Quality control

"TQC" redirects here. For other uses, see TQC (disambiguation).

Short description: Processes that maintain quality at a constant level

Quality inspector in a Volkseigener Betrieb sewing machine parts factory in Dresden, East Germany, 1977 Munitions workers inspecting 3-inch mortar bombs, 1943

Quality control (QC) is a process by which entities review the quality of all factors involved in production. ISO 9000 defines quality control as "a part of quality management focused on fulfilling quality requirements".[1]

This approach places emphasis on three aspects (enshrined in standards such as ISO 9001):[2][3]

1. Elements such as controls, job management, defined and well managed processes,[4][5] performance and integrity criteria, and identification of records
2. Competence, such as knowledge, skills, experience, and qualifications
3. Soft elements, such as personnel, integrity, confidence, organizational culture, motivation, team spirit, and quality relationships.

Inspection is a major component of quality control, where physical product is examined visually (or the end results of a service are analyzed). Product inspectors will be provided with lists and descriptions of unacceptable product defects such as cracks or surface blemishes for example.[3]

## Contents

* 1 History and introduction
* 2 Notable approaches
* 3 In project management
* 4 See also
* 5 References
* 6 Further reading
* 7 External links

## History and introduction

See also: History:History of technology

Early stone tools such as anvils had no holes and were not designed as interchangeable parts. Mass production established processes for the creation of parts and system with identical dimensions and design, but these processes are not uniform and hence some customers were unsatisfied with the result. Quality control separates the act of testing products to uncover defects from the decision to allow or deny product release, which may be determined by fiscal constraints.[6] For contract work, particularly work awarded by government agencies, quality control issues are among the top reasons for not renewing a contract.[7]

The simplest form of quality control was a sketch of the desired item. If the item did not match the sketch, the item was rejected, in a simple Go/no go procedure. However, manufacturers soon found it was difficult and costly to make parts be exactly like their depiction; hence around 1840 tolerance limits were introduced, wherein a design would function if its parts were measured to be within the limits. Quality was thus precisely defined using devices such as plug gauges and ring gauges. However, this did not address the problem of defective items; recycling or disposing of the waste adds to the cost of production, as does trying to reduce the defect rate. Various methods have been proposed to prioritize quality control issues and determine whether to leave them unaddressed or use quality assurance techniques to improve and stabilize production.[6]

## Notable approaches

There is a tendency for individual consultants and organizations to name their own unique approaches to quality control—a few of these have ended up in widespread use:

Terminology | Approximate year of first use | Description

|
|

Statistical process control (SPC) | 1960s | The use of control charts to monitor an individual industrial process and feed back performance to the operators responsible for that process; inspired by control systems
Total quality management (TQM) | 1985 | Quality movement originating in the United States Department of Defense that uses (in part) the techniques of statistical quality control to drive continuous organizational improvement[8]
Six Sigma (6σ) | 1986 | Statistical quality control applied to business strategy;[9] originated by Motorola
Lean Six Sigma (L6σ) | 2001 | Six Sigma applied with the principles of lean manufacturing and/or lean enterprise; originated by Wheat et al.[10]

## In project management

In project management, quality control requires the project manager and/or the project team to inspect the accomplished work to ensure its alignment with the project scope.[11] In practice, projects typically have a dedicated quality control team which focuses on this area.[12]

## See also

* Analytical quality control
* Corrective and preventative action (CAPA)
* Eight dimensions of quality
* First article inspection (FAI)
* Good automated manufacturing practice (GAMP)
* Good manufacturing practice
* Quality assurance
* Quality management framework
* Standard operating procedure (SOP)
* QA/QC

## References

1. ↑ ISO 9000:2005, Clause 3.2.10
2. ↑ Praxiom Research Group Limited (16 August 2017). "ISO 9001 Translated Into Plain English". Praxiom Research Group Limited. http://www.praxiom.com/iso-9001.htm.
3. ↑ 3.0 3.1 Aft, L.S. (1997). "Chapter 1: Introduction". Fundamentals of Industrial Quality Control. CRC Press. pp. 1–17.
4. ↑ Dennis Adsit (November 9, 2007). "What the Call Center Industry Can Learn from Manufacturing: Part I". National Association of Call Centers. http://www.nationalcallcenters.org/images/stories/InQueue/vol2no21.pdf.
5. ↑ Dennis Adsit (November 23, 2007). "What the Call Center Industry Can Learn from Manufacturing: Part II". National Association of Call Centers. http://www.nationalcallcenters.org/images/stories/InQueue/Vol2No22.pdf.
6. ↑ 6.0 6.1 Shewhart, Walter A. (Walter Andrew); Deming, W. Edwards (William Edwards) (1939). Statistical method from the viewpoint of quality control. Washington: The Graduate School, The Department of Agriculture. pp. 1–5. https://archive.org/stream/CAT10502416#page/2/mode/2up.
7. ↑ "Position Classification Standard for Quality Assurance Series, GS-1910". US Office of Personnel Management. March 1983. http://www.opm.gov/fedclass/gs1910.pdf.
8. ↑ Evans, James R.; Lindsay, William M. (1999), The Management and Control of Quality (4 ed.), Cincinnati, Ohio: South-Western College Publications, p. 118, ISBN 9780538882422, OCLC 38475486, https://archive.org/details/managementcontro0004evan/page/118, "The term total quality management, or TQM, has been commonly used to denote the system of managing for total quality. (The term TQM was actually developed within the Department of Defense. It has since been renamed Total Quality Leadership, since leadership outranks management in military thought.)"
9. ↑ "What Is Six Sigma?". Schaumburg, Illinois: Motorola University. 2010-02-19. p. 2. http://www.motorolasolutions.com/web/Business/_Moto_University/_Documents/_Static_Files/What_is_SixSigma.pdf. "When practiced as a management system, Six Sigma is a high performance system for executing business strategy."
10. ↑ Wheat, B.; Mills, C.; Carnell, M. (2001). Leaning into Six Sigma: The Path to integration of Lean Enterprise and Six Sigma. Publishing Partners. pp. 100. ISBN 9780971249103.
11. ↑ Phillips, Joseph (November 2008). "Quality Control in Project Management". The Project Management Hut. http://www.pmhut.com/quality-control-in-project-management.
12. ↑ Rose, K.H. (2014). Project Quality Management: Why, What and How. J. Ross Publishing. pp. 224. ISBN 9781604271027.

