A bibliography of the literature on process capability indices: 2000–2009

Yum, Bong‐Jin; Kim, Kwan-Woo

doi:10.1002/qre.1115

Cited by 134 publications

(69 citation statements)

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“…For thorough discussions on PCIs and the reviews for the development of PCIs, refer to Kotz and Lovelace (1998), Kotz and Johnson (2002), and Wu, Pearn, and Kotz (2009). In addition, Yum and Kim (2011) summarised the related literature of process capability analysis from 2000 to 2009. These basic capability indices have been defined explicitly as follows:…”

Section: Process Capability Indicesmentioning

confidence: 99%

Developing a variables repetitive group sampling scheme by considering process yield and quality loss

Chen

2014

International Journal of Production Research

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Acceptance sampling is a useful tool for determining whether submitted lots should be accepted or rejected. With the current increase in outsourcing production processes and the high-quality levels required, it is very desirable to have an efficient and economic sampling scheme. This paper develops a variables repetitive group sampling (RGS) plan that accounts for the process yield (meeting the manufacturing specifications) and the quality loss (variation from the target). The plan parameters are determined by solving a nonlinear optimisation problem. This implies that the plan parameters minimise the average sample number required for inspection and fulfil the classical two-point conditions on the operating characteristic (OC) curve. Besides, this paper investigates the efficiency of the proposed plan and compares it with the existing variables single sampling plan. Tables of the plan parameters for the proposed variables RGS plan are provided and an application example is presented for illustration. IntroductionAcceptance sampling is one of the most practical tools in classical quality control and assurance applications, which deal with quality contracts for product orders between factories and their customers. Acceptance sampling plans provide the producer and the consumer with a general criterion for lot sentencing. A well-designed sampling plan can substantially reduce the difference between the required and the actual supplied product quality Wu 2006, 2007). Unfortunately, it cannot avoid the risk of accepting unwanted poor product lots, nor can it avoid the risk of rejecting good product lots without implementing 100% inspection (e.g. Montgomery 2009). The criteria used to measure the performance in an acceptance sampling plan are usually based on the operating characteristic (OC) curve, which quantifies the risks of producers and consumers. The OC curve plots the probability of accepting a lot against the actual quality level of the submitted lots. In other words, the OC curve shows the discriminatory power of the sampling plan, which provides the producer and the buyer with a common base for judging whether the sampling plan is appropriate. Sherman (1965) developed a new type of sampling plan, called the repetitive group sampling (RGS) plan, for attributes. The operating procedure of this RGS plan is similar to that of the sequential sampling plan. Balamurali and Jun (2006) extended the RGS concept to variables inspection for a normally distributed quality characteristic. They also compared the efficiency of the variables RGS plan with the variables single and double sampling plans. Their results indicate that the variables RGS plan gives the desired protection with the minimum average sample number (ASN) for inspection. It is highly desirable to have an efficient and economic acceptance sampling scheme, especially when the required quality level is very high (Montgomery 2009). Therefore, the main purpose of this paper is to develop a variables RGS plan for product acceptance determination based on the ...

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Section: Process Capability Indicesmentioning

confidence: 99%

Developing a variables repetitive group sampling scheme by considering process yield and quality loss

Chen

2014

International Journal of Production Research

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“…It mainly relies on the accurate disclosure of process capability information, which is realised by several process capability indices (PCIs). Given an important quality characteristic X, the PCIs quantify the extent to which the buyer-stipulated specification limits (tolerance) of the product meet the supplier's production performance, in terms of process central tendency and variation (Yum and Kim 2011). The (unitless) values of PCIs genuinely reflect the process yield and/or loss, which are crucial criteria for determining market acceptance or rejection of the products (lots).…”

Section: Process Capability Studiesmentioning

confidence: 99%

A lots-dependent variables sampling plan considering supplier’s process loss and buyer’s stipulated specifications requirement

Wang

Shu

2015

International Journal of Production Research

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A well-established scheme and mechanism for deciding product acceptance is perceived as a win-win situation for the long-term supplier-buyer relationship. In this paper, we develop a lots-dependent variables sampling scheme for product acceptance determination. The dependent state is based on the sample information of the process capability index that incorporates the supplier's process loss and the buyer's demanded specifications requirement. This main scheme is implemented by a three-rule process that accepts or rejects a related lot conditional on the sample results of past lots. The plan-operational parameters satisfying the desired quality levels and constraining the supplier-buyer risks are determined by a non-linear optimisation model. In performance comparisons, our proposed plan demonstrated higher cost effectiveness and discriminatory power than the traditional variables single sampling plan. Finally, industrial applicability of our recommended sampling plans was investigated in a case study.

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“…Different process capability indices have been proposed by different researchers. A detailed review of process capability indices can be seen in Kotz & Johnson [1], and a bibliography of process capability papers is found in Spiring, Leung, Cheng & Yeung [2] and Yum and Kim [3].…”

Section: Opsommingmentioning

confidence: 99%

Process Capability Index-Based Control Chart for Variables

Adeoti¹,

Olaomi²

2017

SAJIE

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This paper proposes a process capability index-based control chart for variables using the Downton estimator with a specified Cp value. The proposed chart is able to address the issue of control and capability simultaneously. We also provide a control chart constant to construct the process capability index-based control chart. A numerical example is presented to demonstrate the application of the proposed chart, and the effect of non-normality is discussed. The result shows that the proposed control chart performs better in monitoring and assessing processes, and eliminates the usual twostage procedure reflected in the literature. OPSOMMING'n Prosesvermoë indeksgebaseerde beheerkaart word voorgestel vir veranderlikes met gebruik van die 'Downton' beramer met 'n gespesifiseerde Cp waarde. Die voorgestelde beheerkaart kan die kwessie rondom die beheer en vermoë gelyktydig aanspreek. 'n Beheerkaartkonstante word ook verskaf om die prosesvermoë indeksgebaseerde beheerkaart te skep. 'n Numeriese voorbeeld word voorgehou om die toepassing van die voorgestelde beheerkaart te illustreer. Die effek van nie-normaliteit word ook bespreek. Die resultaat toon dat die voorgestelde beheerkaart beter vertoon in die monitering en assessering van prosesse en dit elimineer die gewone twee stap prosedure wat in die literatuur gevind word.

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A bibliography of the literature on process capability indices: 2000–2009

Cited by 134 publications

References 1 publication

Developing a variables repetitive group sampling scheme by considering process yield and quality loss

Developing a variables repetitive group sampling scheme by considering process yield and quality loss

A lots-dependent variables sampling plan considering supplier’s process loss and buyer’s stipulated specifications requirement

Process Capability Index-Based Control Chart for Variables

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