Hubbry Logo
Acceptable quality limitAcceptable quality limitMain
Open search
Acceptable quality limit
Community hub
Acceptable quality limit
logo
7 pages, 0 posts
0 subscribers
Be the first to start a discussion here.
Be the first to start a discussion here.
Acceptable quality limit
Acceptable quality limit
Acceptable quality limit
View on Wikipedia
from Wikipedia

The acceptable quality limit (AQL) is the worst tolerable process average (mean) in percentage or ratio that is still considered acceptable; that is, it is at an acceptable quality level.[1] Closely related terms are the rejectable quality limit and rejectable quality level (RQL).[1][2] In a quality control procedure, a process is said to be at an acceptable quality level if the appropriate statistic used to construct a control chart does not fall outside the bounds of the acceptable quality limits. Otherwise, the process is said to be at a rejectable control level.

In 2008 the usage of the abbreviation AQL for the term "acceptable quality limit" was changed in the standards issued by at least one national standards organization (ANSI/ASQ) to relate to the term "acceptance quality level".[3][4] It is unclear whether this interpretation will be brought into general usage, but the underlying meaning remains the same.

An acceptable quality level is a test and/or inspection standard that prescribes the range of the number of defective components that is considered acceptable when random sampling those components during an inspection. The defects found during an electronic or electrical test, or during a physical (mechanical) inspection, are sometimes classified into three levels: critical, major and minor. Critical defects are those that render the product unsafe or hazardous for the end user or that contravene mandatory regulations. Major defects can result in the product's failure, reducing its marketability, usability or saleability. Lastly, minor defects do not affect the product's marketability or usability, but represent workmanship defects that make the product fall short of defined quality standards. Different companies maintain different interpretations of each defect type. In order to avoid argument, buyers and sellers agree on an AQL standard, chosen according to the level of risk each party assumes, which they use as a reference during pre-shipment inspection.

See also

[edit]

References

[edit]

Further reading

[edit]
Revisions and contributorsEdit on WikipediaRead on Wikipedia

Acceptable quality limit

View on Grokipedia
from Grokipedia
The Acceptable Quality Limit (AQL) is a statistical measure in that specifies the worst tolerable average quality level of a production lot, expressed as a or of defective units, beyond which a batch is typically rejected during sampling inspections. Defined in international standards as the process average quality level corresponding to which lots of that average quality level have a high probability (e.g., 95%) of being accepted based on the sample inspection results, AQL helps manufacturers and buyers balance inspection costs with acceptable risk levels for product quality. Originating from military sampling procedures in the 1930s and formalized through standards like MIL-STD-105E (1989), evolved into a global benchmark through ISO 2859-1, first published in 1989 and revised in 1999, which outlines procedures for attribute-based sampling inspections. This standard indexes sampling plans by values, enabling consistent application across industries such as , pharmaceuticals, and consumer goods. Unlike zero-defect ideals, acknowledges practical limitations in large-scale production by permitting a controlled defect rate while minimizing the risk of shipping substandard products. In practice, AQL is applied through sampling tables that determine the sample size based on lot quantity, inspection level (e.g., General Level II for most cases), and defect severity. Defects are categorized into three classes: critical (safety hazards, often 0% AQL), major (functional impairments, typically 2.5% AQL), and minor (aesthetic issues, usually 4.0% AQL). For example, in a lot of 5,000 units at a 1% AQL under General Inspection Level II, inspectors would sample 200 items and accept the batch if no more than seven defects are found, using tools like AQL calculators for precision. Widely used in —particularly for pre-shipment inspections of imported goods— ensures compliance with buyer specifications and reduces disputes by providing an objective, data-driven acceptance criterion. It integrates with methodologies like for process improvement, though critics note it may encourage tolerance of defects rather than driving perfection. Overall, remains a of efficient , adaptable to varying risk tolerances across sectors.

