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Purpose: The aim of this paper is to show how it could utilize the statistical methods for the process management. Design/methodology/approach: The research methodology base on theoretical analysis and on empirical researches. It is presented a practical solution for estimation of process capability indices In case non-normal distribution. Findings: The Clements' method brings sufficiently correct results for the analyzed case. Research limitations/implications: The future research will contain analysis for other non-normal distributions. Practical implications: Described methodology and results can be employed in industrial practice. Originality/value: Comparative analysis of capability indices determination in case of distribution unlike the normal one.
Wydawca
Rocznik
Tom
Strony
39--42
Opis fizyczny
Bibliogr. 18 poz.
Twórcy
autor
- Department of Physical and Powder Metallurgy, Faculty of Metals Engineering and Industrial Computer Science, AGH University of Science and Technology, Al. Mickiewicza 30, 30-059 Kraków, Poland, czarski@uci.agh.edu.pl
Bibliografia
- [1] D. Lock, Gower Handbook of Quality Management, Second Edition, PWN, Warsaw, 2002 (in Polish).
- [2] M. Dudek-Burlikowska, Quality Research Methods as a Factor of Improvement of Preproduction sphere, Journal of Achievements in Materials and Manufacturing Engineering 18 (2006) 435-438.
- [3] A. Gwiazda, Quality Tools in a Process of Technical Project Management, Journal of Achievements in Materials and Manufacturing Engineering 18 (2006) 439-442.
- [4] P. Paliska, D. Pavletic, M. Sokovic, Quality Tools - Systematic Use in Process Industry, Journal of Achievements in Materials and Manufacturing Engineering 25/1 (2007) 79-82.
- [5] M. Sokovic, D. Pavletic, E. Krulcic, Six Sigma Process Improvements in Automotive Parts Production, Journal of Achievements in Materials and Manufacturing Engineering 19/1 (2006) 96-102.
- [6] M. Dudek, D. Szewieczek, Usage of Quality Methods: Failure Mode and Effect Analysis (FMEA) as a Element of Production Process, Proceedings of the 11th Scientific Conference “Contemporary Achievements in Mechanics, Manufacturing and Materials Science” CAM3S’2005, Gliwice - Zakopane, 2005, 317-321.
- [7] Glossary and Tables for Statistical Quality Control, Fourth Edition, ASQ Statistics Division, ASQ Quality Press, 2004.
- [8] F.W. Breyfogle III, Implementing Six Sigma. Smarter Solutions Using Statistical Methods, Second Edition, John Wiley and Sons, Inc., 2003.
- [9] D.C. Montgomery, Design and Analysis of Experiments, Six Edition, John Wiley and Sons, Inc., 2005.
- [10] A.M. Joglekar, Statistical Methods for Six Sigma, in: R&D and Manufacturing, John Wiley and Sons, Inc., 2003.
- [11] P. Mathews, Design of Experiments with Minitab, ASQ Quality Press, 2005.
- [12] D.C. Montgomery, Introduction to Statistical Quality Control, Fourth Edition, John Wiley and Sons, Inc., 2000.
- [13] M. Dudek-Burlikowska, Quality Estimation of Process with Usage Control Chards Type X-R and Quality Capability of Process Cp, Cpk, Proceedings of the 13th International Scientific Conference “Achievements in Mechanical and Materials Engineering” AMME’05, Gliwice - Wisła, 2005, 211-216.
- [14] A. Czarski, Capability Process Assessment in Six Sigma Approach, Metallurgy and Foundry Engineering 33 (2007) 105-111.
- [15] A. Czarski, K. Satora, P. Matusiewicz, Statistical Methods in Quality Management - Process Capability Analysis, Metallurgy and Foundry Engineering 33 (2007) 121-128.
- [16] S. Kotz, C.R. Lovelance¸ Process Capability Indices in Theory and Practice, Arnold, 1998.
- [17] L. Pei-Hsi, C. Fei-Long, Process Capability Analysis of nonnormal Process Data Using the Burr XII Distribution, International Journal of Advanced Manufacturing and Technology 27 (2007) 975-984.
- [18] J.A. Clements, Process Capability Calculations for Nonnormal Distributions, Quality Progress 22 (1989) 95-100.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BSL7-0033-0030
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