Quality assurance (QA) is a way of preventing mistakes or defects in manufactured products and avoiding problems when delivering solutions or services to customers; which ISO 9000 defines as "part of quality management focused on providing confidence that quality requirements will be fulfilled". This defect prevention in quality assurance differs subtly from defect detection and rejection in quality control, and has been referred to as a shift left as it focuses on quality earlier in the process.
QA is applied to physical products in pre-production to verify what will be made meets specifications and requirements, and during manufacturing production runs by validating lot samples meet specified quality controls. QA is also applied to software to verify that features and functionality meet business objectives, and that code is relatively bug free prior to shipping or releasing new software products and versions.
Quality assurance comprises administrative and procedural activities implemented in a quality system so that requirements and goals for a product, service or activity will be fulfilled. It is the systematic measurement, comparison with a standard, monitoring of processes and an associated feedback loop that confers error prevention. This can be contrasted with quality control, which is focused on process output.
Two principles included in quality assurance are: "Fit for purpose" (the product should be suitable for the intended purpose); and "right first time" (mistakes should be eliminated). QA includes management of the quality of raw materials, assemblies, products and components, services related to production, and management, production and inspection processes.
Suitable quality is determined by product users, clients or customers, not by society in general. It is not related to cost, and adjectives or descriptors such as "high" and "poor" are not applicable. For example, a low priced product may be viewed as having high quality because it is disposable, whereas another may be viewed as having poor quality because it is not disposable.
- 1 History
- 2 Approaches
- 3 In practice
- 4 See also
- 5 References
- 6 Further reading
Initial efforts to control the quality of production
Royal governments purchasing material were interested in quality control as customers. For this reason, King John of England appointed William Wrotham to report about the construction and repair of ships. Centuries later, Samuel Pepys, Secretary to the British Admiralty, appointed multiple such overseers.
Prior to the extensive division of labor and mechanization resulting from the Industrial Revolution, it was possible for workers to control the quality of their own products. The Industrial Revolution led to a system in which large groups of people performing a specialized type of work were grouped together under the supervision of a foreman who was appointed to control the quality of work manufactured.
During the time of the First World War, manufacturing processes typically became more complex with larger numbers of workers being supervised. This period saw the widespread introduction of mass production and piece work, which created problems as workmen could now earn more money by the production of extra products, which in turn occasionally led to poor quality workmanship being passed on to the assembly lines. To counter bad workmanship, full-time inspectors were introduced to identify, quarantine and ideally correct product quality failures. Quality control by inspection in the 1920s and 1930s led to the growth of quality inspection functions, separately organized from production and large enough to be headed by superintendents.
The systematic approach to quality started in industrial manufacturing during the 1930s, mostly in the U.S., when some attention was given to the cost of scrap and rework. The impact of mass production required during the Second World War made it necessary to introduce an improved form of quality control known as Statistical Quality Control, or SQC. Some of the initial work for SQC is credited to Walter A. Shewhart of Bell Labs, starting with his famous one-page memorandum of 1924..
SQC includes the concept that every production piece cannot be fully inspected into acceptable and non acceptable batches. By extending the inspection phase and making inspection organizations more efficient, it provides inspectors with control tools such as sampling and control charts, even where 100 percent inspection is not practicable. Standard statistical techniques allow the producer to sample and test a certain proportion of the products for quality to achieve the desired level of confidence in the quality of the entire batch or production run.
In the period following World War II, many countries' manufacturing capabilities that had been destroyed during the war were rebuilt. General Douglas MacArthur oversaw the re-building of Japan. During this time, General MacArthur involved two key individuals in the development of modern quality concepts: W. Edwards Deming and Joseph Juran. Both individuals promoted the collaborative concepts of quality to Japanese business and technical groups, and these groups utilized these concepts in the redevelopment of the Japanese economy.
Although there were many individuals trying to lead United States industries towards a more comprehensive approach to quality, the U.S. continued to apply the Quality Control (QC) concepts of inspection and sampling to remove defective product from production lines, essentially ignoring advances in QA for decades.
