Classical Control Charts Research Articles

Functional EWMA control chart for phase II profile monitoring

Due to rapid developments in technology, profile monitoring is becoming challenging, as the profile data are a series of observations on a specific quality characteristic that are taken across some continuum. In this paper, a functional exponentially weighted moving average (FEWMA) control chart is developed for phase-II monitoring of nonlinear profiles based on the functional data approach. In particular, the profiles are estimated using cubic B-splines to solve the problem of high dimensionality that hinders the implementation of classical multivariate control charts. An extensive simulation study has been conducted to validate the effectiveness of the proposed control scheme for detecting changes in the functional mean of the process to its magnitude and assuming scenarios of independence and dependence between curves. The properties of the run-length distribution of the proposed scheme are discussed. The FEWMA control chart is also applied to the real-time data of the vertical density profile of particleboard manufacturing.

Sensitivity and reliability comparisons of EWMA mean control chart based on robust scale estimators under non‐normal process: COVID data application

AbstractRobust control charts are getting vital importance in statistical process control theory as they are insensitive to the departure from normality. Therefore, the main objective of current work is to determine the effects of non‐normal process on the Exponentially Weighted Moving Average (EWMA) control chart. To achieve this goal, the sensitivity and reliability comparisons are made under the non‐normal process by comparing five robust M‐scale estimators, suggested in literature to modify the EWMA control limits for monitoring process mean and on the basis of percentile bootstrap estimator. The paper addresses the run length (RL) distribution of a robust EWMA control chart under the non‐normal process for which the exponential distribution is used as non‐normal process. The standard deviation of RL, out‐of‐control average run length (ARL), and shift detection probabilities are examined to assess the sensitivity and reliability of robust EWMA control charts for mean of monitoring process. The results of this research indicate that the classical EWMA control chart's performance is substantially impacted by the non‐normal distribution and the proposed EWMA control charts show higher sensitivity than classical one in terms of having smaller values of out‐of‐control ARLs. A real‐life example from the medical sciences field is provided the practical usage of the proposed control charts. The simulation analysis and practical example have shown that the suggested control charts are effective in quickly monitoring the out‐of‐control process.

Optimal triple sampling [formula omitted] charts for autocorrelated processes

Autocorrelation is a phenomenon in which the characteristics of interest being monitored are dependent. In the presence of autocorrelation, the detection ability of classical control charts deteriorates significantly, and this poses great concerns to practitioners as some processes in real-life settings produce autocorrelated observations. This paper deals with the problem of autocorrelation in Shewhart charts by incorporating the AR(1) and MA(1) models into the classical TS X¯ chart, which is an extension of the Shewhart chart. The performance of the introduced TS X¯ charts for AR(1) and MA(1) is improved by optimizing the parameters of the charts when the in-control average run length (ARL) of the charts is 370. The TS X¯chart for AR(1) is found to outperform the DS X¯chart for AR(1) in the presence of autocorrelation. The performance of the proposed charts is assessed using ARL, standard deviation of the run length (SDRL), average number of observations to signal (ANOS), and standard deviation of the number of observations to signal (SDNOS) criteria. Based on these criteria, the TS X¯chart for MA(1) is found to surpass the TS X¯chart for AR(1) as the autocorrelation level increases.

Generalized multivariate EWMA control chart supplemented with bivariate ranked set techniques

ABSTRACT Existing univariate exponential weighted moving average (EWMA) control charts with ranked set schemes are used for monitoring related characteristics independently. Also, existing multivariate EWMA control charts are generally applicable for simple random sampling (SRS) based processes. In this work, a generalized multivariate EWMA control chart is proposed with bivariate ranked set techniques (BVRST) for simultaneously monitoring. Furthermore, an extensive comparative analysis between proposed generalized multivariate EWMA and classical control chart is conducted. In this regard, we have used various performance measures, which cover average run length, median of run length, relative average run length, performance comparison index, and extra quadratic loss. The performance measures are obtained using the Monte Carlo simulation (MCS) approach. Results revealed that the proposed generalized multivariate EWMA control chart has an outstanding detection ability relative to the existing multivariate control charts. Additionally, existing multivariate control charts are found special cases of the proposed control chart. A real-life example from the agriculture field is included in which chemical properties (residual sodium and calcium-magnesium contents) of irrigation water are monitored through the control charts. The results revealed that the proposed control chart is proficient in diagnosing small variations in the chemical characteristics relative to the existing control charts.

