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In manufacturing industry, quality is very important, because it can determine customers' satisfaction and distinguish the product from others. The effort that can be made by companies to maintain the products quality is by monitoring and controlling them. One of the statistical methods that can be used for monitoring and controlling quality is control charts. Generally, there are two types of control charts, control chart for mean and control chart for variability. Three models of control charts, recently, have been developed, such as Shewhart, Cumulative Sum (CUSUM), and Exponentially Weighted Moving Average (EWMA). This research will be stated Exponentially Weighted Moving Variance (EWMV) and Double Moving Average-S (DMA-S) for monitoring variability based on Neoteric Ranked Set Sampling (NRSS). EWMV and DMA-S control charts can detect small shifts, and NRSS has better performance than Simple Random Sampling (SRS) and Ranked Set Sampling (RSS). Furthermore, the performance of EWMV based on NRSS and DMA-S basd on NRSS control charts will be compared and evaluated by using Average Run Length (ARL) value with Monte Carlo simulation approach to detect any particular shifts. Both of the control chart models will be applied in Combined Cycle Power Plant (CCPP) case. By this evaluation, the result shows that the DMA-S control chart based on NRSS performs better than the EWMV control chart based on NRSS.

Original languageEnglish
Article number012056
JournalJournal of Physics: Conference Series
Issue number1
Publication statusPublished - 19 Jun 2020
Event3rd International Conference on Combinatorics, Graph Theory, and Network Topology, ICCGANT 2019 - East Java, Indonesia
Duration: 26 Oct 201927 Oct 2019


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