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1 – 10 of over 2000
Article
Publication date: 12 August 2014

Yu-Ting Cheng and Chih-Ching Yang

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…

Abstract

Purpose

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.

Design/methodology/approach

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

Findings

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.

Originality/value

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.

Details

Management Decision, vol. 52 no. 7
Type: Research Article
ISSN: 0025-1747

Keywords

Article
Publication date: 29 July 2014

Soroush Avakh Darestani, Azam Moradi Tadi, Somayeh Taheri and Maryam Raeiszadeh

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…

Abstract

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Originality/value

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.

Details

International Journal of Quality & Reliability Management, vol. 31 no. 7
Type: Research Article
ISSN: 0265-671X

Keywords

Article
Publication date: 15 August 2018

Hatice Ercan Teksen and Ahmet Sermet Anagun

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…

Abstract

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Research limitations/implications

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.

Originality/value

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.

Details

Journal of Enterprise Information Management, vol. 31 no. 6
Type: Research Article
ISSN: 1741-0398

Keywords

Article
Publication date: 26 April 2018

Somayeh Fadaei and Alireza Pooya

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…

Abstract

Purpose

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.

Design/methodology/approach

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.

Findings

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 OCavg curve and FOCα 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.

Originality/value

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.

Details

The TQM Journal, vol. 30 no. 3
Type: Research Article
ISSN: 1754-2731

Keywords

Article
Publication date: 31 December 2015

Soroush Avakh Darestani and Mina Nasiri

In this context, process capability indices (PCI) reveal the process zones base on specification limits (SLs). Most of the research on control charts assumed certain data…

2112

Abstract

Purpose

In this context, process capability indices (PCI) reveal the process zones base on specification limits (SLs). Most of the research on control charts assumed certain data. However, to measure quality characteristic, practitioners sometimes face with uncertain and linguistic variables. Fuzzy theory is one of the most applicable tools which academia has employed to deal with uncertainty. The paper aims to discuss these issues.

Design/methodology/approach

In this investigation, first, fuzzy and S control chart has been developed and second, the fuzzy formulation of the PCIs such as C pm ,C pmu ,C pml , C pmk , P p , P pl , P pu , P pk are constructed when SLs and measurements are at both triangular fuzzy numbers (TFNs) and trapezoidal fuzzy numbers (TrFNs) stages.

Findings

The results show that using fuzzy make more flexibility and sense on recognition of out-of-control warnings.

Research limitations/implications

For further research, the PCIs for non-normal data can be conducted based on TFN and TrFN.

Practical implications

The application case is related to a piston company in Konya’s industry area.

Originality/value

In the previous researches, for calculating C p , C pk , C pm and C pmk indices, the base approach was calculate standard deviation for a short term variation. For calculating these indices, the variation between subgroups are being ignored. Therefore, P p and P pk indices solved this fault by mentioning long term and short term variations. Therefore these two indices calculate the actual process capability.

Details

International Journal of Quality & Reliability Management, vol. 33 no. 1
Type: Research Article
ISSN: 0265-671X

Keywords

Article
Publication date: 9 March 2012

Osman Taylan and Ibrahim A. Darrab

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…

Abstract

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Research limitations/implications

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

Practical implications

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.

Originality/value

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

Details

Journal of Manufacturing Technology Management, vol. 23 no. 3
Type: Research Article
ISSN: 1741-038X

Keywords

Article
Publication date: 1 July 2000

Chen‐Fang Tsai, Chris Bowerman and John Tait

Much research has recently been conducted into the use of models for the economic design of multiple control charts (EDCC). Control chart models generally assume that most process…

Abstract

Much research has recently been conducted into the use of models for the economic design of multiple control charts (EDCC). Control chart models generally assume that most process variables are constant and only a limited number of the major variables are varied to reach a local optimum. In the economic design of multiple control charts (EDMCC), multiple control charts are used to analyse many manufacturing process variables simultaneously, in order to produce an optimal design for process control. However, the large number of variables often makes it difficult to solve this optimisation problem manually. This research explores the proposition that EDMCC can be optimised by using a novel genetic algorithm which dynamically adjusts the genetic algorithm’s (GA) operator and parameter settings during operation to ensure optimum effectiveness. This method involves refining the chromosome structure and using orthogonal arrays with fuzzy reasoning to reduce the search space.

