A review of univariate and multivariate process capability indices

de-Felipe, David; Benedito, Ernest

doi:10.1007/s00170-017-0273-6

Cited by 39 publications

(31 citation statements)

References 67 publications

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“…Whereas, the third component LI takes a value of 0 or 1 [18]. Furthermore, a recent review of multivariate and univariate process capability indices is provided in [23].…”

Section: Process Capabilitymentioning

confidence: 99%

Multivariate Process Capability Assessment and Improvement: A Case Study

Abo-Hawa

El-Din

Nada

2018

The International Conference on Applied Mechanics and Mechanica

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In today's competitive manufacturing environment, the challenge is to responsively produce products with minimum cost and high quality. Achieving and controlling the targeted quality level in manufacturing processes does not only increase customer satisfaction, but it can also result in significant cost and time savings. Further, measuring the process performance is a critical issue in process improvement initiatives. The common practice in several industries is using the Univariate Process Capability Indices (UPCIs) to measure the process performance, which are based on only a single quality characteristic. In most of the applications, it is not acceptable to judge the performance based on a single quality characteristic as it actually relies on more than one characteristic. In this paper, univariate and multivariate PCIs are used to measure the performance of the flare making process. This process is a critical step in the straight fluorescent light bulb production line. In addition, multivariate control charts such as the Hotelling ܶ ଶ as well as the Multivariate Exponentially Weighted Moving Average (MEWMA) are constructed for the collected data to verify that the process is in control before assessing its capability. Besides, Principal Component Analysis (PCA) and Joint Normal Distribution (JND) techniques are applied in the multivariate process capability assessment. In this paper, Multivariate Process Capability Indices (MPCIs) have been evaluated to compare the process performance before and after improvement efforts. In the considered case study, MPCIs provide the user with an overall assessment of process capability regardless of the fluctuations in the individual variables capabilities.

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“…Whereas, the third component LI takes a value of 0 or 1 [18]. Furthermore, a recent review of multivariate and univariate process capability indices is provided in [23].…”

Section: Process Capabilitymentioning

confidence: 99%

Multivariate Process Capability Assessment and Improvement: A Case Study

Abo-Hawa

El-Din

Nada

2018

The International Conference on Applied Mechanics and Mechanica

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“…The paper must be written In today's competitive and globalized markets, industries are obligated to produce high-quality and cost-effective products that consistently meet the consumers and engineering design specifications (Felipe & Benedito, 2017;Goswami & Dutta, 2013;Krolczyk et al, 2015). Subsequently, quality level and process capability have become indispensable attributes and key issues among producers to achieve competitive advantage particularly in the world of knowledgeable consumers (Krolczyk et al, 2015;Leiva, Marchant, Saulo, Aslam, & Rojas, 2014;Lupo, 2015).…”

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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“…A detailed and somewhat critical review on PCIs is available in Kotz and Lovelace, 3 whereas Vännman 4 proposes a unified approach. A thorough review of contributions is reported in de‐Felipe and Benedito 5 and Bong‐Jin and Kwan‐Woo 6 among others). Most of the papers focus on the assumption of normality of the random components, but how capability indices could be efficiently estimated when non normal distributions are involved remains a challenge.…”

Section: Introductionmentioning

confidence: 99%

Model‐based process capability indices: The dry‐etching semiconductor case study

Borgoni

Zappa

2020

Quality & Reliability Eng

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Process capability indices are widely used to check quality standards both at the production level and for business activity. They consider the location and the deviation from specification limits and targets. The literature contains many contributions on this topic both in the univariate and the multivariate context. Motivated by a real semiconductor case study, we discuss the role of rational subgroups and the challenge they present in the computation of capability indices, especially when data refer to lots of products. In addition, our context involves a mix of problems: unilateral specification limit, nonsymmetric distribution of the data, evidence of data from a mixture of distributions, and the need to filter one component of the mixture. After solving the previous issues and because of the peculiar characteristics of semiconductor processes based on the so called "wafers," we contribute to the literature a proposal on how to compute capability indices in the case of heteroscedastic spatial processes. With a generalized additive model, we show that it is possible to estimate a capability surface that allows the identification of regions expected to not be fully compliant with the desired quality standards. K E Y W O R D S dry-etching semiconductor processes, mixture distributions, process capability indices, sampling network 1 | INTRODUCTION Process capability indices (PCIs) are well-known tools to estimate the mean-deviance performance of key product characteristics with respect to both targets and specification limits. The main aim of PCIs was originally to evaluate the instant quality of a process given specification limits and prior to manufacturing. Next, because of their simple interpretation, they have also become a fundamental tool for commercial activity. Nowadays, PCIs are unavoidable instruments for managers and engineers; hence, when assumptions upon which these indices are grounded can be violated, much effort has been made to extend and adapt them to such situations (see Montgomery 1). A flow of contributions is available in the literature for both univariate and multivariate cases. The capability indices that researchers have proposed are so many that, since the masterful work of Kotz and Johnson, 2 the word "avalanche" has been coined. A detailed and somewhat critical review on PCIs is available in Kotz and Lovelace, 3 whereas Vännman 4 proposes a unified approach. A thorough review of contributions is reported in de-Felipe and Benedito 5 and Bong-Jin and Kwan-Woo 6 among others). Most of the papers focus on the assumption of normality of the random components, but how capability indices could be efficiently estimated when non normal distributions are involved remains

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A review of univariate and multivariate process capability indices

Cited by 39 publications

References 67 publications

Multivariate Process Capability Assessment and Improvement: A Case Study

Multivariate Process Capability Assessment and Improvement: A Case Study

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

Model‐based process capability indices: The dry‐etching semiconductor case study

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