Your submission has been received and we'll be in contact with you shortly.
Return home
Thank you for registering
May 2026
Cleaning a single product's BoM can absorb beyond 40 hours of senior engineer time — hundreds of line items, thousands of parameters. The cost isn't the hours. It's the unit-cost savings the team isn't finding while they clean.
Looking to subscribe to our newsletter? Click here.
Every cost engineer knows the first job when building a cost model is cleaning the Bill of Material (BoM) data for the product.
Unfortunately, cleaning a single product's BoM can absorb 40 hours+ of an engineer's time. A BoM is rarely a tidy list — most carry hundreds of line items and thousands of parameters across material, supplier, geometry, and process. Reconciling that file before any model gets built is the work that fills the calendar.
The cost of dirty BoM data isn't the hours. It's what the team isn't doing with them. While cost engineers are normalizing units and chasing material grades, they aren't finding the supplier alternative that takes 8% out of unit cost, running the scenarios that price tariff exposure, or pricing the design tradeoff that affects margin for the next two years. Cleanup work crowds out the work that moves the P&L.
The team is forced to limit modeling to the products that matter most. Cutting out scale, meaning they only deliver single models per quarter instead of the hundreds they really should.
Most BoMs that land on a cost engineer's desk fail one or more of the following checks. None of these are catastrophic alone. Together they make the model unusable.
A BoM should describe one product. In practice you get files where two or three assemblies are interleaved in the same sheet — sometimes because procurement consolidated similar SKUs into one extract, sometimes because the ERP export collapses parents into a single dump. If your modeling tool assumes one root, it builds a chimera, and you won't catch it from a unit cost number alone.
Hierarchy gets flattened. A subassembly that should sit between the parent product and its components is missing — its children appear directly under the top-level product, with no flag that they belong to a module. The model treats them as peers, which inflates direct-material allocation and hides assembly cost where nobody is looking.
Mass, material, supplier, manufacturer part number, lead time, country of origin — pick any column a cost model needs and there will be rows where it's blank. Engineering defined the right component but didn't populate what procurement needs to price it. So someone spends the afternoon emailing suppliers for material grades on parts the team has bought for three years.
"ABC Components." "ACME Industries." Internal codes that map to two or three legal entities. A supplier name that resolves to a vendor in the ERP but not to an actual manufacturer. Cost modeling needs supplier locations, capabilities, and pricing references — none of which you can extract from a vendor name that points to a distributor with offices in three countries.
Engineering thinks in millimeters. Procurement buys in inches. The ERP requires SI. A material is purchased by the kilogram and consumed by the gram. If the BoM doesn't declare units, your modeling tool will multiply two numbers in the wrong base and produce a cost figure that's off by orders of magnitude. The model runs. The number looks plausible. The decision built on it isn't.
Excel didn't cause dirty BoMs, but the workflow around it guarantees they stay dirty. There's no structural validation — Excel will happily save a parent row with no children, a child with no parent, or a hierarchy that breaks halfway down. There's no unit-of-measure enforcement: the cell next to 0.04 doesn't know whether it should be kilograms or inches. There's no audit trail, so when the engineer edits a row to make the model work, that edit is invisible to anyone opening the file three weeks later.
Errors compound. You inherit a file with five problems, normalize three, hand it to a colleague who normalizes one more in the wrong direction. The version that ends up in the model has six problems instead of five, and nobody knows which.
Leveraging AI for BoM comprehension provides a new solution: parse what's there, declare what's broken, accept user edits where available, fill in the rest.
In practice, four moves.
The file goes in as it is. The system parses structure — finding the actual root, resolving hierarchy, mapping columns to fields even when headers are non-standard. You don't normalize before uploading. The system normalizes during ingestion.
Every issue is a specific flag against a specific row. Multiple roots: flagged. A child without a parent: flagged. A missing unit: flagged. An ambiguous supplier name: flagged with the candidate entities it could resolve to. The engineer sees a punch list, not a "your BoM has problems" warning.
Engineers can make edits inline. Each flagged error surfaces in a single view, so the ones the engineer already knows the answer to get corrected fast. The needle-in-a-haystack exercise that used to take hours collapses into a single pass done in under a minute.
Some data gaps are unavoidable — suppliers not responding, a product design too early to have full specs. Muir's material and manufacturing databases fill those gaps by generating a product twin, so modeling can continue without waiting on the missing inputs. Every filled value carries provenance, so the engineer can see which fields the system generated and which came from the source.
This is the workflow Muir follows for BoM ingestion — upload, auto-analyze, flag errors, fill gaps, hand back a model where every assumption is visible. You can run it in five minutes instead of a quarter.
The cycle-time change isn't subtle. What used to take weeks fits in an afternoon, and the team gets that week back to spend on the work that justifies their seat at the table.
Before automated ingestion: receive BoM, open in Excel, spend two to four days cleaning, hand to a colleague for sanity-check, build a model, iterate, present a single number. Total cycle: one to three weeks. Outputs per quarter: a handful of models on the most strategic products.
With automated ingestion: upload, review flags within minutes, resolve the ones that need human judgment, run the model. Then run a second scenario against it. Then a third. Total cycle: an afternoon.
The capacity that gets unlocked is the point. The team that used to ship one model a month can run ten scenarios on it. They can bring cost into design reviews instead of post-design negotiation. They can find the supplier alternative the old workflow didn't have time to look for. With the bottleneck gone, the team can finally spend its time on the work that actually improves the company's bottom line.
If most of your cost-modeling cycle is data work that never makes it into the model itself, the bottleneck isn't your modeling approach — it's the workflow above it. Book a demo to see how Muir handles BoM ingestion end-to-end, from upload to working cost model.
A Bill of Materials with structural or data-quality issues that prevent it from being used directly in a cost model — multiple roots, skipped hierarchy levels, missing fields, ambiguous suppliers, inconsistent units. Almost every BoM that enters a cost engineering workflow is dirty by this definition.
Cost models are deterministic calculations applied to BoM data. If the BoM has wrong units, missing fields, or ambiguous suppliers, the model produces wrong numbers — with the same confidence as if the data were clean. Cost-model failures are usually upstream data failures, not modeling failures.
For a single product, cost modeling teams can spend beyond 40 hours of senior engineer time before a model can be built. BoMs commonly carry hundreds of line items and thousands of parameters; reconciling them by hand is the work that fills the calendar. The cost isn't the hours — it's the unit-cost savings the team isn't finding while they clean.
Yes. AI can absorb the multi-day cleanup work that used to sit between the BoM and the cost model: parsing structure, flagging errors, resolving ambiguous part numbers, generating product twins where supplier data is missing. The guardrail that matters: every inferred value carries provenance. Engineers see which fields were generated by the system, which were declared in the source, and which they edited inline. That audit trail is what makes the final cost model trustworthy.
You've signed up!
Don't worry, we don't spam.