A valuable process to perform on a whole consumer product is failure testing or stress testing. In mechanical terms this is the operation of a product until it fails, often under stresses such as increasing vibration, temperature, and humidity. This exposes many unanticipated weaknesses in a product, and the data is used to drive engineering and manufacturing process improvements. Often quite simple changes can dramatically improve product service, such as changing to mold-resistant paint or adding lock-washer placement to the training for new assembly personnel.
Statistical control is based on analyses of objective and subjective data. Many organizations use statistical process control as a tool in any quality improvement effort  to track quality data. Any product can be statistically charted as long as they have a common cause variance or special cause variance to track.
Walter Shewart of Bell Telephone Laboratories recognized that when a product is made, data can be taken from scrutinized areas of a sample lot of the part and statistical variances are then analyzed and charted. Control can then be implemented on the part in the form of rework or scrap, or control can be implemented on the process that made the part, ideally eliminating the defect before more parts can be made like it.
Total quality management
The quality of products is dependent upon that of the participating constituents, some of which are sustainable and effectively controlled while others are not. The process(es) which are managed with QA pertain to Total Quality Management.
If the specification does not reflect the true quality requirements, the product's quality cannot be guaranteed. For instance, the parameters for a pressure vessel should cover not only the material and dimensions but operating, environmental, safety, reliability and maintainability requirements.
Models and standards
ISO 17025 is an international standard that specifies the general requirements for the competence to carry out tests and or calibrations. There are 15 management requirements and 10 technical requirements. These requirements outline what a laboratory must do to become accredited. Management system refers to the organization's structure for managing its processes or activities that transform inputs of resources into a product or service which meets the organization's objectives, such as satisfying the customer's quality requirements, complying with regulations, or meeting environmental objectives.
The Capability Maturity Model Integration (CMMI) model is widely used to implement Process and Product Quality Assurance (PPQA) in an organization. The CMMI maturity levels can be divided into 5 steps, which a company can achieve by performing specific activities within the organization.
During the 1980s, the concept of "company quality" with the focus on management and people came to the fore. It was realized that, if all departments approached quality with an open mind, success was possible if the management led the quality improvement process.
The company-wide quality approach places an emphasis on four aspects :-
- Elements such as controls, job management, adequate processes, performance and integrity criteria and identification of records
- Competence such as knowledge, skills, experiences, qualifications
- Soft elements, such as personnel integrity, confidence, organizational culture, motivation, team spirit and quality relationships.
- Infrastructure (as it enhances or limits functionality)
The quality of the outputs is at risk if any of these aspects is deficient.
QA is not limited to the manufacturing, and can be applied to any business or non-business activity, including: design, consulting, banking, insurance, computer software development, retailing, investment, transportation, education, and translation.
It comprises a quality improvement process, which is generic in the sense that it can be applied to any of these activities and it establishes a behavior pattern, which supports the achievement of quality.
In manufacturing and construction activities, these business practices can be equated to the models for quality assurance defined by the International Standards contained in the ISO 9000 series and the specified Specifications for quality systems.
In the system of Company Quality, the work being carried out was shop floor inspection which did not reveal the major quality problems. This led to quality assurance or total quality control, which has come into being recently.
QA is very important in the medical field because it helps to identify the standards of medical equipments and services. Hospitals and laboratories make use of external agencies in order to ensure standards for equipment such as X-ray machines, Diagnostic Radiology and AERB.
Software Quality Assurance consists of a means of monitoring the software engineering processes and methods used to ensure quality. The methods by which this is accomplished are many and varied, and may include ensuring conformance to one or more standards, such as ISO 9000 or a model such as CMMI. In addition, enterprise quality management software is used to correct issues such as: supply chain disaggregation and regulatory compliance which are vital among medical device manufacturers.