Control charts for high-dimensional time series with estimated in-control parameters

In this article, we study the effect of misspecification caused by fitting the target process in the Phase I analysis of the monitoring procedure on the behavior of several types of multivariate exponentially weighted moving average (MEWMA) control charts in the high-dimensional setting. In particular, the classical MEWMA control charts, whose control statistics are based on the exact and asymptotic Mahalanobis distance, are considered together with the novel approaches where the Euclidean distance and the diagonalized Euclidean distance are employed in the construction of control statistics. The high-dimensional distributions of the control statistics are deduced at each time. These results are later used to assess the performance of the considered control charts under misspecification. Both theoretical and empirical findings lead to the conclusion that the control charts based on the Euclidean distance and the diagonalized Euclidean distance are robust to misspecification for moderate dimensions of the data-generating model, whereas they tend to overestimate the in-control average run lengths (ARLs) in the case of larger dimensions. On the other hand, the control schemes based on the Mahalanobis distance are considerably affected by the estimation of the parameters of the target process, and their application results in drastically smaller values of the ARLs, especially when the dimension of the data-generating model is large.

Integration of hybrid score functions to design new adaptive EWMA control chart to monitor the process mean shift

Small-to-moderate shifts in the process parameters can be detected using the traditional Exponentially Weighted Moving Average (EWMA) control chart. The classical EWMA control chart's parameter helps only to identify the specific size of the shift. However, various shifts occur in real-world processes, so the conventional EWMA control chart performs inefficiently for off-target shifts. This problem has been confronted by applying an adaptive EWMA (AEWMA) control chart, an advanced version of the classical EWMA control chart. The score functions are utilized to design the AEWMA control chart. Generally, the Huber and Bi-square (score) functions have unique characteristics, such as monotone and redescended, respectively.There has not been any investigation into the simultaneous and/or joint deployment of score functions. So, by taking this point as motivation, this study aims to propose a new score function by blending the Huber and Bi-square characteristics into one hybrid formulation and then utilize it to design a new AEWMA control chart to monitor different sizes of the shift in the process location parameter. To evaluate the proposed control chart performance against other control charts, Monte Carlo simulation is applied to obtain the run length properties under zero-state and steady-state environments. Performance measures, such as average run length, standard deviation of run length, and median of run length, are considered to evaluate the performance at a single shift. In contrast, extra quadratic loss, relative average run length, and performance comparison index are utilized to evaluate effectiveness for a specific range of shifts. The comparison based on performance measures and visual presentation reveals the superiority of the proposed control chart. Besides, the proposed control chart is employed in the real-world chemical engineering process industry problem, demonstrating excellent detection ability of anomalies in air pollutant data. The comparison based on performance measures and visual presentation reveals the good performance of the proposed control chart.

An adaptive EWMA control chart based on Hampel function to monitor the process location parameter

AbstractThe classical memory Exponential Weighted Moving Average (EWMA) and Cumulative Sum (CUSUM) control charts (CCs) are important to detect small‐to‐moderate sizes of shift in the process location and/or dispersion parameters. To make the memory control chart (CC) more efficient for broad range of shift, the score functions in particular Huber and Bi‐square functions are incorporated in the classical memory CCs. These score functions help to formulate the parameters of memory CCs and are called Adaptive EWMA (AEWMA) and Adaptive CUSUM (ACUSUM). The AEWMA and ACUSUM are capable to handle various sizes (i.e., small, moderate, and large) of shift. The aim of this study is to utilize Hampel function as the alternative of the mentioned score functions, and propose a new AEWMA CC, symbolized as AEWMAHample. To develop the proposed AEWMAHampleCC, the standard EWMA statistic and Hampel function are incorporated in the classical EWMA CC structure. The proposed AEWMAHampleCC is also efficient to handle certain sizes of shift in the process location parameter. Performance evaluation procedures, like average run length is considered for a particular shift. Similarly, the performance comparison index, relative average run length, and extra quadratic loss function are estimated over a range of shift. All these performance measures are calculated from run length (RL) characteristic of the proposed control chart. An algorithm in MATLAB is developed based on Monte Carlo simulation procedure to generate the RLs. Some existing control charts including EWMA, CUSUM, MEC, MCE, , , IACCUSUM, , and are taken for comparison reason. For specific sizes of shift, the proposed AEWMAHampleCC reveals the superiority. Besides, to address the practical significance for practitioners and quality engineers, the real‐life and simulated data examples are considered.

