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In real industries, multivariate process monitoring is crucial as there are many instances that involve at least two quality variables to be monitored simultaneously. The short runs process is commonly seen in production after the industry moved toward flexible manufacturing. Monitoring the coefficient of variation (CV) is useful in a wide variety of scientific areas. In a view of the importance in monitoring the CV and the fact that most real-life data in process monitoring are multivariate in nature, this paper proposes to monitor the multivariate CV in short runs process by means of run rules (RR) control charts. A Markov chain model is established for designing the proposed charts. The statistical performances of the RR multivariate CV (MCV) and Shewhart MCV (SH MCV) charts are compared in terms of the truncated average run length and the expected truncated average run length. The results show that the proposed charts surpass the SH MCV chart for detecting small and moderate multivariate CV shifts. The implementation of the RR MCV chart in the short runs process is illustrated with an example using a real dataset.

The efficiency of run rules schemes for the multivariate coefficient of variation in short runs process