RETRACTED ARTICLE: Monitoring of production of blood components by attribute control chart under indeterminacy

Khan, Nasrullah; Aslam, Muhammad; Jeyadurga, P.; Balamurali, S.

doi:10.1038/s41598-020-79851-5

Cited by 19 publications

(9 citation statements)

References 22 publications

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“…In consequence of the existing Anderson-Darling test that cannot be applied for testing the assumption of the Weibull distribution, Aslam [51] presented the Anderson-Darling test under neutrosophic statistics. Khan et al [52] presented new attribute control charts for monitoring the blood components under the neutrosophic statistics. e applications of these control charts demonstrate that the proposed control charts are quite effective, adequate, flexible, and informative for monitoring the blood components under uncertain environment.…”

Section: Discussionmentioning

confidence: 99%

Economic Design of EWMA Control Charts with Variable Sampling Intervals for Monitoring the Mean and Standard Deviation under Preventive Maintenance and Taguchi’s Loss Functions

2021

Mathematical Problems in Engineering

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The control chart and the maintenance management need to control process quality and reduce out-of-control cost. They are two key tools in the production process; however, they have usually been analyzed separately in the literature. Moreover, the existing studies integrating these two concepts suffer from three significant drawbacks as follows: (1) using control charts with fixed parameters to monitor the process, so that the small and middle shifts are detected slowly; (2) monitoring the mean and standard deviation separately, whereas, in real condition, the mean and standard deviation should be monitored simultaneously; (3) quality loss function is not usually used to design economic model, which leads to a large social quality loss in the monitoring process of control chart. To eliminate these weaknesses, the economic design of the exponential weighted moving average (EWMA) control chart with variable sampling intervals (VSI) for monitoring the mean and standard deviation under preventive maintenance and Taguchi’s loss functions is proposed. The optimal values of the parameters are determined to minimize the loss-per-item in an average cycle process. In addition, a genetic algorithm is used in a numerical example to search for the optimal values of the parameters. According to the sensitivity analysis, the effect of the model parameters on the solution of the economic model is obtained. Finally, the comparison study shows that the VSI EWMA control charts designed by the joint economic model are expected to reduce loss.

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Section: Discussionmentioning

confidence: 99%

Economic Design of EWMA Control Charts with Variable Sampling Intervals for Monitoring the Mean and Standard Deviation under Preventive Maintenance and Taguchi’s Loss Functions

2021

Mathematical Problems in Engineering

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“…Additionally, sample scheme other than simple random sampling also disturbs the performance of proposed control charts. Furthermore, it can be extended for nonparametric environment, neutrosophic statistics (see Aslam et al, 42,43 Aslam, 44 and Khan et al 45 ) and for other sampling schemes to cover the other aspects of real-life.…”

Section: Concluding Remarks and Limitationsmentioning

confidence: 99%

Efficient adaptive CUSUM control charts based on generalized likelihood ratio test to monitor process dispersion shift

Zaman

2021

Quality & Reliability Eng

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In practice, a shift in the process parameters (location and/or dispersion) is unknown in prior and cannot be diagnosed precisely with the classical cumulative sum (CUSUM) control chart. To overcome this issue, this study proposed two adaptive CUSUM (ACUSUM) control charts. The proposed control charts utilized linear weighted function that is inspired by generalized likelihood ration test (GLRT) to monitor small and certain range of shift in the process dispersion. In more details, the proposed control charts methodologies are based on GLRT, exponentially weight moving average statistic, and score functions. To obtain the run length of the proposed control charts for performance assessment, algorithms are designed in MATLAB based on Monte Carlo simulation technique. Further, average run length (ARL) is used as a performance measure tool to compare the control charts performance for a single shift. For certain range of shift, extra quadratic loss function, relative ARL, and performance comparison index performance measures based on ARL are calculated. Some existing control charts are used for comparison purpose. The proposed control charts show outstanding capability to detect out‐of‐control signal against these control charts. Moreover, real‐life data of inside diameter of cylinder bore in an engine block are used to reveal the practicality and worth of the proposed control charts relative to other control charts.

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“…The main aim of this approach is to improve the quality of the products by reducing unfavorable variations. By controlling the manufacturing process of a product, SPC detects internal system inconsistencies and provides solutions to improve the production process with the help of some major tools involving Pareto chart, control chart, cause-and-effect diagram, scatter diagram, check sheet, stratification and histogram 1 , 2 .…”

Section: Introductionmentioning

confidence: 99%

Monitoring non-parametric profiles using adaptive EWMA control chart

Yeganeh

Shongwe

2022

Sci Rep

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To monitor the quality of a process in statistical process control (SPC), considering a functional relationship between a dependent variable and one or more independent variables (which is denoted as profile monitoring) is becoming an increasingly common approach. Most of the studies in the SPC literature considered parametric approaches in which the functional relationship has the same form in the in-control (IC) and out-of-control (OC) situations. Non-parametric profiles, which have a different functional relationship in the OC conditions are very common. This paper designs a novel control chart to monitor not only the regression parameters but also the variation of the profiles in Phase II applications using an adaptive approach. Adaptive control charts adjust the final statistic with regard to information of the previous samples. The proposed method considers the relative distance of the chart statistic to the control limits as a tendency index and provides some outcomes about the process condition. The results of Monte Carlo simulations show the superiority of the proposed monitoring scheme in comparison with the common non-parametric control charts.

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RETRACTED ARTICLE: Monitoring of production of blood components by attribute control chart under indeterminacy

Cited by 19 publications

References 22 publications

Economic Design of EWMA Control Charts with Variable Sampling Intervals for Monitoring the Mean and Standard Deviation under Preventive Maintenance and Taguchi’s Loss Functions

Economic Design of EWMA Control Charts with Variable Sampling Intervals for Monitoring the Mean and Standard Deviation under Preventive Maintenance and Taguchi’s Loss Functions

Efficient adaptive CUSUM control charts based on generalized likelihood ratio test to monitor process dispersion shift

Monitoring non-parametric profiles using adaptive EWMA control chart

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