APSS – Software Support for Decision Making in Statistical Process Control

Darja Noskievicova (1)
(1) VSB Technical University Ostrava

Abstract

Purpose: SPC can be defined as the problem solving process incorporating many separate decisions including selection of the control chart based on the verification of the data presumptions. There is no professional statistical software which enables to make such decisions in a complex way.

Methodology/Approach: There are many excellent professional statistical programs but without complex methodology for selection of the best control chart. Proposed program in Excel APSS (Analysis of the Process Statistical Stability) solves this problem and also offers additional learning functions.

Findings: The created SW enables to link altogether separate functions of selected professional statistical programs (data presumption verification, control charts construction and interpretation) and supports active learning in this field.

Research Limitation/implication: The proposed SW can be applied to control charts covered by SW Statgraphics Centurion and Minitab. But there is no problem to modify it for other professional statistical SW.

Originality/Value of paper: The paper prezents the original SW created in the frame of the research activities at the Department of Quality Management of FMT, VŠB-TUO, Czech Republic. SW enables to link altogether separate functions of the professional statistical SW needed for the complex realization of statitical process control and it is very strong tool for the active learning of statistical process control tasks.

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References

Cox, D.R., 2006. Principles of Statistical Inference. Cambridge: Cambridge University Press.

Dell Statistica Help, 2017. Help. [online] Available at: < http://documentation.statsoft.com/STATISTICAHelp.aspx?path=common/AboutSTATISTICA/ElectronicManualIndex > [Accessed 01 August 2018].

Keller, P.A., 2011. Statistical Process Control Demystified. New York: McGraw-Hill.

Minitab, 2018. Minitab. [online] Available at: < http://www.minitab.com/en-us/products/minitab/ > [Accessed 01 August 2018].

Montgomery, D.C., 2013. Statistical Quality Control: A Modern Introduction. 7th Ed. Hoboken: J. Wiley & Sons.

Noskievi?ová, D., 2010. Effective Implementation of Statistical Process Control, Engineering the Future. In: L. Dudas, ed. 2010. Engineering the Future. London: InteChopen. Ch. 11.

Noskievi?ová, D., 2015. Module 4 Process Improvement Using Statistical Analysis, Submodule 4.4 Analysis of the Process Statistical Stability, User manual. Ostrava: VŠB-TU Ostrava.

Oakland, J.S., 2011. Statistical Process Control. 6th Ed. New York: Routledge.

Qiu, P., 2014. Introduction to Statistical Process Control. Boca Raton: CRC Press.

Statgraphics Technologies, 2017. Statgraphics 18. [online] Available at: < http://www.statgraphics.com/centurion-xvii > [Accessed 01 August 2018].

Thompson, J.R. and Koronacki, J., 2002. Statistical Process Control: the Deming Paradigm and Beyond. 2nd ed. Boca Raton: Chapman & Hall/CRC.

Zimmerman, D.W., 2011. A Simple and Effective Decision Rule for Choosing a Significance Test to Protect Against Non-normality. Br. J. Math. Stat. Psychol., 64(3), pp.388-409. http://dx.doi.org/10.1348/000711010X524739.

Authors

Darja Noskievicova
darja.noskievicova@vsb.cz (Primary Contact)
Noskievicova, D. (2018). APSS – Software Support for Decision Making in Statistical Process Control. Quality Innovation Prosperity, 22(3), 19–26. https://doi.org/10.12776/qip.v22i3.1141

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