Definition and Principles

Core Definition

The Acceptable Quality Limit (AQL) is the worst tolerable process () defect rate in a series of lots subjected to sampling , expressed as a or of nonconforming units. Defined in the ISO 2859-1 as the "quality level that is the worst tolerable," it specifies the maximum proportion of defects deemed acceptable for ongoing production, such as 1% AQL indicating that an average of up to 1% defects across multiple lots is permissible. AQL emphasizes long-term average quality in the manufacturing process rather than the rejection criterion for an individual lot, where a single batch might be accepted even if its defect rate temporarily exceeds the AQL due to the statistical nature of sampling. This distinction ensures that AQL serves as a threshold for process capability over time, with repeated inspections balancing acceptances and rejections to maintain the overall defect average at or below the specified limit. Acceptance numbers are determined from standardized sampling tables (e.g., in ISO 2859-1) that specify the maximum allowable defects in the sample based on the , sample size, and desired risk levels, ensuring a high probability of accepting lots at the . For example, a 2.5% applied to batches means those with an average 2.5% defect rate are generally acceptable over time, but sustained higher rates would result in more frequent rejections to enforce the limit. The Acceptable Quality Limit (AQL) framework in acceptance sampling is grounded in key statistical concepts that manage the risks associated with lot inspection decisions. Central to this are the producer's risk, denoted as α, which represents the probability of incorrectly rejecting a lot that meets the acceptable quality level, thereby protecting the supplier from undue penalties for good quality output. This risk is typically set at 5%, ensuring a high probability (95%) of accepting lots at the AQL. Complementing this is the consumer's risk, denoted as β, which is the probability of erroneously accepting a lot with unacceptable quality, safeguarding the buyer from poor products entering their processes. This risk is commonly fixed at 10%, corresponding to a 90% chance of rejecting defective lots at the threshold of unacceptability. These risks form the basis for designing sampling plans that balance supplier and buyer protections in quality control. A critical tool for evaluating these risks is the Operating Characteristic (OC) curve, which graphically depicts the probability of lot acceptance as a function of the true defect proportion, p, in the population, expressed as P(accept) = f(p). The curve illustrates the discriminatory power of a sampling plan: it approaches 1 (near-certain acceptance) for low defect levels near the AQL and drops toward 0 (likely rejection) for higher defect rates, with the shape determined by sample size and acceptance criteria. By plotting acceptance probability against p, the OC curve allows assessment of how well the plan controls α at the AQL and β at unacceptable quality levels, aiding in plan selection for desired risk profiles. Another foundational concept is the Lot Tolerance Percent Defective (LTPD), defined as the defect proportion at which the probability of lot rejection reaches 90% (or acceptance probability is 10%, aligning with β). Unlike the , which marks the upper limit of acceptable quality with high acceptance likelihood, the LTPD specifies the poorest quality level tolerable to the , where the sampling plan reliably detects and rejects bad lots. This contrast ensures that plans focus on permitting good lots while stringently guarding against those exceeding the LTPD threshold.

Historical Development

Origins in Military Standards

The concept of the Acceptable Quality Limit (AQL) emerged in the 1940s within the U.S. military during , driven by the need to inspect vast quantities of ammunition and supplies efficiently while maintaining reliability in procurement processes. This military application built upon earlier work by statisticians and Harry G. Romig at Bell Laboratories, who developed sampling inspection tables in the and published key contributions in 1941. The U.S. Army Ordnance Department led this development, collaborating with Dodge and Romig, who focused on attribute sampling techniques to detect defective items in high-volume production. This approach addressed the impracticality of 100% inspection amid wartime demands, introducing AQL as a threshold for acceptable defect rates to ensure consistent supplier quality without excessive resource expenditure. The first formalization of these ideas occurred in with the publication of the Army Ordnance Standard Sampling Inspection Tables, which outlined sampling plans indexed by values and emphasized process average quality over exhaustive s. These tables allowed military inspectors to accept lots based on representative samples, switching to tightened if quality fell below the specified , thereby incentivizing suppliers to sustain production standards. This military-specific framework prioritized statistical risks, such as producer and error probabilities, to balance efficiency and protection against substandard goods. Post-World War II, the adoption of AQL-based sampling expanded in U.S. military to curb the escalating costs of inspection in sustained high-volume manufacturing. By formalizing these procedures in MIL-STD-105A, issued in 1950, the Department of Defense integrated AQL into standardized attribute sampling for ongoing reliability, reducing the need for full lot inspections while upholding quality thresholds established during the war. This shift marked a pivotal gain, as sampling plans enabled faster throughput without proportionally increasing defect acceptance.

