Goodness-of-fit Tests Comparison for Statistical Process Control in an Automotive Industrial Unit

Cláudia Silva; Radu Godina

doi:10.1109/ICITM48982.2020.9080401

Goodness-of-fit Tests Comparison for Statistical Process Control in an Automotive Industrial Unit

Cláudia Silva, Radu Godina

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Citation (Scopus)

Abstract

This paper aims to evaluate whether if the data that form several samples used for the statistical process control (SPC) control charts derive from a population with a normal distribution or not. A piece manufactured in a Portuguese small and medium-sized enterprises (SME) that operates in the automotive industry is used as an example. Knowing if the distribution is normal or not allows identifying what out of control tests should be applied and can also help finding precise false alarm rates. For this purpose, six goodness-of-fit tests are used and then compared. Some of these goodness-of-fit tests could be more sensible than others in detecting departures from normality. The results for two in three scenarios of the same dimensional feature show that some goodness-of-fit tests reject the null hypothesis and that the data of the measured samples do not derive from a population with a normal distribution.

Original language	English
Title of host publication	ICITM 2020 - 2020 9th International Conference on Industrial Technology and Management
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	161-165
Number of pages	5
ISBN (Electronic)	9781728143064
DOIs	https://doi.org/10.1109/ICITM48982.2020.9080401
Publication status	Published - Feb 2020
Event	9th International Conference on Industrial Technology and Management, ICITM 2020 - Oxford, United Kingdom Duration: 11 Feb 2020 → 13 Feb 2020

Conference

Conference	9th International Conference on Industrial Technology and Management, ICITM 2020
Country/Territory	United Kingdom
City	Oxford
Period	11/02/20 → 13/02/20

Keywords

automotive industry
control charts
goodness-of-fit test
quality control
statistical process control

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1109/ICITM48982.2020.9080401

Cite this

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title = "Goodness-of-fit Tests Comparison for Statistical Process Control in an Automotive Industrial Unit",

abstract = "This paper aims to evaluate whether if the data that form several samples used for the statistical process control (SPC) control charts derive from a population with a normal distribution or not. A piece manufactured in a Portuguese small and medium-sized enterprises (SME) that operates in the automotive industry is used as an example. Knowing if the distribution is normal or not allows identifying what out of control tests should be applied and can also help finding precise false alarm rates. For this purpose, six goodness-of-fit tests are used and then compared. Some of these goodness-of-fit tests could be more sensible than others in detecting departures from normality. The results for two in three scenarios of the same dimensional feature show that some goodness-of-fit tests reject the null hypothesis and that the data of the measured samples do not derive from a population with a normal distribution.",

keywords = "automotive industry, control charts, goodness-of-fit test, quality control, statistical process control",

author = "Cl{\'a}udia Silva and Radu Godina",

note = "Radu Godina acknowledges Fundacao para a Ciencia e a Tecnologia (FCT -MCTES) for its financial support via the project UIDB/00667/2020 (UNIDEMI).; 9th International Conference on Industrial Technology and Management, ICITM 2020 ; Conference date: 11-02-2020 Through 13-02-2020",

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doi = "10.1109/ICITM48982.2020.9080401",

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pages = "161--165",

booktitle = "ICITM 2020 - 2020 9th International Conference on Industrial Technology and Management",

publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

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Silva, C & Godina, R 2020, Goodness-of-fit Tests Comparison for Statistical Process Control in an Automotive Industrial Unit. in ICITM 2020 - 2020 9th International Conference on Industrial Technology and Management., 9080401, Institute of Electrical and Electronics Engineers (IEEE), pp. 161-165, 9th International Conference on Industrial Technology and Management, ICITM 2020, Oxford, United Kingdom, 11/02/20. https://doi.org/10.1109/ICITM48982.2020.9080401

Goodness-of-fit Tests Comparison for Statistical Process Control in an Automotive Industrial Unit. / Silva, Cláudia ; Godina, Radu.
ICITM 2020 - 2020 9th International Conference on Industrial Technology and Management. Institute of Electrical and Electronics Engineers (IEEE), 2020. p. 161-165 9080401.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Godina, Radu

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Y1 - 2020/2

N2 - This paper aims to evaluate whether if the data that form several samples used for the statistical process control (SPC) control charts derive from a population with a normal distribution or not. A piece manufactured in a Portuguese small and medium-sized enterprises (SME) that operates in the automotive industry is used as an example. Knowing if the distribution is normal or not allows identifying what out of control tests should be applied and can also help finding precise false alarm rates. For this purpose, six goodness-of-fit tests are used and then compared. Some of these goodness-of-fit tests could be more sensible than others in detecting departures from normality. The results for two in three scenarios of the same dimensional feature show that some goodness-of-fit tests reject the null hypothesis and that the data of the measured samples do not derive from a population with a normal distribution.

AB - This paper aims to evaluate whether if the data that form several samples used for the statistical process control (SPC) control charts derive from a population with a normal distribution or not. A piece manufactured in a Portuguese small and medium-sized enterprises (SME) that operates in the automotive industry is used as an example. Knowing if the distribution is normal or not allows identifying what out of control tests should be applied and can also help finding precise false alarm rates. For this purpose, six goodness-of-fit tests are used and then compared. Some of these goodness-of-fit tests could be more sensible than others in detecting departures from normality. The results for two in three scenarios of the same dimensional feature show that some goodness-of-fit tests reject the null hypothesis and that the data of the measured samples do not derive from a population with a normal distribution.

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Goodness-of-fit Tests Comparison for Statistical Process Control in an Automotive Industrial Unit

Abstract

Conference

Keywords

UN SDGs

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