Economic Control of Quality of Manufactured Product¶
Shewhart, W. A. (1931). Economic Control of Quality of Manufactured Product.
Cited by¶
18 citations across 18 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.
Supported in partVerified against the work's full text
Shewhart supplies the control-chart half of the claim — plotted limits containing ~99.7 per cent of points, with out-of-limit points read as trouble — but says nothing about z-scores or studentized residuals.
“Under controlled conditions, this band should include approximately 99.7 per cent of the Fk;. 78.— Linl of Regression and 99.7 Per Cent Limits.”
- 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.
Supported in partVerified against the work's full text
Shewhart supplies the control-chart principle — criteria for deciding when assignable causes of variation have been removed so production is 'controlled within limits' — but not run-rules or a cluster threshold.
“Based upon evidence such as already presented, it appears feasibkr\o set up criteria by which to determine when assignable causes of variation in quality have been eliminated so that the product may then be considered to be controlled within limits.”
- Manufacturing QC — control-chart run-rules are an institutionalized defense, flagging only clusters that exceed 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.
- 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.
- 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.
Supported in partVerified against the work's full text
Shewhart's text supplies the control limits and the find-and-eliminate-assignable-cause corrective action of economic control, plus the fraction non-conforming measure — not an item-level release/rework decision boundary.
“This book is the natural outgrowth of an investigation started some six years ago to develop a scientific basis for attaining economic control of quality of manufactured product through the establishment of control limits to indicate at every stage in the production process from raw materials to finished product when the quality of product is varying more than is economically desirable.”
- 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.
- 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.
Supported in partVerified against the work's full text
Shewhart's text backs the frame of weeding out assignable causes of variation in quality, but neither the excerpt nor the supplements state a residual-pattern reading of common-cause versus special-cause variation.
“By weeding out assignable causes of variability, the manufacturer goes to the feasible limit in assuring uniform quality.”
- 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.
- 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.
Borderline supportVerified against the work's full text
Shewhart supplies the frame: observed data as samples of chance causes, with control-chart limits built from natural variation as the baseline from which real departures become visible.
“It is at once apparent, therefore, that sampling theory should prove a valuable tool in testing engineering hypotheses.”
Two runs of the same check disagreed on this citation; the later verdict is shown and the citation is queued for review.
- 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)
- 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.
Supported in partVerified against the work's full text
Shewhart's text supplies the statistical-process-control half of the pairing, but not the Juran half or the claim's own multi-parent-edge assertion.
“Upon the basis of Postulate 3, it follows that we can find •feind remove causes of variability until the remaining system of causes is constant or until we reach that state where the probability that the deviations in quality remain within any two fixed limits (Fig. 5) is constant.”
- 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).
Mechanisms¶
- Baseline Context Band
- Shewhart's control chart is the archetypal case: its control limits are a persistent baseline band that turns a lone data point into a signal ("in control" or "out of control") no isolated number could carry.
This sourceDevelops the control chart and control limits as a persistent reference for signaling whether observed process variation remains in control or indicates lack of control.
- Shewhart's control chart is the archetypal case: its control limits are a persistent baseline band that turns a lone data point into a signal ("in control" or "out of control") no isolated number could carry.
- Control-Chart-Triggered Inspection Escalation
- Built on Shewhart's separation of common-cause noise from assignable-cause signals
This sourceD. Van Nostrand Company. Separates stable chance-cause variation from signals that a process has left statistical control.
- Built on Shewhart's separation of common-cause noise from assignable-cause signals
- Golden-Sample Regression Suite
- Treating the golden set as a fixed baseline and charting deviation over time is essentially statistical process control applied to a mapping — separating an in-control mapping from one that has shifted
This sourceDevelops control charts to distinguish stable chance variation from an assignable shift in a process.
- Treating the golden set as a fixed baseline and charting deviation over time is essentially statistical process control applied to a mapping — separating an in-control mapping from one that has shifted
- Management by Exception
- The control chart is the exception filter — it converts a torrent of routine data into silence, and raises a flag only when a point falls outside the limits or a run of points drifts in a suspicious pattern.