Evolution into International Standards

Following the establishment of AQL principles in military standards during and after , the 1950s marked the beginning of their adaptation for civilian use as global manufacturing expanded beyond defense needs. The U.S. military's series, with its 1963 revision (MIL-STD-105D) providing detailed sampling tables, directly influenced the development of commercial standards, culminating in the American Society for Quality's ANSI/ASQ Z1.4 in 1971, which transposed these procedures for non-military in industries like and automotive production. This shift was driven by the need for consistent, cost-effective inspection methods in peacetime economies, where the final 1989 revision of (MIL-STD-105E) further refined the framework before its cancellation in 1995, by which time ANSI/ASQ Z1.4 had become the de facto civilian equivalent, nearly identical in structure and application. The push toward international harmonization accelerated in the 1970s, with the (ISO) publishing ISO 2859 in 1974 as a unified procedure for attribute sampling indexed by , drawing from the lineage to create globally applicable tables that minimized discrepancies in cross-border trade. This was expanded and technically revised into ISO 2859-1 in 1989, establishing it as the cornerstone international standard for -based inspection by attributes and aligning it closely with ANSI/ASQ Z1.4-1989 to facilitate worldwide adoption in supply chains. Subsequent updates enhanced the standard's robustness; the 1999 edition of ISO 2859-1 introduced significant revisions, including a new procedure for switching from normal to reduced inspection and a reference to skip-lot sampling, alongside tightened specifications for switching rules between normal, tightened, and reduced inspection levels, thereby addressing evolving needs for precision in diverse contexts. This was further amended in 2011. A new edition is expected in late 2025. During the 1980s, with the rise of comprehensive , procedures, including those based on AQL, were incorporated into broader systems such as the ISO 9000 series released in 1987, which supported the use of statistical methods like those in ISO 2859 for and compliance across international organizations. This linkage elevated AQL from a tactical method to a foundational element of global quality regimes, influencing standards like ISO 9001 for ongoing process monitoring.

Implementation and Methodology

Sampling Inspection Procedures

Sampling inspection procedures under the Acceptable Quality Limit (AQL) framework follow a standardized sequence to evaluate product lots for acceptance or rejection based on attribute , as defined in ISO 2859-1. The process begins with determining the lot size, which represents the total number of units in the batch to be inspected, and selecting an appropriate inspection level. Inspection levels are categorized into general (I, II, III) and special (I, II, III), with General Level II serving as the default for most applications to balance inspection rigor and efficiency. Special levels are used for lots requiring tighter control, such as high-value items, while reduced inspection may apply after consistent performance. Once the lot size and level are established, a sample size code letter is selected from Table I of the standard, which maps lot size ranges to (e.g., small lots of 2 to 8 units typically correspond to code letter A under General Level II). This code letter determines the sample size to be drawn randomly from the lot, ensuring representativeness. Next, the value is chosen based on the agreed tolerance, often specified separately for different defect severities. Defects are classified into critical, major, and minor categories, with critical defects (those posing risks) assigned the lowest (e.g., 0.10% or less), major defects (affecting function) a moderate (e.g., 1.0% to 2.5%), and minor defects (aesthetic issues) a higher (e.g., 4.0%). Each defect class uses its own for independent evaluation. The inspection then proceeds by examining the sample for defects according to the chosen sampling plan type—single, double, or multiple—typically starting with single sampling for simplicity. From Table II-A, the is entered alongside the sample size code letter to reference Table II-B, which provides the number (Ac) and rejection number (Re) for the plan. Inspectors count defects in the sample and classify them accordingly. The decision rule is straightforward: the lot is accepted if the number of defects in any class is less than or equal to Ac, and rejected if it exceeds Re; if the sample size is fully inspected without reaching Re, acceptance occurs. This process ensures statistical protection for both and risks, with switching rules allowing transition to reduced or tightened based on prior lot performance.

