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Quality-control — 2026 Update

By Editorial Desk · published 2025-04-01 · last reviewed 2025-04-30 · Blog

If you have been reading about quality-control and want a single page that covers the useful parts, this is it: definitions, context, how it is studied, and the questions that come up repeatedly.

Last reviewed on 2025-04-30. Where a claim depends on a specific study, the study is described rather than over-claimed.

Supporting material

Agile Manufacturing is a modern production approach that enables companies to respond swiftly and flexibly to market changes while maintaining quality and cost control. This methodology is designed to create systems that can adapt dynamically to changing customer demands and external factors such as market trends or supply chain disruptions. It is mostly related to lean manufacturing. While Lean Manufacturing focuses primarily on minimizing waste and increasing efficiency, Agile Manufacturing emphasizes adaptability and proactive responses to change. The two approaches are complementary and can be combined into a “leagile” system, which balances cost efficiency with flexibility. The principles of Agile Manufacturing, with its focus on flexibility, responsiveness to change, collaboration, and delivering customer value, serve as a foundation for the later development of Agile Software Development.

A/B tests are sensitive to variance; they require a large sample size in order to reduce standard error and produce a statistically significant result. In applications in which active users are abundant, such as with popular online social-media platforms, obtaining a large sample size is trivial. In other cases, large sample sizes are obtained by increasing the experiment enrollment period. However, using a technique coined by Microsoft as Controlled Experiment Using Pre-Experiment Data (CUPED), variance from before the experiment start can be taken into account so that fewer samples are required to produce a statistically significant result. Because of its nature as an experiment, running an A/B test introduces the risk of wasted time and resources if the test produces unwanted or unhelpful results. In December 2018, representatives with experience in large-scale A/B testing from 13 organizations (Airbnb, Amazon, Booking.com, Facebook, Google, LinkedIn, Lyft, Microsoft, Netflix, Twitter, Uber and Stanford University) summarized the top challenges in a paper. The challenges were grouped into four areas: analysis, engineering and culture, deviations from traditional A/B tests and data quality.

After graduating in 2006, Smith became a graduate assistant for North Carolina. Smith began his NFL coaching career in 2007, when he became the defensive quality control coach for the Washington Redskins. His father, FedEx founder Frederick W. Smith, was a minority owner of the team. Smith would stay at that position through 2008. In 2010, Smith was hired as a defensive intern and administrative assistant for Ole Miss.

AS9000 (1997) Aerospace Basic Quality System Standard As aerospace suppliers soon found that ISO 9001 (1994) did not address the specific requirements of their customers, including the DoD, NASA, FAA, and commercial aerospace companies including Boeing, Lockheed Martin, Northrop Grumman, GE Aircraft Engines and Pratt & Whitney, they developed AS9000, based on ISO 9001, to provide a specific quality management standard for the aerospace industry. Prior to the adoption of an aerospace specific quality standard, various corporations typically used ISO 9001 and their own complementary quality documentation/requirements, such as Boeing's D1-9000 or the automotive Q standard. This created a patchwork of competing requirements that were difficult to enforce and/or comply with. The major American aerospace manufacturers collaborated to develop a unified quality standard based on ISO 9001:1994, which led to the creation of AS9000. Following its release, companies like Boeing discontinued their previous quality supplements in favor of complying with AS9000.

April III was defensive quality control coach for the Philadelphia Eagles from 2011 to 2012. In 2013, April III moved to the New York Jets as defensive quality control coach and assistant linebacker coach. In 2014, he was promoted to linebackers coach with the Jets. In 2015, April III moved with Rex Ryan from the Jets to the Buffalo Bills and continued to serve as linebackers coach through 2016. On February 1, 2026, April III returned to the Buffalo Bills as the team's new outside linebackers coach, under head coach Joe Brady. April III had accepted a position to be the defensive ends coach with Minnesota in the college ranks only a month earlier. April earned his bachelor's degree in sports management from Louisiana-Lafayette. He is the son of Bobby April Jr., a former NFL and college special teams coordinator. Buffalo Bills profile Wisconsin profile

Sources: en.wikipedia.org

Notes from published material

Greater emphasis on risk management Introduces “Special Requirements” Introduces “Critical Items” Measure: Requirements conformance Measure: Delivery performance Adopt proven product development processes Eliminate “recurring corrective actions” AS9100 Revision C was released in January, 2009, with considerable delay in application of the new version in audits, largely due to the delay in the release of AS9101 Revision D and auditor training to the increased auditing rigor of that update. AS9100 Revision D (2016), Quality Management System – Requirements for Aviation, Space and Defense Organizations The update of AS9100 from revision C to D includes the full text of ISO 9001:2015. In addition to aligning the structure of the aviation, space and defense requirements to the new structure of ISO 9001:2015, the following key changes were implemented:

failure mode and effects analysis (FMEA) manual statistical process control (SPC) manual measurement systems analysis (MSA) manual production part approval process (PPAP) manual APQP serves as a guide in the development process and also a standard way to share results between suppliers and automotive companies. APQP specifies three phases: Development, Industrialization, and Product Launch. Through these phases, 23 main topics will be monitored. These topics must be completed before the production is started. They include the following aspects: design robustness, design testing, and specification compliance, production process design, quality inspection standards, process capability, production capacity, product packaging, product testing, and operator training plan. These activities are sometimes carried out by third-party inspection and quality control companies such as SGS, Bureau Veritas, or QCADvisor, which provide on-site inspections, audits, and testing services to support APQP compliance. APQP focuses on:

Advanced product quality planning (APQP) is a framework of procedures and techniques used to develop products in industry, particularly in the automotive industry. It differs from Six Sigma in that the goal of Six Sigma is to reduce variation but has similarities to Design for Six Sigma (DFSS). According to the Automotive Industry Action Group (AIAG), the purpose of APQP is "to produce a product quality plan which will support development of a product or service that will satisfy the customer." It is a product development process employed by General Motors, Ford, Chrysler, and their suppliers.

The first National Air Pollution Symposium in the United States was held in 1949 and hosted by Stanford Research Institute (now SRI International). At first, smaller governments were responsible for the passage and enforcement of such legislation. The main purpose of the Air Pollution Control Act of 1955 was to provide research assistance to find a way to control air pollution from its source. A total of $5 million was granted to the public health service for a five-year period to conduct this research. According to a private website, the amount was $3 million allotted per year for the five-year period of research.

Two-sample hypothesis tests are appropriate for comparing the two samples in which the samples are divided by the two control cases in the experiment. Z-tests are appropriate for comparing means under stringent conditions regarding normality and a known standard deviation. Student's t-tests are appropriate for comparing means under relaxed conditions when less is assumed. Welch's t-test assumes the least and is therefore the most commonly used two-sample hypothesis test in which the mean of a metric is to be optimized. While the mean of the variable to be optimized is the most common choice of estimator, others are regularly used. Fisher's exact test can be employed to compare two binomial distributions, such as a click-through rate.

Sources: en.wikipedia.org

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