This sourceControl charts signal investigation for observations outside control limits and for nonrandom run patterns indicating systematic behavior.
- The control chart is the exception filter — it converts a torrent of routine data into silence, and raises a flag only when a point falls outside the limits or a run of points drifts in a suspicious pattern.
- Manufacturing Batch Trace Analysis
- Statistical process control gives it its backbone: a line that is monitored for its characteristic variation is a line whose imprint can later be recognized.
This sourceBuilds statistical process control around monitoring characteristic process variation and detecting departures from control.
- Statistical process control gives it its backbone: a line that is monitored for its characteristic variation is a line whose imprint can later be recognized.
- Quality Control Chart
- Its strength is that it distinguishes the two kinds of variation a stabilizing loop must never confuse — common-cause noise and special-cause signal — a distinction at the heart of statistical process control.
This sourceBuilds control-chart reasoning around distinguishing stable chance-cause variation from assignable departures from statistical control.
- Its strength is that it distinguishes the two kinds of variation a stabilizing loop must never confuse — common-cause noise and special-cause signal — a distinction at the heart of statistical process control.
- Sensor Health and Drift Monitor
- The control-chart logic it leans on is the classic discipline for separating a real shift from routine variation.
This sourceDevelops control-chart reasoning to distinguish variation attributable to a stable chance-cause system from variation signaling an assignable cause.
- The control-chart logic it leans on is the classic discipline for separating a real shift from routine variation.
- Stage Conversion Anomaly Alert
- Its strength is speed and attention economy: it converts continuous stage metrics into a short stream of this stage just changed signals, applying the logic of statistical process control
This sourceIntroduces statistical control as a method for separating stable chance variation from assignable causes that warrant investigation.
- Its strength is speed and attention economy: it converts continuous stage metrics into a short stream of this stage just changed signals, applying the logic of statistical process control
- 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.
- 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).
- Surprise Threshold Alert
- It is closely related to control-chart practice: flag a special-cause signal, ignore common-cause variation.
This sourceD. Van Nostrand Company, 501 pp.. https://search.worldcat.org/title/1045408 Establishes the control-chart distinction between routine common-cause variation and special-cause signals that warrant investigation.
- It is closely related to control-chart practice: flag a special-cause signal, ignore common-cause variation.
- 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.
- Limits set too tight turn the chart into a noise-chaser
Verification¶
Does it exist? Not checked yet. This entry carries no identifier to resolve. It was extracted from the citation as written in the article, normalized, and deduplicated against the rest of the registry.
Does it back the claim? Read against the text for 6 of 18 citations: 5 supported in part, 1 borderline. Each verdict is shown under its citation below, with what in the work backs the sentence.
Support is checked per citation rather than per work — the same source can be cited soundly in one article and wrongly in another. Per-citation recording began recently, so a citation with no recorded check is a gap in the record rather than evidence it went unchecked.
See how references were verified.
Links previously used in the corpus¶
Before the registry existed this work was also linked 9 other ways.
- https://search.worldcat.org/title/Economic-control-of-quality-of-manufactured-product/oclc/1045408 ×3
- https://archive.org/details/economiccontrolo0000shew ×2
- https://search.worldcat.org/title/1045408 ×2
- https://archive.org/details/in.ernet.dli.2015.150272 ×1
- https://books.google.com/books?id=X9lTAAAAMAAJ ×1
- https://onlinebooks.library.upenn.edu/webbin/book/lookupid?key=ha001115960 ×1
- https://openlibrary.org/books/OL6766665M/Economic_control_of_quality_of_manufactured_product ×1
- https://search.worldcat.org/title/economic-control-of-quality-of-manufactured-product/oclc/001045408 ×1
- https://search.worldcat.org/title/economic-control-of-quality-of-manufactured-product/oclc/1045408 ×1
Registry ID ref:59038a655049 · see in the full table