Integrating Human Factors Models into Statistical Quality Control

1976 ◽  
Vol 20 (1) ◽  
pp. 1-5 ◽  
Author(s):  
C. G. Drury
Keyword(s):  
Quality Control ◽  
Operating Characteristic ◽  
Statistical Quality Control ◽  
Control Field ◽  
Sampling Plans ◽  
Statistical Quality ◽  
Input Quality ◽  
Human Inspection

Recent progress in the Statistical Quality Control field has led to the design of Sampling plans which do not assume perfect inspection. Simple methods now exist for analyzing the effect of inspector error on the operating characteristic (OC) curve of a plan and further for re-designing the plan so that a predetermined OC curve is obtained. However, the usual assumption made about human inspection error is that it is constant. Many studies show that Type 1 and Type 2 inspector error change systematically with many variables such as input quality, complexity of item inspected, type of fault, standards, individual differences, etc. This paper develops a methodology for including an explicit human inspector model into the sampling plan design. A particular model integrating visual search and decision making (proposed earlier by the author) is used to demonstrate the feasibility of including explicit human inspector data in the design process. The applications of this model to single and double sampling plans are discussed, together with evidence for the validity of the model under laboratory and field conditions.

Integrating Human Factors Models into Statistical Quality Control

1978 ◽  
Vol 20 (5) ◽  
pp. 561-572 ◽  
Author(s):  
Colin G. Drury
Keyword(s):  
Quality Control ◽  
Operating Characteristic ◽  
Statistical Quality Control ◽  
Control Field ◽  
Sampling Plans ◽  
Statistical Quality ◽  
Input Quality ◽  
Human Inspection

Recent progress in the statistical quality control field has led to the design of sampling plans which do not assume perfect inspection. Simple methods now exist for analyzing the effect of inspector error on the operating characteristic (OC) curve of a plan and further for redesigning the plan so that a predetermined OC curve is obtained. However, the usual assumption made about human inspection error is that it is constant. Many studies show that Type 1 and Type 2 inspector errors change systematically with many variables such as input quality, complexity of item inspected, type of fault, standards, individual differences, etc. This paper develops a methodology for including an explicit human inspector model into the sampling plan design. A particular model integrating visual search and decision making (proposed earlier by the author) is used to demonstrate the feasibility of including explicit human inspector data in the design process. The applications of this model to single and double sampling plans are discussed, together with evidence for the validity of the model under laboratory and field conditions.

Reference-mean-centered statistical quality control

2020 ◽  
Vol 58 (9) ◽  
pp. 1517-1523
Author(s):  
Martín Yago ◽  
Carolina Pla
Keyword(s):  
Quality Control ◽  
Measurement Procedure ◽  
Statistical Quality Control ◽  
Measuring Instruments ◽  
Control Procedure ◽  
Control Material ◽  
Statistical Quality ◽  
Maximum Benefit ◽  
Control Procedures

AbstractBackgroundStatistical quality control (SQC) procedures generally use rejection limits centered on the stable mean of the results obtained for a control material by the analyzing instrument. However, for instruments with significant bias, re-centering the limits on a different value could improve the control procedures from the viewpoint of patient safety.MethodsA statistical model was used to assess the effect of shifting the rejection limits of the control procedure relative to the instrument mean on the number of erroneous results reported as a result of an increase in the systematic error of the measurement procedure due to an out-of-control condition. The behaviors of control procedures of type 1ks (k = 2, 2.5, 3) were studied when applied to analytical processes with different capabilities (σ = 3, 4, 6).ResultsFor measuring instruments with bias, shifting the rejection limits in the direction opposite to the bias improves the ability of the quality control procedure to limit the risk posed to patients in a systematic out-of-control condition. The maximum benefit is obtained when the displacement is equal to the bias of the instrument, that is, when the rejection limits are centered on the reference mean of the control material. The strategy is sensitive to error in estimating the bias. Shifting the limits more than the instrument’s bias disproportionately increases the risk to patients. This effect should be considered in SQC planning for systems running the same test on multiple instruments.ConclusionsCentering the control rule on the reference mean is a potentially useful strategy for SQC planning based on risk management for measuring instruments with significant and stable uncorrected bias. Low uncertainty in estimating bias is necessary for this approach not to be counterproductive.

Statistical Quality Control of HIV-1 ELISA Test Performance

1994 ◽  
Vol 89 (428) ◽  
pp. 1200-1208 ◽  
Author(s):  
R. C. Gentleman ◽  
M. S. Hamada ◽  
D. E. Matthews ◽  
A. R. Wilson
Keyword(s):  
Quality Control ◽  
Test Performance ◽  
Elisa Test ◽  
Statistical Quality Control ◽  
Statistical Quality ◽  
Hiv 1

Inspection Efficiency

1945 ◽  
Vol 152 (1) ◽  
pp. 69-75
Author(s):  
J. C. Edwards ◽  
W. A. Bennett
Keyword(s):  
Quality Control ◽  
Personnel Selection ◽  
Statistical Quality Control ◽  
Statistical Quality ◽  
Time And Motion Study ◽  
Time And Motion ◽  
Motion Study

The purpose of the paper is to outline the numerous directions in which improvements can be sought in engineering inspection. It shows how direct improvements in efficiency can be effected by carefully planned methods of recording results, including the use of statistical quality control, by adopting the principles of time and motion study in the planning of flow of work through inspection, and in the design of gauging fixtures and the arrangement of gauges. The importance of correct personnel selection and organization is stressed, as is also the avoidance of duplication of inspection. The paper concludes by quoting figures showing the substantial reductions which have been achieved in the authors' company by a progressive application of the methods described over a period of several years.

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