Abstract
The continuous monitoring of the machine is beneficial in improving its process reliability through reflected power function distribution. It is substantial for identifying and removing errors at the early stages of production that ultimately benefit the firms in cost-saving and quality improvement. The current study introduces control charts that help the manufacturing concerns to keep the production process in control. It presents an exponentially weighted moving average and extended exponentially weighted moving average and then compared their performance. The percentiles estimator and the modified maximum likelihood estimator are used to constructing the control charts. The findings suggest that an extended exponentially weighted moving average control chart based on the percentiles estimator performs better than exponentially weighted moving average control charts based on the percentiles estimator and modified maximum likelihood estimator. Further, these results will help the firms in the early detection of errors that enhance the process reliability of the telecommunications and financing industry.
Highlights
The scholars are anxious to know about the error tendency during the entire manufacturing process to validate the pre-production testing results
It was expected during the machine installation process that the pre-testing results remain valid in the practical life, and errors remain in control for instance, laptop manufacturing, which passes through several processes
2.1 Proposed Process Monitoring for Reflected Power Function Distribution Using Zaka et al [1] and assuming x1, x2, x3, . . . , xt being independent identically distributed random variables follows the reflected power function distribution (RPFD) as given below f (x)
Summary
The scholars are anxious to know about the error tendency during the entire manufacturing process to validate the pre-production testing results. We introduce control charts based on the assumptions that if the number of errors follows the RPFD and there exists a non-random variation in the distribution, errors can be determined and handled at the initial stage These control charts make the monitoring process of a machine more reliable and provides persistent results. These real-life applications of the control charts motivate scholars to explore them in non-normal cases where error patterns are unpredictable, in manufacturing concerns It helps the practitioners in the early solution to the errors that further lead to continuing the process without any interval, saving time and cost.
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