Nobody gets a margin alert that says “BOM error.” That’s what makes this problem so persistent. Bad BOM data doesn’t announce itself. It leaks out slowly through overtime, expedited freight, write-offs, and quotes you win but shouldn’t have — and even the best inventory software solutions can’t catch what a flawed BOM feeds them. The system is only as accurate as the data underneath it.
The Four Leaks
Quoting below cost. If a BOM understates material content — a missing fastener set, an outdated resin price, no scrap allowance — your quoted margin looks healthy and your realised margin doesn’t. On a product you sell ten thousand units of, a two-rupee error per unit is twenty thousand rupees you never see. Multiply across a catalogue and the number gets uncomfortable.
Buying the wrong quantity. Material requirements planning is only as good as the input. If the BOM says you need 1.0 metres of a component but real consumption including offcuts is 1.15 metres, MRP will under-order by fifteen percent forever. Your buyers will compensate with emergency purchases at spot prices, and everyone will assume that’s just how the market is.
Inventory that doesn’t reconcile. When the BOM and actual consumption disagree, your system deducts the wrong amounts at backflush. Book stock drifts from physical stock. Eventually someone does a full count, finds a large variance, and writes it off. That write-off is a BOM problem wearing an inventory costume.
Production stoppages. The one everyone notices. A component missing from the BOM is a component nobody bought, and you find out when the line stops.
Why it Stays Broken
BOM data degrades because ownership is unclear. Engineering owns the design. Procurement owns the pricing. Production owns what actually happens. Costing owns the standard. Each group edits its own slice, often in its own file, and no single person is accountable for the whole record being true.
The second reason is that errors are invisible at the point they’re made. Type 100 instead of 1000 in a stock count and someone notices next week. Type the wrong scrap percentage into a BOM and you might not notice for a year.
How to Find The Damage
Run a variance analysis: for each product, compare BOM-implied material consumption against actual issued quantities over the last quarter. Sort by absolute value. The top twenty items on that list are where your money is going. Most manufacturers who do this exercise for the first time are surprised by which products show up.
Then check your standard costs against current purchase prices. Anything more than six months stale should be re-based. And check for BOMs that haven’t been revised since launch — not because age is a problem, but because it usually means nobody has looked.
Keeping it Clean
Accuracy is a process, not a project. Set a review cadence tied to product volume, require a change record for every edit, and make consumption variance a monthly metric someone reports on. ticktick.ai flags BOM lines where actual consumption diverges from the standard, so the error surfaces in weeks rather than at year-end audit.
