AI for Manufacturing Quoting Needs an Operations Lens
ARTICLE
AI for manufacturing quoting needs an operations lens
AI can price every line of a fabricated job. It just cannot use what your best estimator knows until someone sits down and captures it.
What AI does in quoting that people cannot
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It prices every line on every job.
A large fabricated assembly carries hundreds of cost lines: plate, coating, bolts, fit-up hours, freight, and more. An AI model trained on the manufacturer's own job costs prices all of them, on every bid. That is how you stop covering the small items with a flat percentage, which is where margin disappears on your largest jobs. In the work we have done, the largest jobs were coming back at 14 percent against a 22 percent quote before anyone had the historical data to see it.
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It compares every quote to what the job actually cost.
Thousands of closed jobs at once, broken out by product line, size, material, customer, and rep. Most manufacturers have never seen this comparison. It shows which work returns the margin you priced and which does not.
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It recognizes a bid like one it has seen before.
When an RFQ matches the product line, size range, and customer profile of a job that ran over, the estimator and inside sales see it while the number can still change, along with what that job was quoted and where the cost went.
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It builds the ISO documentation as the quote is built.
Compliance packages assembled from content that is already approved and revision-controlled, populated from the same job record the quote came from. The package is finished when the quote is, not weeks later. Compliance still signs off.
Connecting the quote to what the job actually cost
All four use cases depend on this connection, and most manufacturers have never made it. The quotes live in spreadsheets. The closed job costs live in the accounting system.
We build the historical data from quoting workbooks, job cost detail, and the general ledger, reconciled job by job. We track quote revisions so each job matches the version that was actually sold, and keep change orders separate from original scope.
The analysis itself is not sophisticated. It goes undone because the reconciliation sits between three functions and belongs to none of them. Sales owns the quote, operations owns what happened on the floor, accounting owns the cost, and nobody owns the mapping between them.
Once the historical data exists, the pattern on large jobs is obvious. The flat percentage carried for small items covers them at ordinary size and stops covering them as jobs grow. Coating overspray grows with surface area. Piece counts climb. Labor efficiency drops across a longer run. Any one of those is minor. Past a certain job size, together, they are the gap.
Sales owns the quote. Operations owns what happened on the floor. Accounting owns the cost. And nobody owns the mapping between them.
What the estimator knows that the historical data does not
At most manufacturers, one or two people price the most complicated jobs, with thirty or forty years of experience. They know which fabrications take longer to fit than the drawing suggests, which customers revise after release, and when a scope will grow. None of it is written down, which is why none of it is in Claude or ChatGPT, and why it leaves when they do.
We capture it by sitting with them on live bids and asking why at each adjustment. Three things come out:
- A written account of how estimating actually works, exceptions included
- A rule set a system can run and a reviewer can read
- A versioned library of the assumptions, standards, and cost bases behind each decision
The second one is what governs the model. Estimators set which assumptions move first and what each round of tuning is measured against. They set where the system stops and raises an engineering error instead of resolving on its own. Every run returns a summary of what parameters were kept or changed and why, so an estimator can defend a price a year later.
One example of what that surfaces: an estimator told us that if a third round of tuning is needed, the earlier adjustments have to reverse rather than stack, because compounding corrections produce a number that means nothing. Nobody asked for that rule. It came out of watching him work.
Miss a rule at that level and every quote the system produces carries the same error before anyone notices.
Nobody asked for that rule. It came out of watching him work.
Five teams rebuild the same job five different ways
Inside sales builds the quote. Engineering rebuilds the scope against code. Operations rebuilds the specification for the floor. Accounting rebuilds cost against margin with burden and workers comp, which is where the margin numbers leadership manages by get formed, reconstructed from a quote never built to carry them. Compliance rebuilds the ISO package after the job ships, because nobody asked what the document needed before it was written.
Vendors miss both, because the requirements come from whoever briefed them, and that is almost always inside sales.
So we ask all five departments what they need before anything gets built. Then quoting moves into a secure web application inside the manufacturer's existing Microsoft 365 environment: pricing maintained in one place, pricing rules held centrally, permissions by role, and approval steps for quotes that need review.
Excel does not go away. Accounting gets its financial breakouts, operations and engineering get the views they work from, compliance gets the ISO package. Every one is generated from the same approved record, so five rebuilds become one. Excel becomes an output instead of the system where pricing lives.
Quoting runs about 70 percent faster, throughput is up about 40 percent with no added staff, and roughly a third of the margin that was leaking on large jobs is back.
Everything above depends on the order of the work. The departments and the estimators first, the technology after. Reverse it and you get a faster quote that is wrong in a new way.
If your quotes and job costs have never been compared, that is where we would start.
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What you're probably
wondering
Quick answers to the most common questions
Can AI improve quoting accuracy in manufacturing?
Yes, but only where the inputs exist. AI can price every cost line on every bid and compare closed jobs against what they were quoted, which no estimator can sustain by hand. Both depend on historical data connecting quotes to final job costs. In most manufacturers that connection has never been built, and no model creates it for you.
Why do AI quoting projects fail?
They usually fail on inputs rather than models. Quotes sit in individual spreadsheets, costs sit in accounting, and nothing links them, so there is nothing reliable for the model to learn from. The second common failure is missing the estimating judgment that was never documented, which means the system runs without rules that experienced estimators would consider obvious.
Where does margin leak in manufacturing quotes?
Most often in the largest jobs. The flat percentage estimators carry for small unpredictable items covers them at ordinary size and stops covering them as jobs grow, because coating overspray grows with surface area, piece counts climb, and labor efficiency drops across a longer run. We have seen jobs quoted at 22 percent returning 14 percent before anyone had the data to see the pattern.
Do you have to replace Excel to use AI in quoting?
No. Excel changes role rather than disappearing. Pricing and pricing rules move into a controlled system with approvals and permissions, and Excel becomes an output: accounting gets its financial breakouts, operations and engineering get the views they work from, and every one is generated from the same approved record.