AQL Tables and Calculations

The Acceptable Quality Limit (AQL) sampling system, as defined in ISO 2859-1, relies on standardized tables to determine sample sizes and acceptance criteria for attribute inspection. These tables enable inspectors to select appropriate plans based on lot size, inspection level, and specified AQL values, ensuring consistent quality assessment without examining every item. The primary tables include Table I for assigning sample size code letters and Tables II-A and II-B for deriving acceptance and rejection numbers under normal and tightened inspection, respectively. Table I, titled "Sample size code letters," cross-references lot or batch sizes against levels (special, general I, II, III, or reduced) to assign a code letter (e.g., A through N), which indirectly determines the sample size. For instance, general levels are commonly used, with Level II providing a standard balance between effort and protection; for a lot size of 501 to 1,200 units at Level II, the code letter is G, corresponding to a sample size of 32 units. This table ensures , as larger lots receive proportionally larger samples while keeping feasible. Table II-A provides single sampling plans for normal inspection, listing acceptance number (Ac) and rejection number (Re) for each sample size code letter and value, expressed as percent nonconforming. It serves as the master table, where the quality index (derived from ) guides the limits; for example, under code letter H and an of 1.5%, the plan specifies Ac=1 (accept if 1 or fewer defects) and Re=2 (reject if 2 or more). Table II-B extends this for tightened inspection, applying stricter criteria (lower Ac and Re) when prior lots indicate quality issues, such as for the same code H and 1.5% , where Ac=0 and Re=1 to heighten scrutiny. These tables prioritize producer's and consumer's risks, with representing the worst tolerable average quality. To apply these tables, an inspector first uses Table I to find the code letter, then consults Table II-A (or II-B) for the Ac and Re based on the . For a lot of 1,000 units at general inspection Level II and an of 2.5%, the code letter is G, yielding a sample size of 32; the lot is accepted if no more than 1 defect is found (Ac=1, Re=2). This calculation balances defect tolerance with lot protection, assuming random sampling and binomial defect distribution. Switching between inspection types follows predefined rules to adapt to supplier performance. Reduced , which uses smaller samples and higher Ac/Re thresholds from a separate table (Table II-C for reduced), is invoked after 10 consecutive lots are accepted under normal , signaling stable quality; conversely, tightened applies after two out of five successive lots are rejected. These rules, outlined in section 9 of ISO 2859-1, prevent overuse of stringent plans while maintaining vigilance. For variables sampling, where quality is assessed via measurements rather than attributes, ISO 3951 extends the AQL framework by indexing plans to percent nonconforming estimates derived from process mean and standard deviation. Single sampling plans in ISO 3951-1, for example, accept lots if the estimated nonconforming fraction (using estimators like the known or unknown schemes) falls below the , providing tighter control for continuous data without counting discrete defects. This approach complements attribute methods by leveraging measurement precision for the same targets.

