Good control charts for high quality processes are often based on the number of successes between failures. Geometric charts are simplest in this respect, but slow in recognizing moderately increased failure rates p. Improvement can be achieved by waiting until r > 1 failures have occurred, i.e. by using negative binomial charts. In this paper we analyze such charts in some detail. On the basis...

This working paper series is intended to facilitate discussion and encourage the exchange of ideas. Inclusion here does not preclude publication elsewhere. It is the original work of the author(s) and subject to copyright regulations. ABSTRACT We believe that conflicting decision signals resulting from examining Shewhart Mean and Variability Control Charts produce undesirable consequences. Ther...

The purpose of this article is to evaluate the application of forecasting models along with the use of residual control charts to assess production processes whose samples have autocorrelation characteristics. The main objective is to determine the efficiency of control charts for individual observations (CCIO) and exponentially weighted moving average (EWMA) charts when they are applied to res...

Control charts based on time between events (TBE) data have been shown to be useful for monitoring production or failure processes where defect or failure occurrence can be recorded over time. One type of TBE chart is set up with probability control limits, e.g. the Cumulative Quantity Control (CQC) chart and its extension CQC-r chart, which monitors the quantity inspected until the occurrence ...

The fuzzy regression control chart is a functional technique to evaluate the process in which the average has a trend and the data represents a linguistic or approximate value. In this study, the theoretical structure of the “α-level fuzzy midrange for α-cut fuzzy X ~ -regression control chart” is proposed for triangular (TFN) and trapezoidal (TrFN) membership functions. In addition, the real w...