A Fuzzy Evaluation Decision Model for the Ratio Operating Performance Index

Li, Mingyuan; Chen, Kuen‐Suan; Yu, Chun-Min; Yang, Chun-Ming

doi:10.3390/math9030262

Cited by 6 publications

(7 citation statements)

References 29 publications

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“…Based on the above-mentioned, the method proposed in this paper can not only meet the needs of enterprises to pursue rapid response but also help the industry achieve the effects of cost reduction, energy saving and carbon reduction, and then it moves towards the goal of innovative and intelligent management. In addition, the as- Based on Li et al [20], take φ = 0.2, and since d S /d T = 0.05 < φ, then reject and conclude that A PI < 5. According to the statistical test, the upper limit of index A PI is U A PI0 = 5.08>A 0 = 5, so H 0 is not rejected, and A PI > A 0 = 5 is concluded.…”

Section: Discussionmentioning

confidence: 93%

“…Thus, an operating performance index was proposed. Li et al [20] revised the operating performance index as a more concise ratio operating performance index according to the concept that the index is a simple and unitless management tool. Therefore, based on the concept of Li et al [20], this paper defines an APP performance index as an evaluation tool for the APP performance.…”

Section: Introductionmentioning

confidence: 99%

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Fuzzy Evaluation Model of Bank APP Performance Based on Circular Economy Thinking

et al. 2021

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As the environment of the Internet of Things (IoT) gradually becomes common and mature, various smart application (APP) platforms have sprung up, making what we are doing more convenient, more economical and more efficient. Then, this paper used a bank APP as the research background to discuss issues related to smart APPs. Obviously, through the bank APPs, customers can complete their transfer and payment for various expenses at home, eliminating the inconvenience of going out, which not only can alleviate traffic congestion as well as reduce carbon emissions but also can save the manpower expenditure costs for banks. Consequently, improving APP performance and increasing the number of users of an APP is a very important issue. Therefore, this paper proposed an APP performance index to evaluate the performance of a bank APP. This APP performance index is to evaluate the performance of the APP through the time interval of customers’ access to the APP. The shorter the time interval is, the greater the number of users within a unit time is. In addition, based on cost considerations and effectiveness, the sample size n is usually not too large in practice, in order to make decisions quickly and accurately in a short time. Since the fuzzy testing model based on the confidence interval can be integrated with the past accumulated experience of data experts, the testing accuracy can be leveled up under the condition of small-sized samples. Accordingly, a fuzzy evaluation model was proposed to evaluate whether the performance of the bank APP can reach the required level, and this model was also regarded as a basis for decision-making to determine whether to improve the bank APP. At the same time, we can grasp the opportunities for improvement, achieve the effect of cost reduction, energy saving and carbon reduction, and further move towards the goal of innovative and intelligent management.

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Section: Discussionmentioning

confidence: 93%

Section: Introductionmentioning

confidence: 99%

Fuzzy Evaluation Model of Bank APP Performance Based on Circular Economy Thinking

et al. 2021

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“…According to Chen et al [22] and Li et al [23], the α-cuts of the half-triangle-like shaped fuzzy number UC PI M can be expressed below:…”

Section: Fuzzy Testing For Process Improvement Capability Indexmentioning

confidence: 99%

Fuzzy Evaluation Model of Process Improvement Capability with Costs Consideration

Chen

Hsu

et al. 2021

Applied Sciences

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The Taguchi capability index, which reflects the expected loss and the yield of a process, is a useful index for evaluating the quality of a process. Several scholars have proposed a process improvement capability index based on the expected value of the Taguchi loss function as well as the corresponding cost of process improvement. There have been a number of studies using the Taguchi capability index to develop suppliers’ process quality evaluation models, whereas models for evaluating suppliers’ process improvement potential have been relatively lacking. Thus, this study applies the process improvement capability index to develop an evaluation model of the supplier’s process improvement capability, which can be provided to the industry for application. Besides, owing to the current need to respond quickly, coupled with cost considerations and the limits of technical capabilities, the sample size for sampling testing is usually not large. Consequently, the evaluation model of the process improvement capability developed in this study adopts a fuzzy testing method based on the confidence interval. This method reduces the risk of misjudgment due to sampling errors and improves the testing accuracy because it can incorporate experts and their accumulated experiences.

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“…Based on the above-mentioned, Chen et al [2] has put forward a fuzzy evaluation model of bank APP performance to promote the APP service performance, enhance customers' satisfaction with the use of the APP, and increase the number of users of the APP by means of evaluation, analysis, and improvement. According to some research, the number of customers (N t ) accessing an APP is a Poisson distribution with a rate λt [17][18][19][20][21]. As noted by Chen et al [2], the mean time (T j ) is the average time that two continuous customers access an APP, and then T j is a distributed exponential random variable with mean τ = 1/λ.…”

Section: Introductionmentioning

confidence: 99%

A Fuzzy Improvement Testing Model of Bank APP Performance

et al. 2022

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Numerous studies have pointed out that the issue of global warming is getting increasingly more serious. Therefore, the concepts of circular economy (CE) and sharing economy have been more and more valued by enterprises and governments. With the gradual popularization and maturity of the Internet of Things (IoT), various smart APP platforms have sprung up rapidly. For example, the fuzzy evaluation model of bank APP performance was proposed in such an environment, aiming to improve the APP service performance by means of evaluating, analyzing, improving, and enhancing customers’ satisfaction with their use of APPs, and increasing the number of users of APPs. Since the follow-up of the article did not mention the improved testing model used to verify the improvement effect, this paper then proposed a fuzzy two-tailed testing model with two indices before and after the improvement based on the confidence interval to verify whether the improvement has had a significant effect. This complete bank APP fuzzy performance evaluation, analysis, and improvement model measured the bank APP operation performance using customer time intervals, so the data collection time was short. Not only can it meet enterprises’ need for rapid response and grasp the opportunity for improvement to achieve the effect of energy-saving and carbon reduction, but it also can satisfy enterprises’ requirement to pursue fast and accurate decision-making. Furthermore, the fuzzy two-tailed test proposed by this paper was based on the confidence interval, which can reduce the risk of misjudgment caused by sampling error. Plenty of studies have indicated that the designs based on confidence intervals can integrate expert experience and past data so that the accuracy of testing can be maintained in the case of small-sized samples.

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A Fuzzy Evaluation Decision Model for the Ratio Operating Performance Index

Cited by 6 publications

References 29 publications

Fuzzy Evaluation Model of Bank APP Performance Based on Circular Economy Thinking

Fuzzy Evaluation Model of Bank APP Performance Based on Circular Economy Thinking

Fuzzy Evaluation Model of Process Improvement Capability with Costs Consideration

A Fuzzy Improvement Testing Model of Bank APP Performance

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