Applications and Use Cases

In Manufacturing and

In manufacturing, the Acceptable Quality Limit (AQL) plays a central role in production processes, particularly through pre-shipment inspections that verify the outgoing quality of batches before they reach end-users or downstream assembly lines. These inspections use AQL to define the maximum tolerable defect rate in a sampled lot, allowing manufacturers to accept batches that meet predefined quality thresholds while rejecting those exceeding them. For instance, in assembly, an AQL of 1.0% is commonly applied to major defects—such as faulty or component misalignment—to ensure reliability without halting production entirely. AQL integrates effectively with Six Sigma methodologies by serving as a baseline for more stringent defect targets, such as (DPMO). While aims for a DPMO of 3.4 or fewer to achieve near-perfect process performance, AQL provides a practical sampling framework during production to monitor and align with these goals, enabling data-driven adjustments to reduce variation. This synergy allows manufacturers to transition from acceptance-based quality to proactive process improvement, using AQL results to identify root causes of defects and refine operations iteratively. A notable application appears in the automotive sector, where AQL levels such as 0.25% for critical defects—like structural weaknesses in brake components—support and help reduce product recalls. This approach enhances safety and compliance with standards like IATF 16949. The primary benefits of AQL in stem from its cost efficiencies compared to 100% , which requires substantially more labor and time for large batches. By inspecting only a representative sample, AQL significantly reduces inspection expenses in high-volume production, freeing resources for continuous improvement initiatives like defect . This approach not only maintains but also supports for long-term process enhancements, such as refining supplier inputs or machinery .

In Import/Export and Supply Chain Management

In global trade, importers frequently apply the Acceptable Quality Limit (AQL) to assess the quality of incoming shipments from suppliers, particularly for consumer goods such as apparel, where an AQL of 2.5% for major defects serves as a standard threshold to confirm compliance with specified quality standards. This approach allows buyers to evaluate batches without inspecting every item, reducing costs while mitigating risks of substandard products entering the market. For instance, in apparel imports, this level helps verify that defects like improper stitching or sizing issues do not exceed tolerable limits, ensuring supplier accountability across international supply chains. Third-party inspection services play a pivotal role in enforcing standards during import processes, with organizations such as SGS and conducting factory audits and pre-shipment inspections based on ISO 2859-1 sampling procedures. SGS, for example, employs ANSI/ASQ Z1.4 (equivalent to ISO 2859-1) to perform AQL-based random sampling, generating detailed reports on product quality, packaging, and labeling to support importer decisions. Similarly, integrates ISO 2859 sampling plans into its product inspection protocols, enabling neutral verification of supplier performance in overseas facilities. These services are essential for , as they provide impartial assessments that bridge gaps between distant manufacturers and end buyers. A practical example in the apparel illustrates 's utility for high-volume imports: manufacturers often adopt an of 4.0% for minor defects, such as slight color variations or loose threads, to strike a balance between maintaining acceptable quality and controlling inspection expenses in large orders destined for markets like the or . This level accommodates the realities of while preventing excessive rejects, thereby streamlining logistics and reducing delays in global distribution. AQL sampling also ties into in import/export contexts, where third-party inspections verify aspects like labeling and material integrity to align with standards such as the EU's REACH on chemical substances or the U.S. CPSIA for children's product safety. By incorporating AQL checks during these audits, importers ensure that sampled products meet both quality and legal requirements, facilitating smoother customs clearance and market entry.

Limitations and Alternatives

Criticisms of AQL Approach

One major criticism of the (AQL) approach is its inherent tolerance for a certain level of defects, which can permit chronic, low-level quality issues to persist across batches without triggering rejection. For instance, an AQL of 1% allows up to 1% defective units to be systematically accepted, potentially leading to cumulative risks over time as these defects may not be isolated but indicative of ongoing process shortcomings. In critical industries such as pharmaceuticals or medical devices, this tolerance raises significant consumer safety concerns, as even minor defects could result in product failures with severe consequences, prompting regulators and notified bodies to deem AQL insufficient for validating high-stakes processes. Another flaw lies in AQL's reliance on the assumption of random defect distribution during sampling, which often fails in practice when defects occur in clusters due to inconsistencies or batch-specific issues. If defects are non-randomly clustered—such as in specific production runs or material lots—a randomly selected sample may underrepresent the true defect rate, leading to the erroneous of substandard batches and masking underlying problems. This assumption is particularly challenging to meet, as achieving truly random sampling is difficult in real-world production environments, resulting in high uncertainty and unreliable outcomes even with standard plans like those in ANSI/ASQ Z1.4. The fixed AQL tables, developed in the mid-20th century and standardized in the , are viewed as outdated for contemporary contexts that emphasize , real-time monitoring, and zero-defect philosophies. These static tables do not adapt well to modern automated processes where defect rates are extremely low, making sampling inefficient and costly without providing insights into overall process stability or improvement. Furthermore, AQL conflicts with and goals of eliminating defects entirely, as it accepts variability rather than driving continuous enhancement, leading many organizations to phase it out in favor of process-focused metrics. Empirical evidence underscores these issues, with studies revealing hidden quality drifts in AQL-inspected lots where accepted batches later exhibited higher defect rates upon full . More recent FDA observations, such as those in 2023, have similarly criticized AQL-related shortcomings, where inadequate addressing of visual and sampling failures permitted lapses to go undetected.

