Inspection Sampling Plan by Characteristic Worksheet
Most shops sample the same way for every feature, which over-inspects cosmetic items and under-inspects the dimensions that can scrap a lot. This worksheet sets a zero-acceptance sample size per characteristic from the defect level you are willing to risk, then prices the inspection time.
| Characteristic | Class | Lot size (pcs) | Lot tolerance percent defective (RQL) (%) | Confidence (%) | Inspection time per piece (min) | Inspector rate (per hour) | Sample size (c = 0) | Percent of lot inspected | Inspection hours | Inspection cost | |
|---|---|---|---|---|---|---|---|---|---|---|---|
299 | 12.5% | 7.475 | $411 | ||||||||
119 | 5% | 0.9917 | $45 | ||||||||
57 | 2.4% | 0.95 | $52 | ||||||||
35 | 1.5% | 0.175 | $7 | ||||||||
119 | 14.9% | 3.9667 | $218 | ||||||||
22 | 55% | 1.1 | $66 |
Sample size uses the binomial zero-acceptance formula n = ln(1 - C) / ln(1 - p), with C the confidence and p the lot tolerance fraction defective, rounded up and capped at the lot size. It is conservative for small lots, where the hypergeometric distribution gives a somewhat smaller n. Accept the lot only with zero defects in the sample. This is not an ANSI/ASQ Z1.4 AQL plan, which is indexed by lot size and acceptance quality limit. Confirm any plan against the customer's quality clause.
Check your sampling plans against lot history
Optional: we can outline how to compare planned sample sizes with your actual escapes and rejects from ERP data.
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How to build the plan
List the characteristics from the drawing and control plan. Classify each as critical, major or minor using the customer's definitions. For each, enter the lot size, the lot tolerance percent defective you want to catch (the RQL), and the confidence that a lot at that defect level will be rejected. Tighter RQL and higher confidence for critical features, looser for cosmetic. Add the inspection time per piece and the inspector rate to see hours and cost. The result is a defensible, feature-specific plan rather than one number for the whole part.
- Critical: RQL 1 percent or lower, confidence 95 percent or higher.
- Major: RQL 2.5 to 5 percent, confidence 90 to 95 percent.
- Minor: RQL 6.5 percent or higher, confidence 90 percent.
- Use 100 percent inspection or automated gauging where the sample size approaches the lot.
The math behind c = 0
If a lot has fraction defective p and you inspect n randomly chosen pieces, the chance of finding no defects is (1 - p) to the power n under the binomial model. To have at most a 1 - C chance of accepting a lot at that defect level, solve (1 - p)^n = 1 - C, giving n = ln(1 - C) / ln(1 - p). For 1 percent and 95 percent that is 299 pieces; for 2.5 percent and 95 percent, 119; for 10 percent and 90 percent, 22. The familiar rule that 22 samples at c=0 gives 90 percent confidence of catching a 10 percent defective lot comes straight from this.
Reading the example
In the example, the 2,400-piece lot needs 299 bore measurements at 1.5 minutes each, about 7.5 hours and roughly 410 dollars, while the cosmetic check needs only 35 pieces. The sheet makes the cost of tighter RQL obvious: moving the bore RQL from 1 percent to 0.5 percent roughly doubles the sample to about 598. That helps when negotiating with customers, since you can show the exact inspection burden behind a requirement and where automated gauging would pay back.
Limits and cautions
A c=0 plan protects against bad lots, it does not estimate the process average, and it gives no information on capability. For stable, high-volume characteristics, control charts or variables sampling are more efficient. Use hypergeometric calculations for lots under about 200 where the binomial overstates the sample. Never lower confidence to hit a cost target without recording the risk, and respect customer-imposed plans such as AQL 0.65 with Z1.4 when the contract calls for them.
Frequently Asked Questions
What is RQL?
Rejectable quality level, also called lot tolerance percent defective. It is the defect rate you want the plan to reject with the stated confidence. A plan with RQL 1 percent at 95 percent confidence rejects 95 percent of lots that are exactly 1 percent defective.
How is this different from AQL sampling?
AQL plans, such as ANSI/ASQ Z1.4, are indexed by lot size and the acceptable quality level and usually allow some defects in the sample. This sheet uses a fixed zero-acceptance rule driven by the risk you set, which is common in aerospace and medical contracts.
Why is the sample capped at the lot size?
When the required sample exceeds the lot, you are effectively at 100 percent inspection. The sheet caps n at the lot size so costs are not overstated. For tiny lots, a hypergeometric plan would give a smaller sample than the binomial formula.
Can I use this for destructive tests?
Yes, but use the per-piece time and rate columns to capture test cost and scrap value, and consider a small-sample plan with higher RQL. Destructive tests often justify a variables plan based on measured values rather than pass/fail counts.
If your inspection plans and results live in SAP QM, Infor, Epicor or another ERP, Netray can build a read-only agent that proposes sample sizes from lot history and shows the data it used.
Related Tools
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Free tools
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ERP fixes and how-tos
- How to create an inspection plan in SAP QM (QP01)Create an inspection plan in SAP QM with QP01: header, operations, master inspection characteristics, sampling procedure and material assignment, plus a plan report.
- SAP QM Valuation Mode and Valuation Rule: Where Set and How They DecideHow valuation modes and valuation rules work in SAP QM: where they are set, how they decide accept or reject for characteristics, and how they drive the usage decision.
- How to Create a Sampling Procedure in SAP QMCreate a sampling procedure and sampling scheme in SAP QM: QDV3, QDP1, sample types, valuation modes and how to assign them to inspection plans.
Guides
- SyteLine Inspection Plan Setup and Configuration GuideConfigure SyteLine inspection plans with test criteria, sampling rules, and measurement specs. Set up receiving, in-process, and final inspection plans in CloudSuite.
- How to Set Up Inspection Plans in SyteLineStep-by-step guide to configuring inspection plans in Infor SyteLine. Define sampling rules, inspection operations, quality characteristics, and pass/fail criteria for incoming, in-process, and final inspections.
- Infor LN Quality Management Module: Complete GuideInfor LN Quality Management module guide: inspection orders, sampling, non-conformance and MRB flows, and AS9100D-aligned quality data manufacturers can audit.