# SAP QM Zero Acceptance Sample Size Calculator (c=0) > Source: https://netray.co/tools/sap-qm-zero-acceptance-sample-size-calculator > Publisher: Netray (netray.co), AI and integration engineering for ERP and manufacturing systems. > Section: Free Tools ## Short answer Find how many units to inspect when the rule is accept on zero defects, for a given lot size, LTPD and consumer risk. ## Category sap ## Erp SAP S/4HANA ## Audience - SAP QM consultant - Quality engineer - Incoming inspector ## Tool **Kind:** calculator **Inputs:** #### Lot size **Key:** lot_size **Type:** number **Default:** 500 **Min:** 2 **Step:** 10 **Unit:** units **Help:** Inspection lot quantity as posted by the goods receipt. #### Limiting quality (LTPD) **Key:** ltpd **Type:** number **Default:** 5 **Min:** 0.1 **Max:** 50 **Step:** 0.5 **Unit:** % defective **Help:** The defect rate you want to reject with high confidence. #### Consumer risk (probability of accepting a bad lot) **Key:** beta **Type:** select **Default:** 0.1 **Options:** ##### 10% (90% confidence) **Value:** 0.1 ##### 5% (95% confidence) **Value:** 0.05 ##### 1% (99% confidence) **Value:** 0.01 **Outputs:** #### Defective units in a lot at LTPD **Key:** d **Expr:** max(1, ceil(ltpd / 100 * lot_size)) **Format:** integer #### Sample size (binomial, large lot) **Key:** n_binom **Expr:** min(lot_size, ceil(ln(beta) / ln(1 - ltpd / 100))) **Format:** integer **Description:** Treats every draw as independent. Slightly conservative for small lots. #### Sample size (hypergeometric approximation) **Key:** n_hyper **Expr:** min(lot_size, ceil((lot_size - (d - 1) / 2) * (1 - pow(beta, 1 / d)))) **Format:** integer **Primary:** true **Description:** Finite lot correction. Use this one for lots under a few thousand units. #### Share of the lot inspected **Key:** pct **Expr:** n_hyper / lot_size * 100 **Format:** percent **Chart:** #### Sample size vs LTPD **Type:** line **X:** ltpd **From:** 1 **To:** 10 **Steps:** 19 **Series:** - n_hyper - n_binom **Disclaimer:** Planning aid. Confirm against your contract, the applicable sampling standard and your quality agreement before maintaining the sampling procedure in SAP. ## Content **Intro:** A c=0 plan accepts a lot only if the sample has zero defects. It is common in aerospace, medical and automotive receiving inspection because it is easy to explain and strict at the right defect rate. This calculator gives the sample size that holds the consumer risk you choose. **Sections:** #### How to use it **Body:** Enter the lot size from the inspection lot, the defect rate you consider unacceptable (LTPD) and the chance you will tolerate of accepting such a lot. The tool shows a binomial answer and a finite-lot hypergeometric answer. Take the hypergeometric figure for small and mid-size lots, since inspecting 40 of 60 units tells you much more than the binomial formula assumes. The sweep chart shows how the sample size falls as you relax the LTPD, which is the fastest way to see the cost of a tight requirement. #### The method behind it **Body:** With zero acceptance, the probability of accepting a lot with D defective units out of N is the chance that none appear in n draws without replacement. The tool solves for the smallest n where that chance is at or below the consumer risk, using the approximation n = (N - (D-1)/2) x (1 - beta^(1/D)). For large lots it converges to ln(beta) / ln(1 - p). **Bullets:** - beta 10% at LTPD 5% gives roughly 45 units for a large lot - Halving the LTPD roughly doubles the sample - A smaller lot never needs more than the lot itself #### Using the result in SAP QM **Body:** In SAP QM the sampling procedure is a master data object (transaction QDV1 and related) and the sample scheme or fixed sample size is assigned per inspection characteristic or inspection type. If you work from a table-based scheme you will pick the nearest entry, while a fixed sample size can be typed directly. Document the LTPD and risk behind the number in the inspection plan text so the next engineer knows why it was chosen. #### Common mistakes **Body:** Teams often confuse LTPD with AQL. AQL describes a quality level you would like to accept most of the time, while LTPD describes a level you want to reject almost always. Another mistake is applying c=0 to a process with real, tolerated defect rates, which rejects too many good lots. Finally, remember that a sample of one critical characteristic does not replace 100 percent checks on safety features. **Cta:** If you want sampling decisions, lot history and usage decisions summarised from your SAP data without exporting reports, we can build that on your own system. **Faq:** #### What is the difference between LTPD and AQL? **Answer:** AQL is the defect rate a plan should usually accept, so it protects the producer. LTPD is the defect rate a plan should usually reject, so it protects the consumer. This calculator is built around LTPD and consumer risk. #### Why does the hypergeometric size come out smaller? **Answer:** Sampling without replacement from a small lot gives each draw more information, because the pool of untested units shrinks. The binomial formula ignores that and so asks for slightly more units. #### Can I use this for variable data? **Answer:** No. It applies to pass or fail attribute inspection. For measured characteristics a variables plan or a capability study usually needs fewer units for the same protection. #### Does this match ANSI Z1.4 tables? **Answer:** Not exactly. Z1.4 is indexed by AQL and inspection level, while this tool is built from a chosen LTPD and risk. Use it to sanity check or to design a custom c=0 plan. ## Search terms c=0 sampling plan calculator, zero acceptance number sample size, SAP QM sampling procedure sample size, LTPD sample size calculator, lot tolerance percent defective sample size, SAP QM sampling scheme, acceptance sampling calculator, hypergeometric sample size c=0 --- Talk to an engineer: https://netray.co/contact?intent=erp_ai | Machine catalog: https://netray.co/agent/catalog.json