A Note on the Quality of Statistical Quality Control Procedures

2004 ◽  
pp. 3-14 ◽  
Author(s):  
Elart von Collani
Keyword(s):  
Quality Control ◽  
Statistical Quality Control ◽  
Statistical Quality ◽  
Control Procedures

scholarly journals PENERAPAN STATISTICAL QUALITY CONTROL (SQC) PADA PENGENDALIAN MUTU MINYAK TELON (STUDI KASUS DI PT.X)

AGROINTEK ◽  
2019 ◽  
Vol 13 (1) ◽  
pp. 72
Author(s):  
Andan Linggar Rucitra ◽  
S Fadiah
Keyword(s):  
Quality Control ◽  
Raw Materials ◽  
Control Process ◽  
Statistical Quality Control ◽  
Statistical Quality ◽  
Quality Control Process ◽  
Fishbone Diagram ◽  
Potential Factor ◽  
Measurement Of Success

<p><em>Telon oil is</em><em> one of </em><em> </em><em>the </em><em>traditional medicine in the form of </em><em> </em><em>liquid preparations that serves to provide a sense of warmth to the wearer. PT</em><em>.X</em><em> is one of the companies that produce</em><em> </em><em>telon</em><em> oil</em><em>.</em><em> To maintain</em><em> the quality of telon oil from PT.X</em><em> product</em><em>, required overall quality control that is starting from the quality control of raw materials, quality control process to the quality control of the final product. The purpose of this research is to know the application of Statistical Quality Control (SQC) in controlling the quality of telon oil in PT X. </em><em>F</em><em>inal product</em><em> quality</em><em> become one of the measurement of success of a process, so it needs a good quality control. SQC method used in this research is Pareto Diagram and Cause and Effect Diagram. Pareto diagram is a bar graph </em><em>that </em><em>show the problem based on the order of the number of occurrences of the most number of problems until the least happened. A causal diagram is often called a fishbone diagram, a tool for identifying potential causes of an effect or problem. The result of applying the method indicates that 80% defect is caused by unsuitable volume and on the incompatibility of Expired Date (ED) code. The damage is caused by several factors namely the method, labor, and machine while the most potential factor is the volume conformity to reduce the number of defect products.</em></p>

Standard Operating Procedures (SOPs) for Research in Weed Science

1995 ◽  
Vol 9 (2) ◽  
pp. 397-401 ◽  
Author(s):  
William W. Donald ◽  
Paul H. Schwartz
Keyword(s):  
Quality Control ◽  
Research Program ◽  
Critical Review ◽  
Field Research ◽  
Standard Operating Procedures ◽  
Data Accuracy ◽  
Statistical Quality Control ◽  
Statistical Quality ◽  
Standard Operating ◽  
Control Procedures

Standard operating procedures (SOPs) were developed for repetitive field research tasks to help ensure that instructions were complete and to provide consistency and continuity in the senior author's field research program. SOPs are explicit step-by-step instructions for carrying out experimental tasks that are components of experimental plans. SOPs are not the same as protocols for unique, new experimental plans. However, protocols may incorporate sequences of SOPs, if desired. SOPs are most useful for new workers and when research tasks need to be repeated infrequently in time (e.g., once every 6 mo or less per year). SOPs may help researchers enhance data accuracy, precision, and reproducibility as part of their own statistical quality control procedures. The authors' field-tested SOPs are available on diskette for critical review, modification, and use by interested weed scientists.

scholarly journals Statistical Quality Control for Human Computation and Crowdsourcing

2018 ◽  
Author(s):  
Yukino Baba
Keyword(s):  
Quality Control ◽  
Critical Issue ◽  
Human Computation ◽  
Statistical Quality Control ◽  
Wisdom Of Crowds ◽  
Statistical Quality ◽  
Statistical Approaches ◽  
The Wisdom Of Crowds

Human computation is a method for solving difficult problems by combining humans and computers. Quality control is a critical issue in human computation because it relies on a large number of participants (i.e., crowds) and there is an uncertainty about their reliability. A solution for this issue is to leverage the power of the "wisdom of crowds"; for example, we can aggregate the outputs of multiple participants or ask a participant to check the output of another participant to improve its quality. In this paper, we review several statistical approaches for controlling the quality of outputs from crowds.

Comparison of traditional statistical quality control using commercially available control materials and two patient-based quality control procedures for the ADVIA 120 Hematology System

2018 ◽  
Vol 47 (3) ◽  
pp. 368-376
Author(s):  
Lourdes C. Vanyo ◽  
Kathleen P. Freeman ◽  
Antonio Meléndez-Lazo ◽  
Mariana Teles ◽  
Rafaela Cuenca ◽  
...  
Keyword(s):  
Quality Control ◽  
Statistical Quality Control ◽  
Statistical Quality ◽  
Control Procedures ◽  
Advia 120

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.

scholarly journals Minimization of Casting Defects using SQC Tool

2021 ◽  
Vol 9 (8) ◽  
pp. 1627-1636
Author(s):  
Abhijit M. Mane
Keyword(s):  
Quality Control ◽  
Casting Process ◽  
Statistical Quality Control ◽  
Casting Defects ◽  
Casting Defect ◽  
Statistical Quality ◽  
Analysis Technique ◽  
Quality Control Tool ◽  
Control Tool

Abstract: Casting is most widely used manufacturing technique. During casting process, number of defects in the casting takes place. In this research, Statistical Quality Control tool is used to minimize the defects. Paretro analysis technique is used to find out the defects in the castings. Recommendations are implemented in the casting line. Improved quality of casting and reduction of defects are found after the implementation of SQC tool. Keywords: Casting, Defect, Why-Why analysis, Shift, Manifold

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