A practical reference on quality-control: what it is, how it behaves, what the literature reports, and where the honest uncertainties sit.
Reviewed 2025-08-20. Anything still debated is marked as such rather than presented as settled.
The American Society for Quality (ASQ), formerly the American Society for Quality Control (ASQC), is a society of quality professionals, with more than 30,000 members, in more than 140 countries. ASQC was established on 16 February 1946 by 253 members in Milwaukee, Wisconsin, with George D. Edwards as its first president. The organization was first created as a way for quality experts and manufacturers to sustain quality-improvement techniques used during World War II. In 1948, ASQC's Code of Ethics established standards for members to conduct their activities and business. Business writer Armand V. Feigenbaum served as president of the society in 1961–63. In 1997, the members of the organization voted to change its name from "American Society for Quality Control" to "American Society for Quality".
Acceptance sampling uses statistical sampling to determine whether to accept or reject a production lot of material. It has been a common quality control technique used in industry. It is usually done as products leave the factory, or in some cases even within the factory. Most often a producer supplies a consumer with several items and a decision to accept or reject the items is made by determining the number of defective items in a sample from the lot. The lot is accepted if the number of defects falls below where the acceptance number or otherwise the lot is rejected. In general, acceptance sampling is employed when one or several of the following hold: testing is destructive; the cost of 100% inspection is very high; and 100% inspection takes too long. A wide variety of acceptance sampling plans is available. For example, multiple sampling plans use more than two samples to reach a conclusion. A shorter examination period and smaller sample sizes are features of this type of plan. Although the samples are taken at random, the sampling procedure is still reliable.
A/B testing (also known as bucket testing, split-run testing or split testing) is a user-experience research method. A/B tests consist of a randomized experiment that usually involves two variants (A and B), although the concept can be also extended to multiple variants of the same variable. It includes application of statistical hypothesis testing or "two-sample hypothesis testing" as used in the field of statistics. A/B testing is employed to compare multiple versions of a single variable, for example by testing a subject's response to variant A against variant B, and to determine which of the variants is more effective. Multivariate testing or multinomial testing is similar to A/B testing but may test more than two versions at the same time or use more controls. Simple A/B tests are not valid for observational, quasi-experimental or other non-experimental situations—commonplace with survey data, offline data, and other, more complex phenomena.
Studies indicate that a substantial part of the modern vehicle's value comes from intelligent systems, and that these represent most of the current automotive innovation. To facilitate this, the modern automotive engineering process has to handle an increased use of mechatronics. Configuration and performance optimization, system integration, control, component, subsystem and system-level validation of the intelligent systems must become an intrinsic part of the standard vehicle engineering process, just as this is the case for the structural, vibro-acoustic and kinematic design. This requires a vehicle development process that is typically highly simulation-driven.
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.