Modern Alternatives and Improvements

In response to criticisms of AQL's tolerance for a certain level of defects, zero-defect philosophies have emerged as prominent alternatives, emphasizing process improvement over . , developed by in the 1980s, employs the (Define, Measure, Analyze, Improve, Control) framework to identify and eliminate root causes of defects, targeting a maximum of 3.4 (DPMO), far stricter than AQL's typical 1-4% allowance. This approach shifts focus from lot-by-lot inspection to ongoing process optimization, reducing variability through statistical tools and fostering a culture of continuous improvement in environments. Advanced sampling techniques build on traditional by incorporating adaptability to supplier performance and data-driven insights. Skip-lot sampling, outlined in ISO 2859-3:2005, allows reliable suppliers to skip full inspections after a series of consecutive acceptable lots, potentially reducing overall inspection frequency by up to 80% while maintaining quality safeguards through random checks. Complementing this, AI-driven adaptive plans leverage to dynamically adjust sample sizes based on real-time defect patterns and historical data, enhancing efficiency without compromising detection accuracy. These methods enhance efficiency for high-volume production by prioritizing inspection resources where risks are highest. Improvements to frameworks include refinements in standards and emerging technologies for greater precision and transparency. The ANSI/ASQ Z1.4-2008 standard reaffirms and slightly refines earlier versions with updated switching rules between normal, tightened, and reduced inspection levels, resulting in tighter operating characteristic (OC) curves that better discriminate between acceptable and unacceptable quality levels. Additionally, integration of blockchain technology enables immutable of quality data across supply chains such as in agri-food, allowing real-time verification of compliance and reducing fraud risks in import/export scenarios. In contrast to AQL's producer-oriented focus on acceptable defect rates, LTPD-based plans like those in Dodge-Romig tables prioritize by specifying the lot tolerance percent defective (LTPD)—the poor quality level rejected with high probability (e.g., 90-95%)—often requiring larger samples for critical applications. These plans minimize average outgoing quality limits (AOQL) under rectifying inspection schemes, making them suitable for industries demanding near-zero risk, such as and pharmaceuticals.

