The development of supplier comparison using lower process capability index for Weibull distribution model
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Abstract
Durability, one of the dimensions of quality, is the important criterion for comparing suppliers. In many industries, durability is widely measured by lifetime data modeled with Weibull distribution because of its flexible shape. The process capability indices (PCIs) is an appropriate tool for comparing suppliers regarding quality aspect. Many methods for comparing suppliers using the PCIs are developed to correspond to manufacturing conditions. However, the methods cannot apply with the lifetime data since they are developed under the normality assumption. The mentioned violation leads to probably misleading results in comparison. To consider the lifetime, this paper proposes the new supplier comparison using the PCIs for Weibull distribution model. Two parameters (scale and shape) of the Weibull distribution are studied. Since the shape parameter is sensitive to the PCIs estimation, this paper emphasizes on various shape parameters, e.g. symmetric, right, and left skewed. Regarding the lifetime, this paper studies single quality characteristic and the lower PCIs (Cpl). The producer’s risk and the power of test obtained from Monte Carlo simulation are used to evaluate the performance of the proposed method. This method is compared with supplier comparison methods applying percentile PCIs and Box-Cox transformation for the PCIs estimation.
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References
Chou YM. Selecting a better supplier by testing process capability indices. Qual Eng. 1994;6(3):427-38.
Hubele NF, Berrado A, Gel ES. A Wald test for comparing multiple capability indices. J Qual Tech. 2005;37(4): 304-7.
Pearn WL, Hung HN, Cheng YC. Supplier selection for one-side processes with unequal sample sizes. Eur J Oper Res. 2009;195:381-93.
Manomat S, Sudasna-na-Ayudthya P. The method for selecting multiple suppliers using one-sided specification limit’s capability index. Eng Appl Sci. 2009;36(4):313-21.
Pearn WL, Hung HN, Chuang YS, Su RH. An effective powerful test for one-sided supplier selection problem. J Stat Comput Simulat. 2011;81(10):1313-31.
Wu CC, Horng Shiau JJ, Pearn WL, Hung HN. A bayesian approach for group supplier selections based on the popular process-capability-index Cpk. Qual Tech Quant Manag. 2016;13(2):109-23.
Pearn WL, Wu CH. Supplier selection for multiple-characteristics processes with one-sided specifications. Qual Tech Quant Manag. 2013;10(1):133-9.
Pearn WL, Wu CH, Chuang CC. An effective powerful test for one-sided supplier selection problem with multiple independent characteristics. Qual Tech Quant Manag. 2016;13(2):182-96.
Lan YJ, Lin CJ. An efficient subset selection method for supplier selection under multiple quality characteristics. Qual Tech Quant Manag. 2017;16(2):231-41.
Garvin DA. Assessing the awareness and usage of quality control tools with emphasis to Statistical Process Control (SPC) in Ethiopian Manufacturing Industries. Intell Inform Manag. 1991;87(6):101-9.
Dodson B. The Weibull Analysis Handbook. 2nd ed. USA: ASQ Quality Press; 2006.
Thavorn E, Sudasna-na-Ayudthya P. Effect of Weibull distribution on supplier comparison using lower process capability index: the examples study. 2nd International Conference on Management Science and Industrial Engineering; 2020 Apr 7-9; Osaka Japan. Association for Computing Machinery; 2020. p. 13-7.
Kane VE. Process capability indices. J Qual Tech. 1986;18:41-52.
Li DC, Lin LS. A new approach to assess product lifetime performance for small data sets. Eur J Oper Res. 2013; 230:290-8.
Wu CW, Shu MH, Chang YN. Variable-sampling plans based on lifetime-performance index under exponential distribution with censoring and its extensions. Appl Math Model. 2018;55:81-93.
Wierda SJ. A multivariate process capability index. ASQC Quality Congress Transactions; 1993 May 24-26; Boston, USA. USA: American Society for Quality Control; 1993. p. 342-8.
Castagliola P. Evaluation of non-normal process capability indices using Burr’s distributions. Qual Eng. 1996;8(4):587-93.
Ahmad S, Abdollahian M, Zeephongsekul P, Abbasi B. Performance analysis for skewed data. Ubiquitous Comput Comm J. 2008;IKE07:1-8.
Hosseinifard SZ, Abbasi B. Process capability analysis in non normal linear regression profiles. Comm Stat Simulat Comput. 2012;41(10):1761-84.
Safdar S, Ahmed E. Process capability indices for shape parameter of Weibull distribution. Open J Stat. 2014;4:207-19.
Montgomery DC. Statistical quality control. 7th ed. New Jersey: John Wiley & Sons; 2009.
Box GEP, Cox DR. An analysis of transformation. J Roy Stat Soc B. 1964;26:211-52.
Kovarik A, Sarga L. Process capability indices for non-normal data. WSEAS Trans Bus Econ. 2014;11:419-29.
Swamy DR, Nagesh P, Wooluru Y. Process capability indices for non-normal distribution-a review. International Conference on Operations Management and Research: (ICOMAR 2016); 2016 Jan 21-22; Mysuru, India. SDMIMD; 2016. p. 1-13.