## Further reading

* Radford, George S. (1922), The Control of Quality in Manufacturing, New York: Ronald Press Co., OCLC 1701274, https://archive.org/details/controlofquality00radf, retrieved 2013-11-16
* Shewhart, Walter A. (1931), Economic Control of Quality of Manufactured Product, New York: D. Van Nostrand Co., Inc., OCLC 1045408
* Juran, Joseph M. (1951), Quality-Control Handbook, New York: McGraw-Hill, OCLC 1220529
* Western Electric Company (1956), Statistical Quality Control Handbook (1 ed.), Indianapolis, Indiana: Western Electric Co., OCLC 33858387
* Feigenbaum, Armand V. (1961), Total Quality Control, New York: McGraw-Hill, OCLC 567344

## External links

* ASTM quality control standards

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Statistics

* Outline
* Index

Descriptive statistics

| Continuous data|

| Center|

* Mean
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* geometric
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* Median
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|

Dispersion|

* Variance
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Shape|

* Central limit theorem
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* Kurtosis
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Count data|

* Index of dispersion

Summary tables|

* Grouped data
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Dependence|

* Pearson product-moment correlation
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Graphics|

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Data collection

| Study design|

* Population
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* Missing data

|

Survey methodology|

* Sampling
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Controlled experiments|

* Scientific control
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Adaptive Designs|

* Adaptive clinical trial
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Observational Studies|

* Cross-sectional study
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Statistical inference

| Statistical theory|

* Population
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* Optimal decision
* loss function
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|

Frequentist inference|

| Point estimation|

* Estimating equations
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* Method of moments
* M-estimator
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* Unbiased estimators
* Mean-unbiased minimum-variance
* Rao–Blackwellization
* Lehmann–Scheffé theorem
* Median unbiased
* Plug-in

|

Interval estimation|

* Confidence interval
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* Prediction interval
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* Resampling
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Testing hypotheses|

* 1- & 2-tails
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Parametric tests|

* Likelihood-ratio
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Specific tests|

* Z-test (normal)
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Goodness of fit|

* Chi-squared
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* Likelihood-ratio test
* Model selection
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Rank statistics|

* Sign
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* Rank sum (Mann–Whitney)
* Nonparametric anova
* 1-way (Kruskal–Wallis)
* 2-way (Friedman)
* Ordered alternative (Jonckheere–Terpstra)

Bayesian inference|

* Bayesian probability
* prior
* posterior
* Credible interval
* Bayes factor
* Bayesian estimator
* Maximum posterior estimator

* Correlation
* Regression analysis

| Correlation|

* Pearson product-moment
* Partial correlation
* Confounding variable
* Coefficient of determination

|

Regression analysis|

* Errors and residuals
* Regression validation
* Mixed effects models
* Simultaneous equations models
* Multivariate adaptive regression splines (MARS)

Linear regression|

* Simple linear regression
* Ordinary least squares
* General linear model
* Bayesian regression

Non-standard predictors|

* Nonlinear regression
* Nonparametric
* Semiparametric
* Isotonic
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* Heteroscedasticity
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Generalized linear model|

* Exponential families
* Logistic (Bernoulli) / Binomial / Poisson regressions

Partition of variance|

* Analysis of variance (ANOVA, anova)
* Analysis of covariance
* Multivariate ANOVA
* Degrees of freedom

Categorical / Multivariate / Time-series / Survival analysis

| Categorical|

* Cohen's kappa
* Contingency table
* Graphical model
* Log-linear model
* McNemar's test

|

Multivariate|

* Regression
* Manova
* Principal components
* Canonical correlation
* Discriminant analysis
* Cluster analysis
* Classification
* Structural equation model
* Factor analysis
* Multivariate distributions
* Elliptical distributions
* Normal

Time-series|

| General|

* Decomposition
* Trend
* Stationarity
* Seasonal adjustment
* Exponential smoothing
* Cointegration
* Structural break
* Granger causality

|

Specific tests|

* Dickey–Fuller
* Johansen
* Q-statistic (Ljung–Box)
* Durbin–Watson
* Breusch–Godfrey

Time domain|

* Autocorrelation (ACF)
* partial (PACF)
* Cross-correlation (XCF)
* ARMA model
* ARIMA model (Box–Jenkins)
* Autoregressive conditional heteroskedasticity (ARCH)
* Vector autoregression (VAR)

Frequency domain|

* Spectral density estimation
* Fourier analysis
* Wavelet
* Whittle likelihood

Survival|

| Survival function|

* Kaplan–Meier estimator (product limit)
* Proportional hazards models
* Accelerated failure time (AFT) model
* First hitting time

|

Hazard function|

* Nelson–Aalen estimator

Test|

* Log-rank test

Applications

| Biostatistics|

* Bioinformatics
* Clinical trials / studies
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* Medical statistics

|

Engineering statistics|

* Chemometrics
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