Details

Integrated Manufacturing Systems, vol. 11 no. 4
Type: Research Article
ISSN: 0957-6061

Keywords

Article
Publication date: 1 February 1995

Reay‐Chen Wang and Chung‐Ho Chen

Considers the problem of determining economic statistical np‐control chart designs under the fuzzy environment of closelysatisfying type I and II errors. Goes on to model the…

747

Abstract

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.

Details

International Journal of Quality & Reliability Management, vol. 12 no. 1
Type: Research Article
ISSN: 0265-671X

Keywords

Article
Publication date: 9 January 2009

James J. Divoky and Mary Anne Rothermel

The purpose of this paper is to explore and analyze the effectiveness of long period supplementary zone rules that can simultaneously increase chart sensitivity to small process…

Abstract

Purpose

The purpose of this paper is to explore and analyze the effectiveness of long period supplementary zone rules that can simultaneously increase chart sensitivity to small process drift and not significantly increase the false alarm rate.

Design/methodology/approach

A stable, on‐target process was simulated and drift induced into the process. The rates of drift varied from 0.03σ to .0003σ per subgroup measurement. A total of 613 different supplementary zone rules were implemented in conjunction with the three‐sigma limiting rule. For each combination, 100,000 observations were simulated and the effect on the false alarm rate and increase in chart sensitivity estimated. An effectiveness measure was developed to relate false alarm rate to chart sensitivity.

Findings

A total of 87 rules were uncovered which effectively detected a wide range of process drifts. When the increase in chart sensitivity is discounted by the false alarm rate, 13 rules increased chart sensitivity by over 10 percent. These rules were based on longer rather than shorter rule length.

Research limitations/implications

The effective rules discovered form a nonlinear pattern in the space the examined rules define. This indicates a direction for future research outside the scope of this study. These rules are also easy to implement in existing Shewhart chart applications where the process drifts at an unknown rate.

Originality/value

While supplementary trend rules have been studied in the past, the extension to zone rules has not been made. This study begins to fill that void and indicates the direction for future efforts in the area.

Details

International Journal of Quality & Reliability Management, vol. 26 no. 1
Type: Research Article
ISSN: 0265-671X

Keywords

Article
Publication date: 11 January 2022

Daniel Ashagrie Tegegne, Daniel Kitaw Azene and Eshetie Berhan Atanaw

This study aims to design a multivariate control chart that improves the applicability of the traditional Hotelling T2 chart. This new type of multivariate control chart displays…

Abstract

Purpose

This study aims to design a multivariate control chart that improves the applicability of the traditional Hotelling T2 chart. This new type of multivariate control chart displays sufficient information about the states and relationships of the variables in the production process. It is used to make better quality control decisions during the production process.

Design/methodology/approach

Multivariate data are collected at an equal time interval and are represented by nodes of the graph. The edges connecting the nodes represent the sequence of operation. Each node is plotted on the control chart based on their Hotelling T2 statistical distance. The changing behavior of each pair of input and output nodes is studied by the neural network. A case study from the cement industry is conducted to validate the control chart.

Findings

The finding of this paper is that the points and lines in the classic Hotelling T2 chart are effectively substituted by nodes and edges of the graph respectively. Nodes and edges have dimension and color and represent several attributes. As a result, this control chart displays much more information than the traditional Hotelling T2 control chart. The pattern of the plot represents whether the process is normal or not. The effect of the sequence of operation is visible in the control chart. The frequency of the happening of nodes is recognized by the size of nodes. The decision to change the product feature is assisted by finding the shortest path between nodes. Moreover, consecutive nodes have different behaviors, and that behavior change is recognized by neural network.

Originality/value

Modifying the classical Hotelling T2 control chart by integrating with the concept of graph theory and neural network is new of its kind.

Details

International Journal of Quality & Reliability Management, vol. 39 no. 7
Type: Research Article
ISSN: 0265-671X

Keywords

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