The short version of quality-control fits in a sentence. The long version — which is the one that helps — is below.
This page was last updated on 2025-06-17 and is reviewed periodically as new material appears.
Stage one: Enterprises operate as isolated islands. Stage two: Corporate-level interactions with little operational-level liaison. Stage three: Agile organizations form virtual enterprises, cooperating at both corporate and operational levels. Agile teams work across company partners. A virtual partnerships enables harnessing and coordination of resources and diverse skills for manufacturing products quickly and facilitates customer involvement in the web of firms. But there are challenges in achieving the 3rd stage. Some key business processes are still poorly understood and ill defined, despite the availability of technology. Furthermore there is a need for techniques to manage companies promoting workforce initiative and performance measures for self-directed, inter-enterprise project teams. The method to operationalize virtual enterprise is different for each scale of company. Big corporations can reorganize business units and refocus on core competences to operate as a virtual enterprise. Small companies can collaborate to deliver quality, scope and scale collectively. SMEs can potentially exploit agile principles thru rapid partnership formation. But this is easier said than done. There is still a lack of clarity on how to become agile, with insufficiently developed mindset, underdeveloped business practices, processes, methods and tools.
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
Weather observation quality control systems verify probability, history, and trends. One of the main and simplest forms of quality control is the check of probability. This check throws out impossible observations, such as the dew point being higher than the temperature or data outside acceptable ranges, such as temperatures over 200 degrees Fahrenheit. Another basic quality control check is to have the data compared to preset geographic extremes, perhaps combined with diurnal variations. However this only flags the data as uncertain because the station could be reporting correctly but there is no way to know. A better way is to correlate with previous observations as well as the other simple checks. This method uses one hour persistence to check the quality of the current observation. This method makes continuity of observations better since the system is able to make better judgments on whether the current observations are bad or not.
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.
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.
Sources: en.wikipedia.org
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.
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:
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.
Sources: en.wikipedia.org
Weather observation quality control systems verify probability, history, and trends. One of the main and simplest forms of quality control is the check of probability. This check throws out impossible observations, such as the dew point being higher than the temperature or data outside acceptable ranges, such as temperatures over 200 degrees Fahrenheit. Another basic quality control check is to have the data compared to preset geographic extremes, perhaps combined with diurnal variations. However this only flags the data as uncertain because the station could be reporting correctly but there is no way to know. A better way is to correlate with previous observations as well as the other simple checks. This method uses one hour persistence to check the quality of the current observation. This method makes continuity of observations better since the system is able to make better judgments on whether the current observations are bad or not.
Pyzdek, T, "Quality Engineering Handbook", 2003, ISBN 0-8247-4614-7 De Feo, J. A., "Juran's Quality Handbook", 2016, ISBN 978-1-25964-361-3 ASTM E105 Standard Practice for Probability Sampling of Materials ASTM E122 Standard Practice for Calculating Sample Size to Estimate, With a Specified Tolerable Error, the Average for Characteristic of a Lot or Process ASTM E141 Standard Practice for Acceptance of Evidence Based on the Results of Probability Sampling ASTM E1402 Standard Terminology Relating to Sampling ASTM E1994 Standard Practice for Use of Process Oriented AOQL and LTPD Sampling Plans ASTM E2234 Standard Practice for Sampling a Stream of Product by Attributes Indexedby AQL Sampling procedures for inspection by attributes, ISO 2859-1:1999 Sampling procedures for inspection by attributes, JIS Z 9015-1:2006 Acceptance Sampling Calculators (SQC Online) (A subscription fee is required to use the calculators. The "free" calculations have locked features.)
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