Select better suppliers based on manufacturing precision for processes with multivariate data

Yen, Ching Ho; Pearn, W. L.

doi:10.1080/00207540701796985

Cited by 11 publications

(22 citation statements)

References 14 publications

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“…Many studies, including Chen and Chen (2006), Yen and Pearn (2009) and Wu, Liao, and Yang (2013), have made various contributions to solving the supplier evaluation problems. Assume that a manufacturer of the TSP terminal application product wants to assess his TSP supplier's process quality to determine whether to accept the TSP products from this supplier.…”

Section: An Applicationmentioning

confidence: 99%

“…By analysing the PCIs, a production department can trace and improve a poor process so that the quality level can be enhanced and the customer requirements can be satisfied. For further information about the applications of PCIs, we refer the readers to , Pearn, Chang et al (2004), Yen and Pearn (2009), Chen, Huang, and Hung (2008), Wu, Pearn, and Kotz (2009), , Cheng (2007, 2010) and Ali and Riaz (2014). Greenwich and Jahr-Schaffrath (1995) introduced an 'incapability' index called C pp .…”

Section: Introductionmentioning

confidence: 99%

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Assessing process incapability when collecting data from multiple batches

Liao

2015

International Journal of Production Research

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Process capability indices have been widely used in industry to measure the ability of firms to meet quality specifications. The C pp index not only can directly reflect the process incapability, but also provides more information regarding process inaccuracy and imprecision than other indices. Therefore, it can help contract manufacturers to better understand their processes for improving quality performance. In real applications, the sampling data are often collected from multiple batches. Based on this condition, this study proposes an alternate method to assess the true C pp value. The concept of generalised pivotal quantity is used, and the generalised confidence interval is derived to estimate C pp . Furthermore, we provide simulations to compare the performances of our proposed method with an existing method. The results show that the empirical confidences of the two methods are affected by the degree of process departure. Therefore, practitioners can select the appropriate one to assess the process capability, depending on the degree of process departure.

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Section: An Applicationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Assessing process incapability when collecting data from multiple batches

Liao

2015

International Journal of Production Research

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“…They also presented a real case study to illustrate how the proposed procedure could be constructed and applied. Yen and Pearn (2008) considered the supplier selection problem based on manufacturing precision in which the processes involved multiple quality characteristics. They derived the distribution of the corresponding test statistic which is a statistical test procedure using the estimator of multivariate extension of C p to judge whether or not the capability of one process is superior to another process.…”

Section: Quality Conditionmentioning

confidence: 99%

A new perspective on fuzzy process capability indices: Robustness

Kaya

Kahraman

2010

Expert Systems with Applications

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“…The global business situation is aggressively competitive; it requires companies to manufacture ''defect-free'' (zero defect) products [1]. Once defective products reach the market, it does cause not only damage to market potential but also additional repair costs, which is unfavorable for the firm [2]. Several aspects can determine and improve quality; having the right supplier to work with is among the significant elements.…”

Section: Introductionmentioning

confidence: 99%

Multiple Comparisons With the Best and Difference Test Statistic for Supplier Selection for Nonlinear Profiles

Negash

Kusumaningrum

2019

IEEE Access

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It is vital to choose the right supplier to reduce cost and provide high-quality products. However, a gap remains because the supplier's process is mainly measured using qualitative and intangible criteria. Further, with technological advances, the measurement of quality characteristics is transforming through smart data sensors. With a specific time or space measurements can be done at high frequency. The functional relationships between the measures or profiles can be established. The profiles indicate the pattern in the data. The literature focused on the case when quality characteristics are described by linear profile and consider symmetric tolerance. However, in a real-world application, nonlinear profiles and asymmetric tolerance is frequently found. This study proposed multiple comparisons with the best and the difference test statistic methods to select the best suppliers when the quality characteristics are described by nonlinear profile with asymmetric tolerances. A Monte Carlo simulation study is conducted, computer programs are written in the R programming language. The result indicated in terms of rejecting inferior suppliers, the multiple comparisons with the best method perform better than the difference test statistics. With the proposed methods, managers can make decisions using a single, easy-to-understand index. Also, these methods can handle any number of suppliers. For the convenience of a decision-maker, critical values, and profile size requirements are provided. An illustrative example is included to give a better insight into the proposed methods.INDEX TERMS Supplier selection, nonlinear profile, multiple comparisons with the best, difference test statistics, process yield indices.

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Select better suppliers based on manufacturing precision for processes with multivariate data

Cited by 11 publications

References 14 publications

Assessing process incapability when collecting data from multiple batches

Assessing process incapability when collecting data from multiple batches

A new perspective on fuzzy process capability indices: Robustness

Multiple Comparisons With the Best and Difference Test Statistic for Supplier Selection for Nonlinear Profiles

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