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The purpose of this paper is to apply fuzzy spectrum in order to collect the vague and imprecise data and to employ the fuzzy U control chart in variable sample size using fuzzy rules. This approach is improved and developed by providing some new rules.

The fuzzy operating characteristic (FOC) curve is applied to investigate the performance of the fuzzy U control chart. The application of FOC presents fuzzy bounds of operating characteristic (OC) curve whose width depends on the ambiguity parameter in control charts.

To illustrate the efficiency of the proposed approach, a practical example is provided. Comparing performances of control charts indicates that OC curve of the crisp chart has been located between the FOC bounds, near the upper bound; as a result, for the crisp control chart, the probability of the type II error is of significant level. Also, a comparison of the crisp OC curve with OC_avg curve and FOC_α curve approved that the probability of the type II error for the crisp chart is more than the same amount for the fuzzy chart. Finally, the efficiency of the fuzzy chart is more than the crisp chart, and also it timely gives essential alerts by means of linguistic terms. Consequently, it is more capable of detecting process shifts.

This research develops the fuzzy U control chart with variable sample size whose output is fuzzy. After creating control charts, performance evaluation in the industry is important. The main contribution of this paper is to employs the FOC curve for evaluating the performance of the fuzzy control chart, while in prior studies in this area, the performance of fuzzy control chart has not been evaluated.

Shewhart's control charts are the most important statistical process control tools that play a role in inspecting and producing quality control. The purpose of this paper is to investigate the attributes of fuzzy U control chart.

If the data were uncertain, they were converted into trapezoidal fuzzy number and the fuzzy upper and lower control limits were trapezoidal fuzzy number calculated using fuzzy mode approach. The result was grouped into four categories (in control, out of control, rather in control, rather out of control). Finally, a case study was presented and the method coding was done in MATLAB software using design U control chart; then, the results were verified.

The definition of fuzzy numbers for each type of defect sensitivity and the unit can be classified into four groups: in-control and out-of-control, rather in-control and rather out-of-control which represent the actual quality of the products. It can be concluded that fuzzy control chart is more sensitive on recognition out of control patterns.

This paper studies the use of control charts, specifically the attributes of a fuzzy U control chart, for monitoring defects in the format of a case study.

The purpose of this paper is to explore the relationship between effective strategies to improve the quality and quality management of allocated resources for the successful implementation of the strategies. For this purpose, three quality management resources (human, organizational and technological) and eight different strategies related to quality are considered.

The paper employs the fuzzy analytic network process (FANP) to prioritize and model the interactions between eight strategies, the three types of resources (human, organizational and technological) needed for effective strategy implementation and the ability to enhance quality. Then, Goal Programming (GP) is formulated by the output of the FANP to identify the extent to which each single strategy is inhibited by a lack of (or overloaded by) resources.

The first three priorities of strategies identified by the FANP include continuous management of quality system, continuous use of human knowledge and continuous approach toward target, and the order of resources is as follows: human resources, organizational resources and technological resources. The results obtained showed the largest share of human resources and its crucial role in improving the quality of the products. The contribution of organizational resources amounts to half of the contribution of human resources.

The main contribution of this paper is to employ the FANP to prioritize, whereas in prior studies in this area, priorities were conducted as definitive, and uncertainty in the opinion of experts was not considered. In this paper, the FANP–GP combined method is used.

Bayesian networks (BNs) are implemented for monitoring a process via statistical process control (SPC) where attribute data are available on output from the system. The paper aims to discuss this issue.

The BN provides a graphical and numerical tool to help a manager understand the effect of sample observations on the probability that the process is out-of-control and requires investigation. The parameters for the BN SPC model are statistically designed to minimize the out-of-control average run length (ARL) of the process at a specified in-control ARL and sample size.

The BN model outperforms adaptive np control charts in all experiments, except for some cases where only a large change in the proportion of sample defects is relevant. The BN is particularly useful when small sample sizes are available and when managers need to detect small changes in the proportion of defects produced by the process.

The BN model is statistically designed and parameters are chosen to minimize out-of-control ARL. Future advancements will address the economic design of BNs for SPC with attribute data.

The BNs allow qualitative knowledge to be combined with sample data, and the average percentage of defects can be modeled as a continuous random variable. The framework of the BN easily permits classification of the system operation into two or more states, so diagnostic analysis can be performed simultaneously with statistical inference.

The control charts are used in many production areas because they give an idea about the quality characteristic(s) of a product. The control limits are calculated and the data are examined whether the quality characteristic(s) is/are within these limits. At this point, it may be confusing to comment, especially if it is slightly below or above the limit values. In order to overcome this situation, it is suitable to use fuzzy numbers instead of crisp numbers. The purpose of this paper is to demonstrate how to create control limits of X ¯ -R control charts for a specified data set of interval type-2 fuzzy sets.

There are methods in the literature, such as defuzzification, distance, ranking and likelihood, which may be applicable for interval type-2 fuzzy set. This study is the first that these methods are adapted to the X ¯ -R control charts. This methodology enables interval type-2 fuzzy sets to be used in X ¯ -R control charts.

It is demonstrated that the methods – such as defuzzification, distance, ranking and likelihood for interval type-2 fuzzy sets – could be applied to the X ¯ -R control charts. The fuzzy control charts created using the methods provide similar results in terms of in/out control situations. On the other hand, the sample points depicted on charts show similar pattern, even though the calculations are different based on their own structures. Finally, the control charts obtained with interval type-2 fuzzy sets and the control charts obtained with crisp numbers are compared.

Based on the related literature, research works on interval type-2 fuzzy control charts seem to be very limited. This study shows the applicability of different interval type-2 fuzzy methods on X ¯ -R control charts. For the future study, different interval type-2 fuzzy methods may be considered for X ¯ -R control charts.