Using contractors and/or consultants
Consultants and contractors are sometimes employed when introducing new quality practices and methods, particularly where the relevant skills and expertise are not available within the organization or when allocating the available internal resources are not available. Consultants and contractors will often employ Quality Management Systems (QMS), auditing and procedural documentation writing CMMI, Six Sigma, Measurement Systems Analysis (MSA), Quality Function Deployment (QFD), Failure Mode and Effects Analysis (FMEA), and Advance Product Quality Planning (APQP).
- Best practice
- Data quality
- Data integrity
- Farm assurance
- GxP, a general term for Good Practice quality guidelines and regulations
- ISO 9000
- Mission assurance
- Program assurance
- Quality engineering
- Quality infrastructure
- Quality management
- Quality management system
- Ringtest, part of a quality assurance program in which identical samples are analyzed by different laboratories
- Software testing
- Software quality assurance
- Total quality management
- Verification and validation
- ISO 9000:2005, Clause 3.2.11
- Larry, Smith (2001). "Shift-Left Testing".<templatestyles src="Module:Citation/CS1/styles.css"></templatestyles>
- ASQ Definition
- The Marketing Accountability Standards Board (MASB) endorses this definition as part of its ongoing Common Language in Marketing Project.
- ASQ – History of Quality. Accessed 17 November 2014
- Evans, James R. (1994) Introduction to Statistical Process Control, Fundamentals of Statistical Process Control, pp 1–13
- Statistical Process Control (SPC) – ASQ
- Thareja, Mannu; Thareja, Priyavrat (February 2007). "The Quality Brilliance Through Brilliant People". Quality World. 4 (2). Retrieved 2010-01-11.<templatestyles src="Module:Citation/CS1/styles.css"></templatestyles>
- "Managing Quality Across the Enterprise: Enterprise Quality Management Solution for Medical Device Companies". Sparta Systems. 2015-02-02.<templatestyles src="Module:Citation/CS1/styles.css"></templatestyles>
- The Quality Assurance Journal, ISSN: 1087-8378, John Wiley & Sons
- QP – Quality Progress magazine, Published by the American Society for Quality
- Quality Assurance in Education, ISSN 0968-4883, Emerald Publishing Group
- Accreditation and Quality Assurance: Journal for Quality, Comparability and Reliability in Chemical Measurement, ISSN: 0949-1775 Print, ISSN: 1432-0517
- Food Quality and Preference, ISSN: 0950-3293
- Asigurarea Calitatii - Quality Assurance, ISSN: 1224–5410
- Almeida, E., Alvaro, A., Meria, S. (2007, September 3–4). A Component quality assurance process. Foundations of Software Engineering, doi: http://doi.acm.org/10.1145/1295074.1295093
- Feldman, S. (2005, February). Quality assurance: much more than testing. Queue, 3(1), doi: http://doi.acm.org/10.1145/1046931.1046943
- Meisinger, M., Wagner, S. (2006, November 6). Integrating a Model of Analytical Quality Assurance into the V-Modell XT. Foundations of Software Engineering, 38-45.doi: http://doi.acm.org/10.1145/1188895.1188906
- Majcen N., Taylor P. (Editors): Practical examples on traceability, measurement uncertainty and validation in chemistry, Vol 1; ISBN 978-92-79-12021-3, 2010.
- Pyzdek, T, "Quality Engineering Handbook", 2003, ISBN 0-8247-4614-7
- Godfrey, A. B., "Juran's Quality Handbook", 1999, ISBN 0-07-034003-X
- Marselis, R. & Roodenrijs, E. "the PointZERO vision", 2012, ISBN 978-90-75414-55-4
- da Silva, R.B., Bulska, E., Godlewska-Zylkiewicz, B., Hedrich, M., Majcen, N., Magnusson, B., Marincic, S., Papadakis, I., Patriarca, M., Vassileva, E., Taylor, P., Analytical measurement: measurement uncertainty and statistics;ISBN 978-92-79-23070-7, 2012.