References

  1. https://www.investopedia.com/terms/a/acceptable-quality-level-aql.asp
  2. https://qualityinspection.org/what-is-the-aql/
  3. https://www.iso.org/obp/ui/#iso:std:iso:2859:-1:ed-2:v1:en
  4. https://www.eurofins.com/assurance/resources/articles/explaining-acceptance-quality-limit-aql/
  5. https://www.6sigma.us/six-sigma-in-focus/acceptable-quality-level-aql/
  6. https://tetrainspection.com/aql-calculator-acceptable-quality-limit/
  7. https://www.qima.com/aql-acceptable-quality-limit
  8. https://sixsigmadsi.com/what-is-an-acceptable-quality-level/
  9. https://www.iso.org/standard/67669.html
  10. https://www.itl.nist.gov/div898/handbook/pmc/section2/pmc22.htm
  11. https://support.minitab.com/en-us/minitab/help-and-how-to/quality-and-process-improvement/acceptance-sampling/how-to/attributes-acceptance-sampling/interpret-the-results/all-statistics-and-graphs/
  12. https://www.itl.nist.gov/div898/handbook/pmc/section2/pmc232.htm
  13. https://bookdown.org/lawson/an_introduction_to_acceptance_sampling_and_spc_with_r26/introduction-historical-background.html
  14. https://asq.org/quality-resources/history-of-quality
  15. https://www.itl.nist.gov/div898/handbook/pmc/section2/pmc231.htm
  16. https://www.qualitymag.com/articles/98097-brief-history-of-ansi-asq-z14
  17. https://www.sqconline.com/about-acceptance-sampling
  18. http://everyspec.com/MIL-STD/MIL-STD-0100-0299/MIL-STD-105E_11675/
  19. https://cargoinspectionservice.net/wp-content/uploads/2021/04/ISO-Sampling-plans-iso2859-1.pdf
  20. https://www.iso.org/standard/7866.html
  21. https://www.iso.org/standard/1141.html
  22. https://asq.org/quality-resources/iso-9000
  23. https://asq.org/quality-resources/z14-z19
  24. https://qualityinspection.org/inspection-level/
  25. https://pppars.com/wp-content/uploads/2021/07/ISO-2859-1-1999.pdf
  26. https://www.hqts.com/aql-calculator/
  27. https://blog.hnpuka.com/understanding-acceptable-quality-limit/
  28. https://www.fastercapital.com/content/Six-Sigma--Six-Sigma--The-Synergy-with-Acceptable-Quality-Level-Explained.html
  29. https://quality.eleapsoftware.com/glossary/acceptance-quality-limit-aql-guide-standards-and-best-practices/
  30. https://www.aiag.org/expertise-areas/quality/iatf-16949-2016
  31. https://www.testcoo.com/en/blog/aql-vs-full-inspection-which-quality-control-method-is-right-for-you
  32. https://www.intouch-quality.com/blog/when-to-perform-a-100-quality-control-inspection-vs.-aql-sampling
  33. https://www.hqts.com/2-5-aql/
  34. https://www.asiaqualityfocus.com/en/about/inspection-level-and-selecting-an-aql-acceptable-quality-level-for-product-inspection/
  35. https://www.textilecoach.net/post/aql-acceptable-quality-level-in-garment-industry
  36. https://www.sgs.com/en/services/final-random-inspection
  37. https://medium.com/inspec-by-bv/aql-sampling-a-beginners-primer-on-using-aql-charts-for-product-quality-inspection-608d8fd2c27f
  38. https://www.onesilq.com/blog/apparel-aql-inspection
  39. https://www.vinmake.com/insights/exports-from-the-garment-industry-recover-ffc4c-s5gtk-zpack
  40. https://aqiservice.com/product-testing/european-reach-compliance/
  41. https://smartersolutions.com/resources/acceptable-quality-level-issues-and-resolution/
  42. https://blog.johner-institute.com/quality-management-iso-13485/aql-acceptance-quality-level/
  43. https://www.fda.gov/about-fda/fda-commissioner/sca-pharmaceuticals-llc-windsor-ct-483-response-issued-11102023
  44. https://asqasktheexperts.org/2012/07/24/iso-2859-3-skip-lot-sampling-5-1-1-5-2-1/
  45. https://digital-library.theiet.org/doi/pdf/10.1049/icp.2025.0117
  46. https://webstore.ansi.org/standards/asq/ansiasqz12008
  47. https://pmc.ncbi.nlm.nih.gov/articles/PMC10255992/
  48. https://www.cqeacademy.com/wp-content/uploads/2019/03/03.-Acceptance-Sampling_Pareto.pdf
  49. https://scholarworks.sjsu.edu/cgi/viewcontent.cgi?article=5000&context=etd_theses
Add your contribution
Related Hubs
User Avatar
No comments yet.