A comparative study of two proposed CCC-r charts for high quality processes and their application to an injection molding process

ABSTRACT

High-quality industrial processes, characterized by a low fraction of non-conforming items, require paying special attention to the statistical control methods employed since traditional Shewhart's control charts are no longer suitable. In this article, CCC-r charts are considered based on the cumulative count of conforming items inspected until r non-conforming items are observed. However, even though these charts have shown to be useful for high-quality processes, they are characterized by a biased average run length (ARL). In order to help engineers interested in this control methodology to select the best option, a computational study of statistical validation was performed to compare the two most outstanding procedures for the cases r = 2, 3, and 4. The performance was evaluated based on the ARL under control. The application of the CCC-r chart to a real process is shown with data from an automobile parts plant. Finally, analysis and discussion of the results are presented.

KEYWORDS:

• average run length (ARL)
• car parts plant
• CCC-r control charts
• high-quality processes
• negative binomial distribution

Notes

1 The authors of this work developed a program in R to solve the problem of determining these control limits. The program is available at https://sites.google.com/site/marcelosmrekar/home/published-papers.

2 Authors developed a program in R to solve the problem of determining this control limits. This program is available at https://sites.google.com/site/marcelosmrekar/home/published-papers.

Additional information

Notes on contributors

Silvia Joekes

Silvia Joekes holds a Bachelor of Science in Statistics, a Master of Science in Applied Statistics and a Ph.D. in Engineering with specialization in Statistical Process Control. She is a Full Professor of Statistics at the School of Economics, Universidad Nacional de Córdoba (Argentina) and teaches a course in Industrial Experimental Design at the Master's program in Quality Management at Universidad Tecnológica Nacional (Argentina). She has published several articles in indexed journals, particularly in the area of quality management.

Marcelo Smrekar

Marcelo Smrekar is a mathematician. He holds a Master of Science in Mathematics and a Ph.D. in Statistics. He teaches Statistics at Universidad Nacional de Córdoba (Argentina). He is a Postgraduate professor of Mathematics and Statistics. He has several publications in indexed journals.

Andrea F. Righetti

Andrea F. Righetti is an accountant and holds a Master of Arts in University Teaching. She teaches Statistics at the School of Economics, Universidad Nacional de Córdoba (Argentina). She has published several articles in indexed journals, particularly in the areas of education and quality management.