Economic Quality Control of Manufactured Product

Shewhart, W. A. (1930). Economic Quality Control of Manufactured Product. Bell System Technical Journal, 364-389.

Type

Journal article

Intellectual base

Primary research

Year

1930

DOI

10.1002/j.1538-7305.1930.tb00373.x

Link

https://asq.org/about-asq/honorary-members/shewhart

Cited from

data_science, engineering_design, psychology, statistics_experimental_design, systems_cybernetics

Cited by

10 citations across 10 artifacts.

Each citation links to the sentence it supports in the citing article.

Primes

• Baseline Deviation
  • In statistics it is outlier detection by z-score or studentized residual and control-chart points outside three sigma.
• Clustering Illusion
  • Manufacturing QC — control-chart run-rules are an institutionalized defense, flagging only clusters that exceed what random variation would produce.

    This sourceFounds statistical process control and control charts, whose run-rules flag only clusters exceeding what random variation would produce.

• Monitoring
  • The underlying structure is the same: define baselines, collect signals, compare against thresholds, interpret noise, and decide whether to intervene, a pattern Shewhart (1931) first systematized in his economic-control framework for manufacturing.

    This sourceD. Van Nostrand Company. Founding text of statistical process control; develops the control chart as a procedure for distinguishing common-cause variation (within spec) from special-cause variation (out of spec), the canonical realization of monitoring-as-verification at scale.

• Quality Control
  • It separates conforming items (acceptable for release) from non-conforming items (rejected or reworked), and names the decision boundary and the corrective feedback, a structural insight Shewhart (1931) introduced in his foundational treatment of economic control of manufactured quality.

    This sourceD. Van Nostrand Company. Founding text of statistical process control; develops the control chart as a procedure for distinguishing common-cause variation (within spec) from special-cause variation (out of spec), the canonical realization of monitoring-as-verification at scale.

• Residual Analysis
  • Quality control: variation left after accounting for known inputs is mined for assignable causes, distinguishing common-cause from special-cause variation by reading residual patterns.

    This sourceDistinguishes common-cause from special-cause (assignable) variation by reading the variation left after known inputs.

• Variability
  • Signal extraction depends on characterizing noise variability: by measuring the baseline noise floor, real effects become visible against it (signal-to-noise ratio, effect sizes relative to natural variation)

    This sourceD. Van Nostrand Company. Founding text of statistical process control; develops the control chart as a procedure for distinguishing common-cause variation (within spec) from special-cause variation (out of spec), the canonical realization of monitoring-as-verification at scale.

• Verification
  • The two halves are both real components of QC and the multi-parent edge captures that honestly — a pairing of conformance-checking against fixed standards (Juran, 1951) with statistical process monitoring (Shewhart, 1931).

    This sourceD. Van Nostrand Company. Founding text of statistical process control; develops the control chart as a procedure for distinguishing common-cause variation (within spec) from special-cause variation (out of spec), the canonical realization of monitoring-as-verification at scale.

Domain-specific

• Natural Process Variation
  • Shewhart's key distinction is operational: a process in control exhibits a pattern sufficiently consistent to support prediction within limits, whereas assignable or special causes produce evidence that the causal system has changed.

Mechanisms

• Statistical Process Control
  • Its defining move is separating common-cause variation (the ordinary scatter of a stable process) from special-cause variation (a real signal worth acting on).

    This sourceDistinguishes stable common-cause variation from assignable or special-cause departures that signal a loss of statistical control and warrant action.

• Window Drift Control Chart
  • Limits set too tight turn the chart into a noise-chaser

    This sourceExplains that control-limit selection balances false searches for nonexistent trouble against the risk of overlooking real trouble.

Verification

This reference passed the adversarial substantiation pipeline: it was checked to exist and to support the claim it is attached to. See how references were verified.

Links previously used in the corpus

Before the registry existed this work was also linked 3 other ways.

• https://archive.org/details/economiccontrolo0000shew ×2
• https://archive.org/details/in.ernet.dli.2015.150272 ×1
• https://search.worldcat.org/title/Economic-control-of-quality-of-manufactured-product/oclc/1045408 ×1

Registry ID ref:955db95a93f2 · see in the full table