Improved adaptive EWMA control chart for process location with applications in groundwater physicochemical parameters and glass manufacturing industry.

The adaptive exponentially weighted moving average (AEWMA) control charts are the advanced form of classical memory control charts used for efficiently monitoring small-to-large shifts in the process parameters (location and/or dispersion). These AEWMA control charts estimate the unknown shifts using exponentially weighted moving average (EWMA) or cumulative sum (CUSUM) control charts statistics. The hybrid EWMA (HEWMA) control chart is preferred over classical memory control charts to detect early shifts in process parameters. So, this study presents a new auxiliary information-based (AIB) AEWMA (IAEWMAAIB) control chart for process location that estimates the unknown location shift using HEWMA statistic. The objective is to develop an unbiased location shift estimator using HEWMA statistic and then adaptively update the smoothing constant. The shift estimation using HEWMA statistic instead of EWMA or CUSUM statistics boosts the performance of the proposed IAEWMAAIB control chart. The Monte Carlo simulation technique is used to get the numerical results. Famous performance evaluation measures like average run length, extra quadratic loss, relative average run length, and performance comparison index are used to evaluate the performance of the proposed chart with existing counterparts. The comparison reveals the superiority of the proposed control chart. Finally, two real-life applications from the glass manufacturing industry and physicochemical parameters of groundwater are considered to show the proposed control chart’s implementation procedure and dominance.

An Adaptive EWMA Control Chart Based on Principal Component Method to Monitor Process Mean Vector

The special causes of variations, which is also known as a shift, can occur in a single or more than one related process characteristics. Statistical process control tools such as control charts are useful to monitor shifts in the process parameters (location and/or dispersion). In real-life situation, the shift is emerging in different sizes, and it is hard to identify it with classical control charts. Moreover, more than one process of characteristics required special attention because they must monitor jointly due to the association among them. This study offers two adaptive control charts to monitor the different sizes of a shift in the process mean vector. The novelty behind this study is to use dimensionally reduction techniques such as principal component analysis (PCA) and an adaptive method such as Huber and Bi-square functions. In brief, the multivariate cumulative sum control chart based on PCA is designed, and its plotting statistic is utilized as an input in the classical exponentially weighted moving average (EWMA) control chart. The run length (RL) properties of the proposed and other control charts are obtained by designing algorithms in MATLAB through a Monte Carlo simulation. For a single shift, the performance of the control charts is assessed through an average of RL, standard deviation of RL, and standard error of RL. Likewise, overall performance measures such as extra quadratic loss, relative ARL, and the performance comparison index are also used. The comparison reveals the superiority over other control charts. Furthermore, to emphasize the application process and benefits of the proposed control charts, a real-life example of the wind turbine process is included.

Adaptive CUSUM control charts for efficient monitoring of process dispersion

AbstractAn adaptive cumulative sum (ACUSUM) control chart is an advance form of the classical CUSUM control chart to identify different sizes of shift in the process parameters (location and/or dispersion). In the continuation of the ACUSUM control chart, this study also proposed ACUSUM control charts to enhance the performance of the process dispersion for a broad range of shift. The proposed ACUSUM control charts methodologies are based on the dispersion CUSUM statistic, score (Huber and Bi‐square) functions, generalized likelihood ratio test, and nonlinear optimization technique. The use of dispersion CUSUM statistic helps to distinguish a specific shift as the classical CUSUM statistic does, the score functions, the nonlinear optimization technique and generalized likelihood ratio test enhance the ability of the proposed ACUSUM control charts to distinguish a shift of different size. For the assessment of control charts, run length (RL) measure is used, and it is generated by developing an algorithm in MATLAB through Monte Carlo simulation method. Further, average of RL is utilized to carry out comparative analysis of control charts for a single shift, while for a certain range of shift (comprehensive analysis), extra quadratic loss (EQL), relative average run length (ARL), and performance comparison index (PCI) measures are used. Findings based on numerical results and visual presentations reveal the superiority of the proposed control charts against some existing control charts. Moreover, for real‐world point of assessment, the proposed control chart is implemented with numerical data to show the worth over other control charts.

