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Yarn Count Deviation: Length Delivered Against Length Contracted

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Yarn is sold by weight and consumed by length. A count certificate inside tolerance can still be an invoice for metres that never arrived.

Contract Terms

What the delivery was bought against

The band the contract allows either side of nominal. Used to count lots, never to cancel the money

What the Yarn Becomes

Your own consumption and contribution, not a figure this sheet assumes

Metres of yarn consumed per metre of finished fabric, from your own consumption records

Selling price less variable cost. Not gross margin, and not the fabric price

Lots Delivered

One row per lot as invoiced. Tested count is your laboratory result or the supplier certificate; leave it at zero for a lot not yet tested and it is weighed but never claimed.

LotNominal Count NeTested Count NeNet Weight kgPrice cost/kgDeviation %Length Short mClaimable costRow actions
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How to read this sheet

The arithmetic is an identity and the judgement is not. Metres per kilogram is Ne x 1693.38 exactly, so the lengths here are as reliable as the counts entered - and a single tested value per lot is a sample, not the lot. Count varies along a delivery and between packages, and a lot averaging 29.4 Ne contains yarn either side of that; where a claim is going to be argued, the count CV and the number of tests behind each average matter as much as the average, and neither is asked for here. The claim valuation prices the missing length at the nominal count, which is the count invoiced, and takes no view on whether the contract permits a claim at all - many yarn contracts price on weight and warrant count only within a tolerance, in which case the money is real and the claim is not. The production valuation is the more useful number and the softer one: it assumes the missing length would have been sold at the stated contribution, which is true only while the mill is selling everything it makes. Untested lots are carried at nominal so they never invent a shortfall, which also means a delivery tested selectively will understate the bias - test the lots at random rather than the ones that looked wrong on the machine.

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