The unique contribution of this research to the relevant literature is that interval type-2 fuzzy numbers for quantitative control charts, such as X ¯ -R control charts, is used for the first time in this context. Since the research is the first adaptation of interval type-2 fuzzy sets on X ¯ -R control charts, the authors believe that this study will lead and encourage the people who work on this topic.

Constructing a fuzzy control chart with interval-valued fuzzy data is an important topic in the fields of medical, sociological, economics, service and management. In particular, when the data illustrates uncertainty, inconsistency and is incomplete which is often the. case of real data. Traditionally, we use variable control chart to detect the process shift with real value. However, when the real data is composed of interval-valued fuzzy, it is not feasible to use such an approach of traditional statistical process control (SPC) to monitor the fuzzy control chart. The purpose of this paper is to propose the designed standardized fuzzy control chart for interval-valued fuzzy data set.

The general statistical principles used on the standardized control chart are applied to fuzzy control chart for interval-valued fuzzy data.

When the real data is composed of interval-valued fuzzy, it is not feasible to use such an approach of traditional SPC to monitor the fuzzy control chart. This study proposes the designed standardized fuzzy control chart for interval-valued fuzzy data set of vegetable price from January 2009 to September 2010 in Taiwan obtained from Council of Agriculture, Executive Yuan. Empirical studies are used to illustrate the application for designing standardized fuzzy control chart. More related practical phenomena can be explained by this appropriate definition of fuzzy control chart.

This paper uses a simpler approach to construct the standardized interval-valued chart for fuzzy data based on traditional standardized control chart which is easy and straightforward. Moreover, the control limit of the designed standardized fuzzy control chart is an interval with (LCL, UCL), which consists of the conventional range of classical standardized control chart.

The purpose of this paper is to propose a framework for the analysis of students’ ratings of teaching quality in higher education and the disclosure of risky issues undermining the quality of teaching and courses that require attention for continuous improvement. The framework integrates two decision-based methods: the standardized u-control chart and the ABC analysis using fuzzy weights. The control chart, using the students’ ratings, allows the identification of those courses requiring an improvement of teaching quality in the short-medium term. While the ABC analysis uses fuzzy weights to deal with the vagueness and uncertainty of students’ teaching evaluations and provides a risk map of the potential areas of teaching performances improvement in the long term. The proposed framework allows the identification of teaching and course quality aspects that need corrective actions in response to students’ criticisms in accordance with different levels of priority.

This study adopts two methods, commonly used in industrial applications, i.e. the u-control chart and ABC analysis. Combining the results of a literature review on teaching evaluation and the application of these two methods as building blocks for the assessment, a framework to detect potential risks reducing teaching quality in higher education is proposed. The application of the framework is shown through an action-based case study developed in an Italian public university.

The study proposes a framework that combines two methods, i.e. u-control chart and ABC analysis with fuzzy weights, to support the assessment of teaching and course quality. The framework is proposed as an assessment approach of the teaching performance in higher education with the purpose to continuously improve the quality of teaching and courses both in the short, medium and long term.

The study provides an original contribution to the understanding of how to analyze students’ evaluation of teaching performance in order to take proper and timely decisions on corrective actions in response to the need of continuously improving the level of teaching and course quality.

The purpose of this paper is to demonstrate the use of artificial intelligence methods in quality control and improvement. The paper introduces a systematic approach for the design of fuzzy control charts of tip shear carpets.

There are certain steps for designing fuzzy control charts. All input, state and output variables of the carpet plant and partition of the universe of discourse were first determined. The interval spanned by each variable and the number of fuzzy subsets each assigned with a linguistic label were identified. Then, the adaptive capability of neural network was used to determine the membership functions for each fuzzy subset. The fuzzy relationship functions between the inputs and outputs were assigned to form the fuzzy rule base (controller) in order to normalize the variables and certain intervals. Fuzzification of input parameters and max‐min composition of rules for inferring crisp outputs was the next step. The aggregation of fuzzified outputs and defuzzification of the outputs were the last step of this study, which helped to produce crisp outputs for latex weight.

Fuzzy linguistic terms were employed for overall quality assessment and rating of the end product. The outcomes of neuro‐fuzzy system were good supplements to other statistical process control tools.

Lack of qualified domain experts, knowledge acquisition of process parameters and time limitation for training of neuro‐fuzzy model were primary limitations.

The approach is more flexible and meaningful to identify the quality distribution of a product. The qualitative aspect of human reasoning for decision making was employed in this approach.

The paper is original and the first such work for local industry.

The purpose of this paper is to present a procedure for finding unusual patterns in accredited tests using a rapid processing method for analyzing video records. The procedure uses the temporal differencing technique for object tracking and considers only frames not identified as statistically redundant.

An accreditation organization is responsible for accrediting facilities to undertake testing and calibration activities. Periodically, such organizations evaluate accredited testing facilities. These evaluations could use video records and photographs of the tests performed by the facility to judge their conformity to technical requirements. To validate the proposed procedure, a real-world data set with video records from accredited testing facilities in the field of vehicle safety in Brazil was used. The processing time of this proposed procedure was compared with the time needed to process the video records in a traditional fashion.

With an appropriate threshold value, the proposed procedure could successfully identify video records of fraudulent services. Processing time was faster than when a traditional method was employed.

Manually evaluating video records is time consuming and tedious. This paper proposes a procedure to rapidly find unusual patterns in videos of accredited tests with a minimum of manual effort.

Considers the problem of determining economic statistical np‐control chart designs under the fuzzy environment of closely satisfying type I and II errors. Goes on to model the problem as fuzzy mathematical programming, and uses a heuristic method to obtaining the solution.