A New Criterion for the Optimal Software Release Problems: Moving Average Quality Control Chart with Bootstrap Sampling

Author(s):  
Mitsuhiro Kimura ◽  
Takaji Fujiwara
2018 ◽  
Vol 8 (5) ◽  
pp. 3360-3365 ◽  
Author(s):  
N. Pekin Alakoc ◽  
A. Apaydin

The purpose of this study is to present a new approach for fuzzy control charts. The procedure is based on the fundamentals of Shewhart control charts and the fuzzy theory. The proposed approach is developed in such a way that the approach can be applied in a wide variety of processes. The main characteristics of the proposed approach are: The type of the fuzzy control charts are not restricted for variables or attributes, and the approach can be easily modified for different processes and types of fuzzy numbers with the evaluation or judgment of decision maker(s). With the aim of presenting the approach procedure in details, the approach is designed for fuzzy c quality control chart and an example of the chart is explained. Moreover, the performance of the fuzzy c chart is investigated and compared with the Shewhart c chart. The results of simulations show that the proposed approach has better performance and can detect the process shifts efficiently.


2021 ◽  
Vol 66 (1) ◽  
pp. 5-16
Author(s):  
Olga-Ioana Amariei ◽  
Codruța-Oana Hamat ◽  
Alexandru-Victor Amariei

In this paper, a manufacturing process is analyzed, having as quality characteristic the “height of the screw head”, using analyzes and representative diagrams. Based on this case study, the way to solve these types of problems using the Quality Control Chart module of the WinQSB program, as well as the XLSTAT program is presented.


2019 ◽  
Vol 493 ◽  
pp. S512-S513
Author(s):  
M. Vershinina ◽  
N. Steriopolo ◽  
V. Ibragimova

2019 ◽  
Vol 57 (9) ◽  
pp. 1329-1338 ◽  
Author(s):  
Huub H. van Rossum ◽  
Daan van den Broek

Abstract Background New moving average quality control (MA QC) optimization methods have been developed and are available for laboratories. Having these methods will require a strategy to integrate MA QC and routine internal QC. Methods MA QC was considered only when the performance of the internal QC was limited. A flowchart was applied to determine, per test, whether MA QC should be considered. Next, MA QC was examined using the MA Generator (www.huvaros.com), and optimized MA QC procedures and corresponding MA validation charts were obtained. When a relevant systematic error was detectable within an average daily run, the MA QC was added to the QC plan. For further implementation of MA QC for continuous QC, MA QC management software was configured based on earlier proposed requirements. Also, protocols for the MA QC alarm work-up were designed to allow the detection of temporary assay failure based on previously described experiences. Results Based on the flowchart, 10 chemistry, two immunochemistry and six hematological tests were considered for MA QC. After obtaining optimal MA QC settings and the corresponding MA validation charts, the MA QC of albumin, bicarbonate, calcium, chloride, creatinine, glucose, magnesium, potassium, sodium, total protein, hematocrit, hemoglobin, MCH, MCHC, MCV and platelets were added to the QC plans. Conclusions The presented method allows the design and implementation of QC plans integrating MA QC for continuous QC when internal QC has limited performance.


2020 ◽  
Vol 206 ◽  
pp. 01004
Author(s):  
Zhang Fuling ◽  
Zhang Zhuo ◽  
Diao Er-long ◽  
Zhao Meiliang ◽  
Liu Menglin ◽  
...  

【Objective】To ensure that each analysis step is in the monitoring state. The quality control chart is used to control the process of soil organic carbon content determination, and the reasons for drifting or exceeding the allowable value of the result data can be found out in time.【Method】The content of soil organic carbon in quality control samples was determined by instrumental analysis, and the quality control chart was drawn based on the determination data in Excel 2007, which was used for the quality control of the soil organic carbon content determination process.【Result】The control line of the mean control chart was 41.94% ~ 40.51%, and the warning limit was 41. 70% ~ 41. 74%. The control line range of the range control chart is 0.00% ~ 2.75%, and the warning limit is 0.00% ~ 2. 12%.【Conclusion】The quality control chart method is simple to operate,easy to master, and can timely find the abnormal points or abnormal trends of data, which has high application value in test analysis, and can ensure the accuracy of laboratory test results.


Sign in / Sign up

Export Citation Format

Share Document