Penerapan Peta Kendali Neutrosophic Exponentially Weighted Moving Average (NEWMA) X dalam Monitoring Rata-Rata Proses Ketebalan Kaca

In the industrial sector, the measurement results of a quality characteristic often involve an uncertainty interval (interval indeterminacy). This causes the classical control chart to be less suitable for monitoring quality. Currently, a control chart with a neutrosophic approach has been developed. The neutrosophic control chart was developed based on the concept of neutrosophic numbers with control charts. One of the control charts that have been developed to monitor the mean process is the Neutrosophic Exponentially Weighted Moving Average (NEWMA) X control chart. This control chart is a combination of neutrosophic with classical EWMA control chart. The neutrosophic control chart consists of two control charts, namely lower and upper, each of which consists of upper and lower control limits. Therefore, NEWMA X is more sensitive to detect out-of-control observations. In this research, the NEWMA X control chart will be used to monitor the average process of the thickness of the panasap dark grey 5mm glass produced by a glass industry. Through the analysis in this research, it was found that by using weighting λN [0, 10; 0, 10] and constant value kN [2, 565; 2, 675], the average process of the thickness of panasap dark grey 5mm glass has not beet controlled statistically because 21 observations were identified that were outside the control limits (out of control). When compared with the classical EWMA control chart with the same weighting λ, 17 observations were detected out of control. This proves that the NEWMA X control chart is more sensitive in detecting observations that are out of control because the determination of the in-control state is based on two values, lower and upper, both at the lower and upper control limits.

Construction X-Bar Chart for Quality Control Using of Robust (MAD) Estimator in Under Consideration Is Outlier with An Application

The presence of outliers in a sample significantly affects the estimated values, which is why the highly efficient Median Absolute Deviation (MAD) method has been used due to of its great robust against the existence of outliers values in the data. In this article, we construct a mean quality control charts using the (MAD) robust. A comparison between the mean control chart based on robust and the mean control chart based on standard deviation to reach a better method, this is through a practical application to the real data (the pressure force on the block). The findings revealed that mean control chart based on (MAD) was more effective than the classical mean control chart based on standard deviation in identifying anomalies from the average production process.

Monitoring of location and dispersion parameters of production processes using hybrid control charts

In practical terms, the measurement data from production processes are usually contaminated which may translate into failure to meet the assumed normality. In consequence, using classical Shewhart control charts to monitor the stability of production processes leads to the occurrence of numerous false signals. The purpose of this article is to propose hybrid control charts and to investigate their performance in monitoring the parameters of location and variability of production processes for which there occurs contamination of measurement data appearing at the stage of monitoring production cycles. To construct the control charts proposed by the authors, we should use extended control limits of classical Shewhart charts for means and ranges (Phase I) and robust estimators as test statistics to control the production cycle (Phase II). The article demonstrates the conducted simulation testing of classical and robust estimators for location and dispersion in terms of their effectiveness, as well as determining the relationship between them. These analyses have contributed to the design of hybrid control charts. Comparative simulation tests of classic and hybrid control charts performance have confirmed the effectiveness of the suggested measures. It follows that the proposed control charts can replace the classic counterparts, because they do not respond to an acceptable level of measurement data contamination, thanks to which it is possible to avoid taking unnecessary corrective actions in the production process. The theoretical portion is closed by a case study based on actual data which aims to illustrate the proposed approach.

Run length distribution for a modified EWMA scheme fitted with a stationary AR(p) model

A modified exponentially weighted moving average (EWMA) scheme is a quality control chart which can quickly detect small shifts in autocorrelated data. In this article, an explicit formula is presented to calculate the average run length (ARL) for a modified EWMA chart with an autoregressive AR(p) model involving exponential white noise. Its performance is compared with a numerical integral equation method via a simulation study to determine whether the explicit formula can help decrease the processing time. Moreover, this scheme is tested against a classical EWMA control chart using the ARL and RMI results. Finally, the explicit formula for ARL is applied to a real numerical example from the health field to demonstrate the efficacy of our method.

Full-scale production of high-quality wood pellets assisted by multivariate statistical process control

