scholarly journals Cost and value in medical education: the role of statistical process control

2017 ◽  
Keyword(s):  
Medical Education ◽  
Process Control ◽  
Statistical Process Control ◽  
Statistical Process

The critical role of NIR spectroscopy and statistical process control (SPC) strategy towards captopril tablets (25 mg) manufacturing process understanding: a case study

2013 ◽  
Vol 20 (3) ◽  
pp. 345-351 ◽  
Author(s):  
Cátia Panizzon Dal Curtivo ◽  
Nathália Bitencourt Funghi ◽  
Guilherme Diniz Tavares ◽  
Sávio Fujita Barbosa ◽  
Raimar Löbenberg ◽  
...  
Keyword(s):  
Process Control ◽  
Statistical Process Control ◽  
Manufacturing Process ◽  
Critical Role ◽  
Nir Spectroscopy ◽  
Statistical Process ◽  
Process Understanding

scholarly journals The Impact of Statistics in Industry and the Role of Statisticians

2016 ◽  
Vol 30 (1) ◽  
pp. 7-20
Author(s):  
Ronald J.M.M. Does ◽  
Albert Trip
Keyword(s):  
Process Control ◽  
Statistical Process Control ◽  
Six Sigma ◽  
Practical Experience ◽  
Successful Implementation ◽  
Statistical Process ◽  
Quality Program ◽  
Quality Programs ◽  
The Impact

The use of statistics in quality management has a long history. Pioneers in this field, such as Walter A. Shewhart and W. Edwards Deming, refer to themselves as industrial statisticians. Statistical thinking in industry means that all work is regarded as a series of interconnected processes, that all processes show variation, and that a reduction in variation is the key for continuous improvement. In literature we find several quantitative quality programs to achieve this. We may mention Statistical Process Control (SPC)and the Six Sigma quality program, among others. We have implemented Statistical Process Control and Six Sigma in several industries. In this paper we briefly describe the philosophies of both programs and the steps needed for a successful implementation. Based on practical experience with both programs we describe the role that a statistician can play in industry. We shall also give an overview of research initiated by the projects we have carried out.

Indicator Organisms in Meat and Poultry Slaughter Operations: Their Potential Use in Process Control and the Role of Emerging Technologies

2011 ◽  
Vol 74 (8) ◽  
pp. 1387-1394 ◽  
Author(s):  
PARMESH K. SAINI ◽  
HARRY M. MARKS ◽  
MOSHE S. DREYFUSS ◽  
PETER EVANS ◽  
L. VICTOR COOK ◽  
...  
Keyword(s):  
Process Control ◽  
Statistical Process Control ◽  
Emerging Technologies ◽  
Indicator Organisms ◽  
Statistical Process ◽  
Poultry Products ◽  
Monitoring Process ◽  
Potential Use ◽  
Level Reduction

Measuring commonly occurring, nonpathogenic organisms on poultry products may be used for designing statistical process control systems that could result in reductions of pathogen levels. The extent of pathogen level reduction that could be obtained from actions resulting from monitoring these measurements over time depends upon the degree of understanding cause-effect relationships between processing variables, selected output variables, and pathogens. For such measurements to be effective for controlling or improving processing to some capability level within the statistical process control context, sufficiently frequent measurements would be needed to help identify processing deficiencies. Ultimately the correct balance of sampling and resources is determined by those characteristics of deficient processing that are important to identify. We recommend strategies that emphasize flexibility, depending upon sampling objectives. Coupling the measurement of levels of indicator organisms with practical emerging technologies and suitable on-site platforms that decrease the time between sample collections and interpreting results would enhance monitoring process control.

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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