scholarly journals Monitoring of Cryptosporidium spp. Outbreaks Using Statistical Process Control Tools and Quantitative Risk Analysis Based on NORS Long-term Trending

2017 ◽  
Vol 9 (1) ◽  
pp. 1-7
Author(s):  
Mostafa Essam Eiss
Keyword(s):  
Risk Analysis ◽  
Process Control ◽  
Statistical Process Control ◽  
Statistical Process ◽  
Quantitative Risk Analysis ◽  
Cryptosporidium Spp

scholarly journals Dynamic Risk Analysis in Metro Construction Using Statistical Process Control

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Yichen Wang ◽  
Hong Zheng ◽  
Xinyue Lu
Keyword(s):  
Risk Analysis ◽  
Process Control ◽  
Dynamic Analysis ◽  
Statistical Process Control ◽  
Control Measures ◽  
Risk Level ◽  
Time Interval ◽  
Statistical Control ◽  
Statistical Process ◽  
And Control

Metro construction is normally carried out in complex engineering geological environment, so it can generate various risk events. In the process of metro construction, a scientific risk dynamic analysis is indispensable to reduce and control risks. In order to analyze the risk in metro construction more scientifically and reasonably, in this study, a new risk dynamic analysis method for metro construction is proposed using statistical process control. The method can analyse the risk level according to the process’s capacity index and identify the characteristics of risk variation according to the statistical control chart. The risk level and the characteristic of risks may vary with dynamical updating of monitoring data, so the conclusion of risk evaluation for a time interval can be drawn and corresponding safety measures can be ascertained. The method ushers statistical process control, so the random factors in risk evolution can be considered fully. Then, the method is applied to the risk analysis of shield construction under the Beijing-Tianjin intercity railway in Beijing Metro Line 8, a typical risk problem in the traffic construction. The variation of the risk level and the characteristic of risks can be evaluated reasonably because the dynamical randomness is considered. Moreover, whether risk control measures should be taken and what the effective measures are can be ascertained explicitly.

SU-C-217BCD-04: Application of Statistical Process Control to Long Term CT Constancy Testing

2012 ◽  
Vol 39 (6Part2) ◽  
pp. 3606-3606
Author(s):  
J Fredriksson ◽  
H Olafsdottir ◽  
A Kristbjornsson ◽  
A Healy ◽  
J Levy ◽  
...  
Keyword(s):  
Process Control ◽  
Statistical Process Control ◽  
Statistical Process

APPLICATION OF SPC AND GROWTH MODELS ON THE ECONOMIC TRAJECTORY OF THE LEATHER AND FOOTWEAR INDUSTRY

2020 ◽  
Author(s):  
Mario Lesina ◽  
Lovorka Gotal Dmitrovic
Keyword(s):  
Human Capital ◽  
Process Control ◽  
Statistical Process Control ◽  
Control Charts ◽  
Growth Models ◽  
Pearson Correlation ◽  
Small Companies ◽  
Statistical Process ◽  
Production Staff ◽  
Footwear Industry

The paper shows the relation among the number of small, medium and large companies in the leather and footwear industry in Croatia, as well as the relation among the number of their employees by means of the Spearman and Pearson correlation coefficient. The data were collected during 21 years. The warning zone and the risk zone were determined by means of the Statistical Process Control (SPC) for a certain number of small, medium and large companies in the leather and footwear industry in Croatia. Growth models, based on externalities, models based on research and development and the AK models were applied for the analysis of the obtained research results. The paper shows using the correlation coefficients that The relation between the number of large companies and their number of employees is the strongest, i.e. large companies have the best structured work places. The relation between the number of medium companies and the number of their employees is a bit weaker, while there is no relation in small companies. This is best described by growth models based on externalities, in which growth generates the increase in human capital, i.e. the growth of the level of knowledge and skills in the entire economy, but also deductively in companies on microeconomic level. These models also recognize the limit of accumulated knowledge after which growth may be expected. The absence of growth in small companies results from an insufficient level of human capital and failure to reach its limit level which could generate growth. According to Statistical Process Control (SPC), control charts, as well as regression models, it is clear that the most cost-effective investment is the investment into medium companies. The paper demonstrates the disadvantages in small, medium and large companies in the leather and footwear industry in Croatia. Small companies often emerge too quickly and disappear too easily owing to the employment of administrative staff instead of professional production staff. As the models emphasize, companies need to invest into their employees and employ good production staff. Investment and support to the medium companies not only strengthens the companies which have a well-arranged technological process and a good systematization of work places, but this also helps large companies, as there is a strong correlation between the number of medium and large companies.

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