ASPG Menu
search

American Scientific Publishing Group

verified Journal

Pure Mathematics for Theoretical Computer Science

ISSN
Online: 2995-3162
Frequency

Continuous publication

Publication Model

Open access · Articles freely available online · $500 APC applies after acceptance

Pure Mathematics for Theoretical Computer Science
Full Length Article

Volume 3Issue 2PP: 01–10 • 2024

Using Nonparametric Methods to Estimate Monitoring Maps Six Sigma of Vegetable Oil Production

Sanaa Mohammed Naeem 1*
1Southern technical university, college of health& medical Techniques in Basrah, Basrah, Iraq.
* Corresponding Author.
verified

Open Access & Copyright

© 2024 The Author(s). Published by ASPG. This article is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).

Received: June 21, 2023 Revised: September 05, 2023 Accepted: December 07, 2023

Abstract

Statistics are considered the backbone of the strategies of the quality control system, because of their important role in the use of tools, theories and analysis in these strategies. in the six sigma strategies (DMAIC) and (DMADV), each step is not without statistical methods. The study relied on the application of the statistical nonparametric methods and quantitative tools used in the Six Sigma strategy to apply the quality control performance of the research sample to improve the required quality by knowing the production derivatives and the reasons for the slowdown in the production process (Al-Moatasem Factory for Vegetable Oils), the fat line with its three sections.

Keywords

Quality control Control chart six sigma strategies (DMAIC) and (DMADV).

References

[1] V. Chernozhukov, I. Fernandez-Val, and A. Galichon, “Improving point and interval estimators of monotone functions by rearrangement,” Biometrika, vol. 96, pp. 559–575, 2009.

[2] A. J. Duncan, Quality Control and Industrial Statistics, 1974.

[3] J. Fox, Regression Diagnostics: An Introduction. Sage Publications, 2019.

[4] F. Aparisi and J. C. García-Díaz, “A multiobjective optimization for the EWMA and MEWMA quality control charts,” in Inverse Problems, Design and Optimization, vol. 1, p. 130, 2004.

[5] A. D. B., Statistical Quality Control, 1994.

[6] S. S. Chakravorty, “Six Sigma failures: An escalation model,” Operations Management Research, vol. 2, pp. 44–55, 2009.

[7] K. Linderman and T. E. Love, “Economic and economic statistical designs for MEWMA control charts,” Journal of Quality Technology, vol. 32, pp. 410–417, 2000.

[8] D. C. Montgomery, Introduction to Statistical Quality Control. John Wiley & Sons, 2007.

[9] R. L. Ott and M. Longnecker, An Introduction to Statistical Methods and Data Analysis. Cengage Learning, 2016.

[10] P. S. Pande, R. P. Neuman, and R. R. Cavanagh, The Six Sigma Way Team Fieldbook: An Implementation Guide for Project Improvement Teams. McGraw-Hill, 2002.

[11] M. E. Kabir, S. Boby, and M. Lutfi, “Productivity improvement by using Six-Sigma,” International Journal of Engineering and Technology, vol. 3, pp. 1056–1084, 2013.

[12] S. H. Park, Six Sigma for Quality and Productivity Promotion. Japan: Asian Productivity Organization, 2003.

[13] S. D. Peddada, “Confidence interval estimation of population means subject to order restrictions using resampling procedures,” Statistics & Probability Letters, vol. 31, pp. 255–265, 1997.

[14] T. P. Ryan, Statistical Methods for Quality Improvement. John Wiley & Sons, 2011.

[15] M. Strand, Y. Zhang, and B. J. Swihart, “Monotone nonparametric regression and confidence intervals,” Communications in Statistics—Simulation and Computation, vol. 39, pp. 828–845, 2010.

[16] G. Taguchi, “On-line quality control system designs,” in Statistical Process Monitoring and Optimization. CRC Press, pp. 21–38, 1999.

Cite This Article

Choose your preferred format

format_quote
Naeem, Sanaa Mohammed. "Using Nonparametric Methods to Estimate Monitoring Maps Six Sigma of Vegetable Oil Production." Pure Mathematics for Theoretical Computer Science, vol. 3, no. 2, 2024, pp. 01–10. DOI: https://doi.org/10.54216/PMTCS.030201
Naeem, S. (2024). Using Nonparametric Methods to Estimate Monitoring Maps Six Sigma of Vegetable Oil Production. Pure Mathematics for Theoretical Computer Science, Volume 3(Issue 2), 01–10. DOI: https://doi.org/10.54216/PMTCS.030201
Naeem, Sanaa Mohammed. "Using Nonparametric Methods to Estimate Monitoring Maps Six Sigma of Vegetable Oil Production." Pure Mathematics for Theoretical Computer Science Volume 3, no. Issue 2 (2024): 01–10. DOI: https://doi.org/10.54216/PMTCS.030201
Naeem, S. (2024) 'Using Nonparametric Methods to Estimate Monitoring Maps Six Sigma of Vegetable Oil Production', Pure Mathematics for Theoretical Computer Science, Volume 3(Issue 2), pp. 01–10. DOI: https://doi.org/10.54216/PMTCS.030201
Naeem S. Using Nonparametric Methods to Estimate Monitoring Maps Six Sigma of Vegetable Oil Production. Pure Mathematics for Theoretical Computer Science. 2024;Volume 3(Issue 2):01–10. DOI: https://doi.org/10.54216/PMTCS.030201
S. Naeem, "Using Nonparametric Methods to Estimate Monitoring Maps Six Sigma of Vegetable Oil Production," Pure Mathematics for Theoretical Computer Science, vol. Volume 3, no. Issue 2, pp. 01–10, 2024. DOI: https://doi.org/10.54216/PMTCS.030201
policy

Publisher's Note

The statements, opinions, and data presented in this article are solely those of the author(s) and do not necessarily represent those of ASPG, the journal, or its editors. ASPG and the editors disclaim responsibility for any harm arising from the use of any ideas, methods, instructions, or products described in this article, to the fullest extent permitted by applicable law.

Digital Archive Ready