rate instead of the shop's actual cost history.

The frustrating part is that the data to make a fast, accurate quote almost always already exists in the ERP. Job cost history has the actual labor, material, and overhead cost for every comparable part the shop has run. Routing history shows which operations and machines a similar part required. Pricing history shows what won and what lost against past customers. The estimator's job is really a search and pattern-matching problem across that history, followed by a judgment call on adjustment for the specific quantity, tolerance, and material of the new RFQ, but today that search happens in someone's head or, at best, a manual query against the ERP.

AI turns that search into something the estimator starts from instead of builds from scratch. A document model reads the incoming RFQ, drawing callouts, material spec, quantity breaks, tolerance requirements, and extracts a structured part description. That description is matched against the ERP's job history to find comparable parts by material, size, process, and complexity, pulling the actual cost and routing data from those historical jobs. A draft quote is generated with the historical basis shown, this part is priced similarly to job 22-4471 and 23-1082, adjusted for the quantity difference, for the estimator to review, adjust, and finalize.

This does not remove the estimator's judgment on the parts that matter most, a genuinely novel part with no close historical match still needs a from-scratch estimate, and the system says so rather than forcing a bad match. What it removes is the manual search time on the RFQs that do resemble prior work, which for most job shops and EMS providers is a large share of incoming volume. For a VP of sales operations watching quote turnaround time and quote-to-win conversion, the win is faster response on RFQs that would otherwise sit in a queue, and pricing that is grounded in the shop's actual cost history instead of a rough rule of thumb.