Fuzzy Supplier Selection Method Based on Smaller-The-Better Quality Characteristic

Yu, Chun-Min; Chen, Kuen‐Suan; Lai, Kuei‐Kuei; Hsu, Chin-Hsien

doi:10.3390/app10103635

Cited by 22 publications

(21 citation statements)

References 44 publications

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“…In addition, due to cost and timeliness considerations, samples are generally small, which lowers estimation accuracy. To increase the accuracy of estimation and decrease the uncertainty of measurement data, many researchers use the confidence interval of indices to construct confidence interval-based fuzzy evaluation models [22][23][24][25][26]. In this study, we first derive the confidence interval for the unilateral specification process' quality indices, and then propose a confidence interval-based fuzzy membership function to establish the two-tailed fuzzy testing rules for a single index.…”

Section: Introductionmentioning

confidence: 99%

Two-Tailed Fuzzy Hypothesis Testing for Unilateral Specification Process Quality Index

Luo

Hsu

et al. 2020

Mathematics

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The quality characteristics with unilateral specifications include the smaller-the-better (STB) and larger-the-better (LTB) quality characteristics. Roundness, verticality, and concentricity are categorized into the STB quality characteristics, while the wire pull and the ball shear of gold wire bonding are categorized into the LTB quality characteristics. In terms of the tolerance, zero and infinity () can be viewed as the target values in line with the STB and LTB quality characteristics, respectively. However, cost and timeliness considerations, or the restrictions of practical technical capabilities in the industry, mean that the process mean is generally far more than 1.5 standard deviations away from the target value. Researchers have accordingly proposed a process quality index conforming to the STB quality characteristics. In this study, we come up with a process quality index conforming to the LTB quality characteristics. We refer to these two types of indices as the unilateral specification process quality indices. These indices and the process yield have a one-to-one mathematical relationship. Besides, the process quality levels can be completely reflected as well. These indices possess unknown parameters. Therefore, sample data are required for calculation. Nevertheless, interval estimates can lower the misjudgment risk resulting from sampling errors more than point estimates can. In addition, considering cost and timeliness in the industry, samples are generally small, which lowers estimation accuracy. In an attempt to increase the accuracy of estimation as well as overcome the uncertainty of measured data, we first derive the confidence interval for unilateral specification process quality indices, and then propose a fuzzy membership function on the basis of the confidence interval to establish the two-tailed fuzzy testing rules for a single indicator. Lastly, we determine whether the process quality has improved.

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Section: Introductionmentioning

confidence: 99%

Two-Tailed Fuzzy Hypothesis Testing for Unilateral Specification Process Quality Index

Luo

Hsu

et al. 2020

Mathematics

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“…Then, the proposed two-tailed fuzzy testing method is built on ROPI as an improved decision rule. The sample size is usually not too large, owing to the consideration of cost and timeliness, so it will affect the accuracy of statistical inference [20][21][22]24,25]. In addition to cost and timeliness, the advantage of this approach is not only to minimize the chance of miscalculations resulting from the sample errors but also to enhance the accuracy of testing.…”

Section: Discussionmentioning

confidence: 99%

“…Based on Chen [18], a large sampling error takes place because the sample size is not large enough. Some studies highlighted that timeliness is extremely critical in a competitive market, and samples are rarely oversized [20][21][22]. Moreover, that is why we came up with a two-tailed fuzzy testing method.…”

Section: Numerical Examplementioning

confidence: 99%

“…Obviously, it is more effective to evaluate a store's operating performance by means of the time interval between customers' arrivals than to collect the number of customers' arrivals per unit time [13,18,19]. In addition, some studies have pointed out that, based on cost considerations and practicality, the sample size, n, is usually not too large in practice, so it will affect the accuracy of statistical inference [20][21][22]. This study first derived index estimates based on sample data, to find out the best estimation formula of the index as the testing statistic.…”

Section: Introductionmentioning

confidence: 99%

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A Fuzzy Evaluation Decision Model for the Ratio Operating Performance Index

Chen

et al. 2021

Mathematics

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In order to take into account cost and timeliness and enhance accuracy testing, this study developed the fuzzy number and membership function, using the confidence interval of ratio operating performance index. Subsequently, according to the statistical test rules and the application of the fuzzy number and membership function, a fuzzy evaluation decision model for the operating performance index is proposed, to evaluate if the business performance reaches the needed level. Based on the abovementioned, the evaluation model in this study took into account not only timeliness but also accuracy, so that it could grasp the opportunity of improvement for operating organizations with poor operating performance after being evaluated. This fuzzy evaluation decision model for the operating performance index constructs a fuzzy membership function retrieved from an index’s confidence interval, reducing the chance of miscalculation due to sampling mistakes and improving the efficiency of evaluation. Finally, in order to facilitate the application of readers and the industry, this paper uses cases to explain the proposed fuzzy verification method. On the whole, the model proposed in this paper is a data-based auxiliary tool for the service operating performance improvement strategy.

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“…Regarding product quality, quite a few quality control engineers and experts in statistics have been dedicated to their studies on process capability indicators in terms of evaluation, analysis, and improvement [3,31,[34][35]. In addition, Six Sigma is also a method commonly adopted in the industry to improve process quality [15,[21][22]25].…”

Section: Introductionmentioning

confidence: 99%

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In practice, lifetime performance index CL has been a method commonly applied to the evaluation of quality performance. L is the upper or lower limit of the specification. The product lifetime distribution is mostly abnormal distribution. This study explored that the lifetime of commodities comes from exponential distribution. Complete data collection is the primary goal of analysis. However, the censoring type is one of the most commonly used methods due to considerations of manpower and material cost or the timeliness of product launch. This study adopted Type-II right censoring to find out the uniformly minimum variance unbiased (UMVU) estimator of the lifetime performance index CL and its probability density function. Afterward this study obtained the 100×(1-α)% confidence interval of the lifetime performance index CL as well as created the uniformly most powerful (UMP) test and the power of the test for the product lifetime performance index. Last, this study came up with a numerical example to demonstrate the suggested method as well as the application of the model.

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Fuzzy Supplier Selection Method Based on Smaller-The-Better Quality Characteristic

Cited by 22 publications

References 44 publications

Two-Tailed Fuzzy Hypothesis Testing for Unilateral Specification Process Quality Index

Two-Tailed Fuzzy Hypothesis Testing for Unilateral Specification Process Quality Index

A Fuzzy Evaluation Decision Model for the Ratio Operating Performance Index

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