In order for solid biofuels to be marketable and accessible to stakeholders (e.g., manufacturers, transhippers and customers), they must strictly meet the rigors of international standards and regulatory agencies regarding quality. On this basis, this study assesses the production of high-performance fuel-flexible woody pellets for heating and power, with the assistance of multivariate statistical process control (MSPC). The operational scenarios of industrially pelletizing pinewood sawdust are POSI (150 MPa and no pre-heating) and POSII (250 MPa and no pre-heating) as references, and POSIII (150 MPa and pre-heating) and POSIV (250 MPa and pre-heating) as alternatives to regularize the workflow on the machine. The stream-monitoring techniques of MSPC are the exponential moving average (EMA) and Hotelling's T2. The EMA-Hotelling system accurately tracks non-random errors and predicts the operationally finest scenario for developing high-quality pellets. Pre-heating the compressing channel of the machine regularizes the feeding and thus produces type I pellets (class A1/A2, European standard) with excellent bulk density (1227.55 kg m−3), durability (98.10%) and hydrophobicity (96.65%), without points outside the specific critical ranges. Clear evidence of improved pellet quality with the assistance of MSPC is found. The high-fidelity concept of MSPC captures the advantages of EMA and Hotelling's T2 into an immersive single framework; hence, it can compensate for the potential bias and misinformation-to-information overlapping of outcomes from monitoring latently multivariable flow on classical quality control charts. The major findings and innovations of this study can assist with rolling-out fine-scale pelletization and thus fulfill the increasing global demand for high-quality biofuels.

Progressive mean control chart is not a special case of an exponentially weighted moving average control chart

AbstractThe progressive mean (PM) statistic is based on a simple idea of accumulating information of each subgroup by calculating the average progressively. Its weighting structure is based on a subgroup number that changes arithmetically, which makes the PM chart unique and efficient compared with the existing classical memory control charts. In a recent article (see reference 1), it was claimed that the PM chart is a special case of the exponentially weighted moving average (EMWA) chart. In this article, it is shown that even though the PM statistic can be written in the form of an EWMA statistic, the variance of the PM statistic is different from that of the EWMA statistic. Consequently, the limits of the PM chart are different from that of the EWMA chart. Therefore, it is found that the PM chart is not a special case of the EWMA chart; hence, the claim in reference 1 is incorrect. Furthermore, it is pointed out in this paper that no adaptive property in the weighting parameter of the PM statistic exists, further contradicting the claim in reference 1.

Mixed memory control chart based on auxiliary information for simultaneously monitoring of process parameters: An application in glass field

Cumulative sum (CUSUM) and exponentially weighted moving (EWMA) control charts, known as memory control charts, are famous to monitor a small-to-moderate shift in the process parameters (location and/or dispersion). The EWMA control chart based on auxiliary information denoted as EWMAAIB(1) and EWMAAIB(2) are advanced forms of the classical EWMA control chart to monitor the process location and dispersion, respectively. Likewise, the combined mixed EWMA-CUSUM (CMEC) control chart is used to simultaneously monitor the process parameters. This study presents the CMEC control chart based on auxiliary information, symbolized as CMECAIB control chart for simultaneous monitoring of the process parameters. The proposed CMECAIB control chart used EWMAAIB(1) and EWMAAIB(2) control charts plotting statistics as inputs in the classical CUSUM control charts. The Monte Carlo simulation is used as a computational technique for numerical results. The proposed CMECAIB control chart based on average run length performance measure is evaluated against other control charts such as CMEC, combined double mixed EWMA-CUSUM, combined CUSUM, maximum double EWMA, maximum EWMA based on auxiliary information (MaxEWMAAIB) control and maximum Hybrid EWMA based on auxiliary information (MaxHEWMAAIB) control charts. The comparison revealed the superiority of the proposed CMECAIB control chart. Besides, the CMECAIB control chart performs better as the correlation coefficient increases. Likewise, the proposed CMECAIB control chart has a better diagnostic ability for possible directions of changes against other control charts. It is vital to mention that some existing control charts such as classical CUSUM, CMEC, and maximum CUSUM are special cases of proposed CMECAIB control chart with the specific parameter’s values. Finally, to demonstrate the vitality of the proposed study from a practical point of view, a real-life application in the glass bottle manufacturing industry is also provided for users and practitioners.

Charts for Exponentially Weighted Moving Average Classical and Robust: A Comparative Study

