Abstract
The Coefficient of Variation (CV) has been applied to different fields to measure the relative variation of a variable. To monitor the quality characteristic of various manufacturing or service processes, the memory-type monitoring scheme has become an important role in practice. In this study, we investigate the performance of the memory-type scheme, a mixed structure of the exponentially weighted moving average (EWMA) and cumulative sum (CUSUM), for monitoring the CV, denoted as the MEC-γ2 scheme. Moreover, another MEC-γ2 scheme with a new resetting technology, denoted as MREC-γ2 scheme, is constructed to improve the monitoring scheme’s sensitivity to large shifts in a process. The fast initial response (FIR) feature is incorporated into the proposed schemes to enhance the efficiency. The simulation results, based on the run length (RL) properties, show the superiority of the proposed monitoring schemes against some competitors, including the EWMA-γ2 and CUSUM-γ2. Finally, an illustrative example is presented using a real dataset from the sintering process.
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