An effective powerful test for one-sided supplier selection problem with multiple independent characteristics

Pearn, W. L.; Wu, Chia-Huang; Chuang, C. C.

doi:10.1080/16843703.2016.1169689

Cited by 9 publications

(3 citation statements)

References 13 publications

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“…Thus, a number of studies have employed hypothesis testing to determine whether process capability achieves required standards. 36–40 It is evident that when a customer requires the process capability of the h NTB quality characteristics of a product to reach v , the manufacturer must conduct hypothesis testing on the product’s process performance to ensure that the product quality truly meets customer requirements. This approach can be expressed as follows:…”

Section: Fuzzy Process Capability Analysis For Machined Productmentioning

confidence: 99%

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Fuzzy process capability analysis for machined product with multiple characteristics of symmetric tolerance

Chang

2022

Proceedings of the Institution of Mechanical Engineers, Part B:

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Amidst fierce competition and diversifying customer needs in today’s markets, product development and design have shifted toward a more user-oriented model. This makes it common for products to have multiple quality characteristics. However, simply meeting customer needs is no longer sufficient; quality is also a key factor of a consumer’s willingness to buy a product. To this end, process capability indices have been used to measure the relationship between manufacturing specifications and processing performance, as well as serve as a bridge of communication between manufacturers and clients. In view of this, the loss-based capability index [Formula: see text] that fully reflects process loss and yield is employed in this study to analyze the process performance of each quality characteristic. However, [Formula: see text] must be estimated based on collected samples, in which the measured values of all sample data are expressed with precise values. Uncertainty and imprecision in collected data increase the risk of misjudgment. To reduce this risk, the [Formula: see text] confidence interval of [Formula: see text] is first derived to define fuzzy estimations of both the critical value and index, and develop a fuzzy process capability analysis model for a machined product with multiple quality characteristics of symmetric tolerance. Finally, an industrial example involving a five-way pipe product is presented to illustrate the applicability of the proposed approach. The results show that the proposed fuzzy analysis model makes determination of the process capability of each quality characteristic more reliable and rigorous to ensure that manufactured products meet requirements.

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Section: Fuzzy Process Capability Analysis For Machined Productmentioning

confidence: 99%

“…Step 7: Calculate ratio R l based on A Pl and A Tl to formulate the decision threshold to reliably judge the process capability of each quality characteristic X l (see equation (38)).…”

Section: Standard Operating Procedures For Practical Applicationmentioning

confidence: 99%

Fuzzy process capability analysis for machined product with multiple characteristics of symmetric tolerance

Chang

2022

Proceedings of the Institution of Mechanical Engineers, Part B:

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“…Wang [9] proposes supplier comparison for multiple linear profiles with 1-sided PCIs. Pearn et al [10] presents subtraction method which is more efficient than division method. Prasetyo and Kurniati [11] show the example of 2 suppliers comparison proposed by Pearn and Wu [8].…”

Section: Introductionmentioning

confidence: 99%

The Effects of Weibull Distribution on Supplier Comparison using Lower Process Capability Index: A Case Study

Thavorn

Sudasna-na-Ayudthya

2022

Trends Sci

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In supplier comparison, durability, one of the quality dimensions, is important criterion. Lifetime data is widely used to measure the durability and commonly modeled with Weibull distribution. Many researchers employ process capability indices (PCIs) for comparing suppliers about quality aspect. However, applying these methods bring to some misleading results when lifetime data are considered because the methods are developed under normal distribution. This study attempts to develop the new method for comparing supplier using lower process capability index (Cpl) to apply with the lifetime data. To be guideline for developing the new method, this research aims to study the effects of Weibull distribution on supplier comparison using Cpl via a case study. The effect is studied through 2 performance measures, e.g., producer’s risk and the power of test obtained by Monte Carlo simulation. The simulation results for Weibull distribution indicate that the process’s shape influences on the performance measures obviously. For right-skewed process, the producer’s risks are lower than = 0.05, and power of tests do not exceed 0.10. Although power of tests are close to 1.00 in case of left-skewed process, the risks are greater than = 0.05. For symmetric process, the producer’s risks are close to = 0.05, but the power of tests do not exceed 0.40 in all cases. So, this study points out that the process’s shape is an important factor affecting to the performance of supplier comparison using Cpl leading to the possibly misleading results. To better understand the effects, this paper presents the example of supplier comparison using real data. The example shows that although the popular methods for handling Weibull distribution are applied, the supplier comparison results are not the same. So, manufacturers should be aware in supplier comparison regarding to lifetime data. HIGHLIGHTS Studying the effects of lifetime data modeled with Weibull distribution on the supplier comparison to be guideline for developing the new method Presenting the example of supplier comparison via real data For Weibull distributed process, the process’s shape is an important factor affecting to the performance of supplier comparison The supplier comparison leads to the possibly misleading results although the process is symmetric GRAPHICAL ABSTRACT

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Design and construction of a variables switch-based sampling system for product acceptance determination

Liu

Chen

et al. 2018

Int J Adv Manuf Technol

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An effective powerful test for one-sided supplier selection problem with multiple independent characteristics

Cited by 9 publications

References 13 publications

Fuzzy process capability analysis for machined product with multiple characteristics of symmetric tolerance

Fuzzy process capability analysis for machined product with multiple characteristics of symmetric tolerance

The Effects of Weibull Distribution on Supplier Comparison using Lower Process Capability Index: A Case Study

Design and construction of a variables switch-based sampling system for product acceptance determination

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