Process control for categorical (ordinal) data

Abstract

Quality improvement is playing the key role in the success of a business. Reduction of variability is the main step for improvement of quality. Control charts are developed for the purpose of monitoring the quality characteristics with the aim of reducing variability. In many industries instead of continuous variable categorical (ordinal) data are used to measure the quality characteristics of interest. Hence developing control charts techniques for monitoring ordinal data has become a recent research focus. Quality control practitioners often face a problem to select the appropriate technique for monitoring ordinal data in the practical field since there are quite a few techniques available in the literature for this purpose. In this paper we have studied the various techniques for monitoring ordinal data and compared their performance to detect the shift in location parameter. Data were simulated from Normal distribution and average run length (ARL) were computed for different values of shift in mean (both in positive and negative direction) using different methodologies under study. The best technique to detect the shift was identified with respect to ARL.