Group selection for processes with multiple quality characteristics

Chen, Lin; Pearn, W. L.; Huang, J. Y.; Chen, Yun-Hsuan

doi:10.1080/03610926.2017.1364392

Cited by 9 publications

(16 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Many studies indicate that if a product has multiple quality characteristics, the product can satisfy customers and be viewed as a good product only when all quality characteristics of the process quality meet the required quality level [15,16,[18][19][20][21][22]. According to Huang et al [10], the required quality level of quality characteristics is k σ and the required quality level of bearing connector is kσ.…”

Section: Manufacturing Process Quality Evaluation Indexmentioning

confidence: 99%

A Model for Evaluating the Performance of the Bearing Manufacturing Process

Chen

Hsu

et al. 2019

Applied Sciences

View full text Add to dashboard Cite

Reliability and product process quality are essential in meeting market demand and enhancing competitiveness in the machine tool industry. In addition, manufacturing time performance is also one of the important indices. Therefore, this paper focuses on process quality and manufacturing time and defines a manufacturing time performance index to feedback the acceptance rate of the manufacturing time. The process performance evaluation chart delineated the observations of variations in various workstations, and, hence, in controlling the stability of the work process. The Six Sigma quality indices are constructed by the process accuracy indices and precision indices are represented by X-axis and Y-axis respectively. The process quality evaluation chart evaluates the level of the process quality, as well as proposes the direction of improvement. The manufacturing time performance—Z-axis is used to assess whether the manufacturing time performance meets the requirements. The process performance evaluation chart constructed by this paper makes it easier for researchers to observe which workstations have a process variation in the process, to control the stability of the process effectively, to provide the improvement reference for staff on the scene and to enhance the competitiveness of the industry.

show abstract

Section: Manufacturing Process Quality Evaluation Indexmentioning

confidence: 99%

A Model for Evaluating the Performance of the Bearing Manufacturing Process

Chen

Hsu

et al. 2019

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…Recently, for multi-supplier and multiline [7] considered a group selection problem, in which a group of suppliers is selected based on a multi-line yield index and applied the Bonferroni method to control the overall error rate. Reference [45], [46] considered a group supplier selection problem for multi-supplier and multi-line, in which a supplier group containing the best suppliers is selected based on a multi-line yield index C T pk . In the literature, practical applications where the quality characteristics are represented using profiles have been spread across multiple sectors [27].…”

Section: Supplier Selection Via Process Yield Indexmentioning

confidence: 99%

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

Negash

Kusumaningrum

2019

IEEE Access

View full text Add to dashboard Cite

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.

show abstract

“…Manufacturers have to assess, compare, and choose the suppliers with the best capabilities and utilize the advancements in quality measurement technology to do so (Chou et al, 2014;Lin et al 2018). Assessments are very frequently made in a given space or time, and the quality of products or processes are described by the relationships between a dependent variable and one or more independent variables, which are known as profiles (Maleki et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

“…For instance, Lin and Pearn (2011) and Pearn and Wu (2013) provided examples using the ratio test statistic; Lin and Kuo (2014) found that MCB is superior to the ratio method, and Wang and Tamirat (2016) employed MCB to study CPU fans, which are laptop parts. Lin et al (2018) implemented the Bonferroni method to manage the cumulative error rate when comparing multiple processes. These studies are limited to linear profiles or utilize traditional sampling methods (Cheng and Yang, 2018;Lin et al 2018).…”

Section: Introductionmentioning

confidence: 99%

“…Lin et al (2018) implemented the Bonferroni method to manage the cumulative error rate when comparing multiple processes. These studies are limited to linear profiles or utilize traditional sampling methods (Cheng and Yang, 2018;Lin et al 2018). Occasionally, profiles are better explained by a nonlinear equation rather than by a linear one (Guevara and Vargas, 2015;Maleki et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A novel approach to measure product quality in sustainable supplier selection

Negash

Kartika

Tseng

et al. 2020

Journal of Cleaner Production

View full text Add to dashboard Cite

A gap remains due to the intangible and qualitative criteria used to measure product quality for supplier evaluation and selection. Improving product quality is a crucial strategy for achieving reduce, reuse, recycle, and recovery. Quality characteristics are described as functional relationships (called profiles), and with the advancements in measurement technology, high dimensional data are collected. Nonetheless, prior studies have not addressed sustainable supplier selection where a nonlinear profile characterizes the product quality. Hence, this study aims to provide a novel approach to measure product quality using the process yield index, presents multiple comparisons with the best and difference test statistics and proposes a Bonferroni correction method. This study applies a Monte Carlo simulation to find the selection power and the required number of profiles. The statistical properties are investigated, and a comparison study is performed. The results show that multiple comparisons with the best outperform the Bonferroni method regarding the sample size requirement and power, and the number of levels and profiles were found to impact the power of the statistical tests. The required number of profiles and the critical value are tabulated for decision-makers.

show abstract

Group selection for processes with multiple quality characteristics

Cited by 9 publications

References 15 publications

A Model for Evaluating the Performance of the Bearing Manufacturing Process

A Model for Evaluating the Performance of the Bearing Manufacturing Process

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

A novel approach to measure product quality in sustainable supplier selection

Contact Info

Product

Resources

About