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.
Updated 2025-08-15. Numbers and descriptions here follow the published literature rather than marketing material.
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.
Prior to development of AS9100 standards for Quality Management Systems, the U.S. military applied two specifications to supplier quality and inspection programs, respectively, MIL-Q-9858A Quality Program Requirements, and MIL-I-45208A Military Specification: Inspection System Requirements. For years these specifications had represented the basic tenets of the aerospace industry. However, when the U.S. government adopted ISO 9001, it withdrew those two quality standards. Large aerospace companies then began requiring their suppliers to develop quality programs based on ISO 9001. As technologies evolve, and industries become more specialized, it is likely that more updates will be made to these standards and additional specialty Quality Management Systems (QMS) requirements for various industries.
Other automotive engineers include those listed below: Aerodynamics engineers will often give guidance to the styling studio so that the shapes they design are aerodynamic, as well as attractive. Body engineers will also let the studio know if it is feasible to make the panels for their designs. Change control engineers make sure that all of the design and manufacturing changes that occur are organized, managed and implemented... NVH engineers perform sound and vibration testing to prevent loud cabin noises, detectable vibrations, and/or improve the sound quality while the vehicle is on the road.
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.
Sources: en.wikipedia.org
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.
Prior to the Air Pollution Control Act of 1955, little headway was made to initiate this air pollution reform. U.S. cities Chicago and Cincinnati first established smoke ordinances in 1881. In 1904, Philadelphia passed an ordinance limiting the amount of smoke in flues, chimneys, and open spaces. The ordinance imposed a penalty if not all smoke inspections were passed. It was not until 1947 that California authorized the creation of Air Pollution Control Districts in every county of the state.
Sources: en.wikipedia.org
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.
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.
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
Sources: en.wikipedia.org