Abstract
Control chart is one of the most important tools in statistical process control (SPC) that leads to improve quality processes and ensure the required quality levels. The usual assumption for designing a control chart is that the data or measurement should have a normal distribution. However, this assumption may not be true for some processes, there are some factors that cause an uncertainty data such as human, measurement device or environmental conditions. Therefore, the purposes of this research are to study, develop and compare the efficiency of fuzzy weighted variance (FWV) control charts which the data has non-normal distribution as Weibull, gamma and Chi-squared. FWV control charts use fuzzy set theory to help in solving the uncertainty data. The random variable for the experiment will be transformed to fuzzy control chart by using triangular membership function. Finally, the performance and comparative efficiency of the FWV control charts are measured in term of average run length (ARL) by Monte Carlo simulation technique.
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