Impact of measurement errors on the performance and distributional properties of the multivariate capability index $$\mathbf{NMC }_\mathbf{pm }$$ NMC pm

Dianda, Daniela Fernanda; Quaglino, Marta Beatriz; Pagura, José Alberto

doi:10.1007/s10182-017-0295-2

Cited by 6 publications

(4 citation statements)

References 16 publications

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“…Several PCIs, including as together with other essential statistical tools for quality assurance, are utilized in order to evaluate the performance of the production process for cases containing a single quality characteristic feature (Chakraborty et. al 2016;Coetzer et al, 2016;Dianda,et al 2017;Felipe,et al 2017;Lupo, 2015;Pearn, et al 2011). PCIs are a type of statistical measurement that is used in industrial enterprises to assess the performance of processes for scenarios that include a particular product attribute (Goodwin, 2015).…”

Section: Introductionmentioning

confidence: 99%

“…Quantitative quality control relies heavily on the assessment of the process capability, and process capability indices (PCIs) are statistical measurements of the process capability (Chakraborty et. al 2016;Coetzer et al, 2016;Dianda, et al 2017;Felipe, et al 2017;Lupo, 2015;Pearn, et al 2011). In the most recent decades, a great number of PCIs have been presented, and they have found widespread use in a variety of sectors.…”

Section: Introductionmentioning

confidence: 99%

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Model Based Six Sigma Concept and Process Capability Indices: The Research Study of Oil Industrial Case in Yemen

Saleh Al-Abdi,

A.M. Ali,

M.M Badr

2023

J. Innov. Bus. Ind.

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Process capability indices are utilized extensively in the quality control inspection process, both at the level of production and for general business operations. They take into consideration both the location and the deviation from the specified limits and targets. Several literatures have contained contributions on this issue. Existing PCIs based on Six Sigma, on the other hand, merely displayed a range of quality levels rather than a single quality level value. Thus, previous studies have found the insufficient and ineffective deployment of Six Sigma to process control and yield process. Motivated by industrial Aden oil refinery process performance case study, we discuss the density characteristic of oil to estimate the process yield, and the capacity of the process.In addition, an effort to identify the level of quality in which the refinery operates. By investigating two different methods for estimating process yield, the sigma level, which areby extends the indicesto estimate capacity, level of sigma, and yield process. The analyses and findings indicated that the indicesoutperformed the existing indices. Ultimately, Six Sigma-based process yield index represents a potential approach that other industries and practitioners can utilize to assess process performance and quality control.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Model Based Six Sigma Concept and Process Capability Indices: The Research Study of Oil Industrial Case in Yemen

Saleh Al-Abdi,

A.M. Ali,

M.M Badr

2023

J. Innov. Bus. Ind.

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“…(PCIs) have been proposed for the manufacturing industry to provide numerical measures on how well a process is capable of reproducing items within the present specification limits in the factory. Numerous process capability indices, including and , p pm pk C C C have been used to evaluate process performance for cases with single quality characteristics, which are important tools for quality/reliability assurance (Chakraborty & Chatterjee, 2016;Coetzer & de Jongh, 2016;Dianda, Quaglino, & Pagura, 2017;Felipe & Benedito, 2017;Lupo, 2015;Pearn, Shiau, Tai, & Li, 2011). the existing PCIs have not assessed the quality of a product or service while taking into consideration the possibility of the process mean to shift by as much as 1.5σ from the target (Bothe, 2002;Hsu, Pearn, & Wu, 2008;Nourelfath, Aldowaisan, & Hassan, 2016).…”

Section: Introductionmentioning

confidence: 99%

TITLE YIELD INDEX BASED ON CONCEPT OF SIX- SIGMA SSCpk FOR ASSESSMENT PROCESS PERFORMANCE IN INDUSTRIES

Ali¹,

Ghaffar²,

Al‐Swidi³

et al. 2022

PES

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Process quality is a key factor in facilitating product sales, (PCIs), which determine the relationship between the manufacturing specifications and the actual process performance by quantifying process potential. Although Cp, Cpk indices are the most popular and important criteria used in manufacturing industries to measure process performance reported extensively in the literature, in the literature, the majority of previous studies neglected the idea of Six Sigma (SS). However, existing PCIs based on SS presented only a range of quality levels rather than a specific quality level value. The purpose of this study is to measure and enhance the precision of performance evaluation for processes industrial, through the use of (PCIs) and (SS) concept. In light of this, this study introduces new performance index based on the idea SS which are SSCpk by extending the indices Cp, Cpk and calculating the sigma process level directly to measure yield process based on idea SS. To demonstrate the effectiveness applicability of the SSCpk index, this study presents an industrial case study to assess the process performance of Aden oil refinery in Yemen. Toward this end, the data for essential quality characteristic of petroleum products namely octane number was collected randomly from Aden refinery. The findings of this study indicated that the proposed index SSCpk outperformed on the existing indices Cpk as the shows at the results and discussion. Finally, the proposed process yield index based on SS concept is a promising approach and thus can be utilized by other industries and practitioners to assess process performance in the aspect of precision and quality control.

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“…Scagliarini 16 and Dianda et al 17 . studied performances of

M C_{p}

and

N M C_{p m}

, respectively, under the influence of measurement error.…”

Section: Introductionmentioning

confidence: 99%

Distributional and inferential properties of some new multivariate process capability indices for symmetric specification region

Chakraborty

Chatterjee

2020

Quality & Reliability Eng

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Statistical quality control is used to improve performance of processes. Since most of the processes are multivariate in nature, multivariate process capability indices (MPCIs) have been developed by many researchers depending on the context. However, it is generally difficult to understand and calculate MPCIs, compared to their univariate counterparts like Cp, Cpk, and so on. This paper discusses a relatively new development in MPCIs, namely, CGfalse(u,vfalse), which is a multivariate analogue of Cpfalse(u,vfalse)—the celebrated superstructure of univariate process capability indices . Some statistical properties of CGfalse(u,vfalse) are studied, particularly of CGfalse(0,0false), a member MPCI of the superstructure, which measures potential capability of a multivariate process. A threshold value of CGfalse(0,0false) is computed, and this can be considered as a logical cut‐off for other member indices of CGfalse(u,vfalse) as well. The expression for the upper limit of the proportion of nonconformance is derived as a function of CGfalse(0,0false). Density plots of asymptotic distributions of four major member indices of CGfalse(u,vfalse), namely, CGfalse(0,0false), CGfalse(1,0false), CGfalse(0,1false), and CGfalse(1,1false), are made. Finally, a numerical example is discussed to supplement the theory developed in this paper.

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Impact of measurement errors on the performance and distributional properties of the multivariate capability index $$\mathbf{NMC }_\mathbf{pm }$$ NMC pm

Cited by 6 publications

References 16 publications

Model Based Six Sigma Concept and Process Capability Indices: The Research Study of Oil Industrial Case in Yemen

Model Based Six Sigma Concept and Process Capability Indices: The Research Study of Oil Industrial Case in Yemen

TITLE YIELD INDEX BASED ON CONCEPT OF SIX- SIGMA SSCpk FOR ASSESSMENT PROCESS PERFORMANCE IN INDUSTRIES

Distributional and inferential properties of some new multivariate process capability indices for symmetric specification region

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