Statistical Process Control Scheme Design

1995 ◽  
Vol 27 (3) ◽  
pp. 214-225 ◽  
Author(s):  
J. Bert Keats ◽  
John D. Miskulin ◽  
George C. Runger
Keyword(s):  
Process Control ◽  
Statistical Process Control ◽  
Statistical Process ◽  
Scheme Design ◽  
Control Scheme

scholarly journals A Quality Control Scheme for a Commercial Pozzolana Plant: A Case Study of Pozzolana Ghana Ltd

2019 ◽  
Vol 60 (2) ◽  
pp. 74-83
Author(s):  
J. Sarfo-Ansah ◽  
K. A. Boakye ◽  
E. Atiemo ◽  
R. Appiah
Keyword(s):  
Mechanical Properties ◽  
Quality Control ◽  
Compressive Strength ◽  
Process Control ◽  
Statistical Process Control ◽  
Setting Time ◽  
Mechanical Tests ◽  
Statistical Process ◽  
Control Scheme ◽  
Quality Control Scheme

A Quality control scheme was developed for a 200 ton per day commercial pozzolana plant. The scheme was evaluated for the first 34 days of production. Statistical Process Control tech­niques were specifically applied to the mechanical properties of setting times and compressive strength. Results obtained showed that pozzolana samples tested were chemically suitable with total SiO2, Al2O3 and Fe2O3 content ≥ 70%. Mechanical tests performed were mostly under control and when out-of-control, they gave valuable indication to plant malfunction or operator errors which were promptly corrected. The results of mechanical properties tested against the three major brands of cement on the Ghanaian market showed that pozzolana gave highest compressive strengths with Dangote CEM I 42.5R ranging between 21.3 MPa - 36.3 MPa at 7 days and 33.8 MPa - 45.1 MPa at 28 days whilst lowest compressive strengths were obtained with Ghacem CEM II B-L 32.5R cement ranging between 16.3 MPa – 23.6 MPa at 7 days and 23.3 MPa – 30.7 MPa at 28 days. Compressive strengths obtained with Diamond CEM II B-L 42.5N cement were average. A mean compressive strength for all brands of ce­ment of 25.2 MPa and 33.6 MPa at 7 days and 28 days respectively were obtained. Keywords: Pozzolana cement, statistical process control, Shewhart chart, compressive strength, setting time

scholarly journals The recurrent event process of a success preceded by a failure and its application

1997 ◽  
Vol 1 (2) ◽  
pp. 101-117 ◽  
Author(s):  
C. D. Lai
Keyword(s):  
Process Control ◽  
Statistical Process Control ◽  
Control Chart ◽  
Discrete Time ◽  
Waiting Time ◽  
Renewal Process ◽  
Recurrent Event ◽  
Statistical Process ◽  
Control Scheme ◽  
Recurrent Event Process

We describe a simple discrete time renewal process of an event where a success is preceded by a failure. Its properties, especially the distributions of the counting and the interval processes, are investigated. We also propose an application to statistical process control based on the waiting time between two adjacent events. It is shown that the average number inspected under the new control scheme is larger than with the so called CCC control chart.

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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