This research discusses the comparison between two control charts classical & robust for both types (EWMA-SMQ) and (EWMA-SM) for the exponentially weighted moving average, which is showed that the robust (EWMA-SMQ) chart for the process is a superior alternative to the (EWMA-SM) chart when the outliers are present in the data. Generally, the (EWMA-SMQ) chart enables easier detection of outliers in the subgroups and is also more sensitive to other forms of out-of-control situations when outliers are present. Hence, the (EWMA-SMQ) becomes a preferred alternative to be taken and applied in quality control. The data used in this research represent the weights of the Al-Sabah Iraqi newspaper published by the Iraqi media networks. Twenty-five samples were taken and each sample consisted of five observations. These samples were taken at different production times, as the average weight of the newspaper was approximately (150) g. Through the application of the classical control chart (EWMA), exit four points for the upper and lower control limits, and the application of (EWMA-SM), (4) points are out of the upper and lower limits of the control; the control limits have been extended from the top and bottom sides of the (EWMA-SM) chart, making it less sensitive to the diagnosis of the shift in the mean process. The (EWMA-SMQ) chart detected extra points out of control. There were (7) points out of the upper and lower limits for the control, tight control limits of the (EWMA-SMQ) chart from the top and bottom sides. Due to this narrowness, three additional points were detected. Therefore (EWMA-SMQ) chart is more robust than the (EWMA-SM) control chart. This research aims to make a comparative study between the classical and robust for both types (EWMA-SM) & (EWMA-SMQ) for the exponentially weighted moving average control chart. The most important result of the research that the (EWMA-SMQ) chart is more suitable than the (EWMA-SM) & classical (EWMA) when outliers are present in the data. This gives importance to it to control the quality of the product. By this robust chart, the outliers must be detected, investigated and the special cause removed if possible. The presence of outliers will reduce the sensitivity of a control chart. The (EWMA-SMQ) control chart is more sensitive to out-of-control conditions when outliers are present in data. The limits computed from the estimate of the interquartile ranges for the (EWMA-SMQ) chart are less influenced by outliers than the (EWMA-SM) chart where the limits are computed based on the sample ranges. Thus the (EWMA-SMQ) chart is more robust than the (EWMA-SM) chart.

Süreç Ortalaması için Tek Taraflı Medyan Kalite Kontrol Kartı

Bir fabrikanın üretim sürecinin kalitesini kontrol etmek amacıyla kullandığı en hızlı, ve güvenilir yöntemlerden biri klasik Shewhart kontrol kartlarıdır. Ancak küçük örneklem durumunda, normal dağılım varsayımının şüpheli olduğu durumlarda ve çarpık ya da ağır kuyruklu dağılım söz konusu olduğunda Shewhart kontrol kartı ile süreci takip etmek riskli olacaktır. Böyle durumlarda parametrik olmayan kontrol kartları tercih edilir. Bu çalışmada tek taraflı parametrik olmayan kontrol kartları ayrıntılı olarak incelenmiştir. Ön istatistiğe dayalı tek taraflı medyan kontrol kartı için yanlış alarm oranı, çalışma uzunluğu ve ortalama çalışma uzunluğunun değerleri hesaplanmıştır. Medyan kontrol kartlarının tek dezavantajı hesaplamalarının güçlüğüdür. Bu güçlüğü gidermek için farklı örneklem hacimleri ve farklı olasılıklar için yapılan hesaplamalar orijinal tablolarda sunulmuştur. Ülkemizde üretim yapan ve istatistiksel kalite kontrol sürecine önem veren sanayi kuruluşları için hazırlanan bu tablolar büyük kolaylıklar sağlayacaktır. Çalışmanın sonunda bir veri seti üzerinde uygulama yapılarak ön istatistiğine dayalı medyan kontrol kartının kullanımı gösterilmiştir.

Design of variable control charts based on type-2 fuzzy sets with a real case study

Control charts (CCs) are very effective tools to follow process variation and to improve process quality. It is completely critical to increase the sensitiveness and flexibility of CCs to gain a deeper perspective for process control. When constructing conventional CCs, errors may occur due to operator or measuring instruments in performing the measurement. By the way, some data related to CCs can include “uncertainty” or “vagueness” related to process and human evaluations or inspector judgments. We know that classical CCs cannot be able to manage this process. The fuzzy set theory (FST) is one of the most important tools to solve these problems, and the CCs designed based on FST are more usable and preferable for monitoring the process. By the way, one of the extensions of FST named type-2 fuzzy sets that have fuzzy membership degrees is more capable of modeling uncertainty. Therefore, they can be successfully used to the design of the control process in a more flexible and sensitive way. In this paper, the type-2 fuzzy sets have been used to the design of variable control charts to increase the precision and flexibility of them. For this aim, $$\bar{x} - R$$ , $$\bar{x} - S$$ and $$I - MR$$ control charts have been re-designed by using type-2 fuzzy sets. Additionally, these charts have been applied on a real case application from electronic industry, and the obtained results indicate that CCs based on type-2 fuzzy sets can evaluate the process in a more sensitive and flexible way.