THE CLASSIFICATION AND CHARACTERISTICS OF CONTROL CHARTS

Izabela Dagmara Czabak-Górska

Abstract


Control Charts are the basic tool for quality control. They were developed in the 1920s when the dominant type of production was mass production. In order to properly use classic Control Charts, the data from the manufacturing process should meet the following assumptions: an empirical distribution of measurement data should be normally distributed or close to a normal distribution, measurement data should be independent, the manufacturing process should be capable of quality depending on the type of Control Chart, a sample that is large enough (sometimes made of several elements) must be taken. Currently, a shift can be observed from mass production towards short production runs, which causes the proper use of the traditional approach to be impossible. In recent years, control charts are once again in the spotlight, and consequently many scientists, i.e. Reynolds, Zimmer, Costa, Calvin and Chan have undertaken the task to adapt the classic idea of keeping Control Charts to modern conditions of production. The development of science in this area allows for the avoidance of making major mistakes in the conduct of Control Charts and for making the wrong decisions based on erroneous analysis. However, the appearance of new literature pieces implies the need to classify Control Charts, therefore, this article describes the idea of conduct, the most important assumptions and distribution of classical Shewhart's Control Charts, as well as a suggestion for the distribution of advanced Control Charts that meet the needs of the currently used production types. The work also contains a concise description of the chosen control charts as well as the threats resulting from their inappropriate selection. This elaboration is an extension to the article of Czabak-Górska (2017).

Keywords


SPC, the classification of control charts, Shewhart's charts, charts of the new generation, sequential charts, standardized charts, adaptive charts, special charts

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References


Ali, S., Pievatolo, A., & Göb, R. (2016). An Overview of Control Charts for High‐quality Processes. Quality and Reliability Engineering International, 32, 2171-2189.

Bartkowiak, M. (2011). Karty kontrolne obrazem zmienności procesu. Kwartalnik nauk o przedsiębiorstwie, 3(2011), 20, 63-71.

Boyapati, S. R., Nasiru, S., & Lakshmi, K. N. V. (2015). Variable Control Charts Based on Percentiles of the New Weibull-Pareto Distribution. Pakistan Journal of Statistics and Operation Research, 11(4), 631-643.

Czabak-Górska, I. D. (2016). Karty kontrolne X i R dla rozkładów skośnych-studium przypadku. Zarządzanie Przedsiębiorstwem/Polskie Towarzystwo Zarządzania Produkcją, (4), 10-17.

Czabak-Górska, I. D. (2017). Klasyfikacja nowoczesnych kart kontrolnych. Innowacje w Zarządzaniu i Inżynierii Produkcji, 2, 281 -290.

Dahlgaard, J. J., Kristensen, K., & Kanji, G. K. (2000). Podstawy zarządzania jakością. Warszawa: Wydawnictwo Naukowe PWN.

Greber, T. (1999). Jak czytać kart kontrolne Shewharta. Normalizacja, 11, 17-22.

Greber, T. (2000). Statystyczne sterowanie procesami-doskonalenie jakości z pakietem STATISTICA. Kraków: StatSoft Polska.

Hamrol, A. (2009). Zarządzanie jakością z przykładami. Warszawa: Wydawnictwo Naukowe PWN.

Hiller, F.S. (1969). X and R Chart Control Limits Based on a Small Number of Subgroups. Journal of Quality Technology, 1, 17-26.

Hillier, F. S. (1967). Small Sample Probability Limits for the Range Chart. Journal of the American Statistical Association, 62, 1488-1493.

Karagöz, D., & Hamurkaroglu C. (2012). Control charts for skewed distributions: Weibull, gamma, and lognormal. Metodoloski zvezki, 9(2), 95 - 106.

Karaoglan, A. D., & Bayhan, G. M. (2011). Performance comparison of residual control charts for trend stationary first order autoregressive processes. Gazi University Journal of Science, 24(2), 329-339.

Keller, P. (2011). Statistical process control demystified. USA: McGraw Hill Professional.

Kowalczyk, A. (2012). „Ocena implementacji i skuteczności metod zarzadzania jakością w opinii dostawców branży motoryzacyjnej.”. Dissertation. Dissertation supervisor: prof. dr hab. A. Maleszka. Katedra Przyrodniczych Podstaw Jakości, Poznań.

Kujawińska, A., & Więcek-Janka, E. (2010). Statystyczna ocena procesów w mikro i małych przedsiębiorstwach. Zeszyty Naukowe Uniwersytetu Szczecińskiego. Ekonomiczne Problemy Usług [51 Uwarunkowania rynkowe rozwoju mikro i małych przedsiębiorstw Mikrofirma 2010], 431-439.

Magaji, A. A., Yahaya, A., & Asiribo, O. E. (2015). Assessing the Effects of Autocorrelation on the Performance of Statistical Process Control Charts. International Journal of Mathematics and Statistics Invention, 3(6), 15-23.

Mohammed, M. A., Panesar, J. S., Laney, D. B., & Wilson, R. (2013). Statistical process control charts for attribute data involving very large sample sizes: a review of problems and solutions. BMJ quality & safety, bmjqs-2012, 362-368.

Montgomery, D. C. (2009). Introduction to statistical quality control. 6th Edition, USA: John Wiley & Sons, Inc.

Oakland, J. S. (2004). Oakland on quality management. Oxford: Butterworth-Heinemann .

Olszewska, A. M. (2008). Karty kontrolne nowej generacji w zarządzaniu jakością produkcji. Dissertation, Warszawa: Oficyna Wydawnicza Politechniki Warszawskiej.

PN-ISO 3534-2 (2010). Statystyka. Statystyczne sterowanie jakością. Terminologia i symbole. Warszawa: Polski Komitet Normalizacyjny.

PN-ISO 8258+AC1 (1996). Karty kontrolne Shewharta. Warszawa: Polski Komitet Normalizacyjny.

Psarakis, S. (2015). Adaptive control charts: recent developments and extensions. Quality and Reliability Engineering International, 31(7), 1265-1280.

Quesenberry, C. P. (1991). SPC Q charts for a binomial parameter p: short or long runs. Journal of quality technology, 23(3), 239-246.

Quesenberry, C. P. (1991). SPC Q charts for start-up processes and short or long runs. Journal of quality technology, 23(3), 213-224.

Sałaciński, T. (2009). SPC statystyczne sterowanie procesami produkcji. Warszawa: Oficyna Wydawnicza Politechniki Warszawskiej.

Szkoda, J. (2004). Sterowanie jakością procesów produkcyjnych: teoria i praktyka. Wydaw. Olsztyn: Wydawnictwo Uniwersytetu Warmińsko-Mazurskiego.

Yen, F. Y., Chong, K. M. B., & Ha, L. M. (2013). Synthetic-type control charts for time-between-events monitoring. PloS one, 8(6), e65440, 1-13.




DOI: http://dx.doi.org/10.12955/cbup.v5.907

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