Sources: en.wikipedia.org
Knowledge includes experiences of people in the organization, company reports, case histories, databases and other repositories In order for organizations to become agile, organizations, they need to focus on building knowledge bases and cultivating a well trained and motivated workforce. Such an organization is driven by knowledge and information available and possessed by the workforce. This epitomizes the notion that `knowledge is power'. "The ability to control the new product introduction process from the conceptualization and design stages through manufacturing to shipment and product support requires the exploitation of a knowledge-rich work force and sophisticated information technology in most industrial sectors"
A/B testing is commonly employed when deploying a newer version of an API. For real-time user experience testing, an HTTP layer 7 reverse proxy is configured in such a way that n% of the HTTP traffic is routed to the newer version of the backend instance, while the remaining 100-n% of HTTP traffic hits the (stable) older version of the backend HTTP application service. This is usually achieved to limit the exposure of customers to a newer backend instance such that, if there is a bug with the newer version, only n% of the total user agents or clients are affected while others are routed to a stable backend, which is a common ingress control mechanism. Adaptive control Between-group design experiment Choice modelling Multi-armed bandit Multivariate testing Randomized controlled trial Scientific control Stochastic dominance Test statistic Two-proportion Z-test
Smith was hired to become the defensive quality control coach for the Tennessee Titans in 2011 under new head coach Mike Munchak. Smith then became the offensive quality coach the following season. In 2013, Smith was promoted to the assistant offensive line and assistant tight ends coach. Munchak was fired after the 2013 season and new head coach Ken Whisenhunt retained Smith as the assistant tight ends coach. Midway through the 2015 season, Whisenhunt was fired and replaced by tight ends coach Mike Mularkey. Mularkey was kept as head coach for the 2016 season and Smith was promoted to the new tight ends coach. When Mularkey was fired after the 2017 season, new head coach Mike Vrabel kept Smith as the tight ends coach for 2018. On January 21, 2019, Smith was promoted to offensive coordinator, replacing Matt LaFleur, who departed to become head coach of the Green Bay Packers two weeks prior. In his first year as offensive coordinator, Smith oversaw the highest-scoring Titans team in 16 years, with Derrick Henry, Ryan Tannehill, and Jonnu Smith having career years. Smith was praised for his play-calling in the Titans' 28–12 road victory over the top-seeded Baltimore Ravens in the AFC Divisional Round. In 2020, the Titans ranked fourth in scoring and second in total yards.
AS9100 Revision A (2001), Model for Quality Assurance in Design, Development, Production, Installation and Servicing During the rewrite of ISO 9001 for the 2000 release, the AS group worked closely with the ISO organization. As the year 2000 revision of ISO 9001 incorporated major organizational and philosophical changes, AS9000 underwent a rewrite as well. It was released as AS9100 to the international aerospace industry at the same time as the new version of ISO 9001. AS9100A was actually two standards referenced in one publication: Section 1 defines an updated QMS model aligned with the updated ISO 9001:2000 publication while Section 2 defines a legacy model aligned with ISO 9001:1994. Organizations that in the year 2001 were operating a QMS based on ISO 9001:1994 were permitted to conform to Section 2 with the expectation that they would then transition their QMS to Section 1.
Sources: en.wikipedia.org
Advanced product quality planning is a process developed in the late 1980s by a commission of experts who gathered around the 'Big Three' of the US automobile industry: Ford, GM, and Chrysler. Representatives from the three automotive original equipment manufacturers (OEMs) and the Automotive Division of American Society for Quality Control (ASQC) created the Supplier Quality Requirement Task Force for developing a common understanding on topics of mutual interest within the automotive industry. This commission worked five years to analyze the then-current automotive development and production status in the US, Europe, and especially in Japan. At the time, the Japanese automotive companies were successful in the US market. APQP is utilized by US automakers and some of their affiliates. Tier 1 suppliers are typically required to follow APQP procedures, techniques, and are also typically required to be audited and registered to IATF 16949. This methodology is also being used in other manufacturing sectors. The Automotive Industry Action Group (AIAG) is a non-profit association of automotive companies founded in 1982. The basis for the process control plan is described in AIAG's APQP manual These include:
Sampling provides one rational means of verification that a production lot conforms to the requirements of technical specifications. 100% inspection does not guarantee 100% compliance and is too time-consuming and costly. Rather than evaluating all items, a specified sample is taken, inspected or tested, and a decision is made about accepting or rejecting the entire production lot. Sampling plans have known risks: an acceptable quality limit (AQL) and a rejectable quality level, such as lot tolerance percent defective (LTDP), are part of the operating characteristic curve of the sampling plan. These are primarily statistical risks and do not necessarily imply that a defective product is intentionally being made or accepted. Plans can have a known average outgoing quality limit (AOQL). A single sampling plan for attributes is a statistical method by which the lot is accepted or rejected on the basis of one sample. Suppose that we have a lot of sizes M {\displaystyle M} ; a random sample of size N < M {\displaystyle N<M} is selected from the lot; and an acceptance number B
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.
Sources: en.wikipedia.org