Statistical monitoring of polytomous logistic profiles in phase II

In certain statistical process control applications, the quality of a process or a product can becharacterized by a function commonly referred to as a profile. some potential applications of profilemonitoring are cases where the quality characteristic of interest can be modelled using dichotomous orpolytomous variables. polytomous variables, especially ordinal variables, have various applications. anordinal (or ordered) variable is a categorical variable, whose values are related in a greater/lesser sense. inthis paper, which is the first investigation on ordinal profiles, we propose four methods for monitoringa profile when the process/service output is an ordinal response variable. ordinal logistic regression(olr) provides the basis for our profile model. these four methods are: multivariate exponentiallyweighted moving average (mewma), 2 statistics, exponentially weighted moving average (ewma)with r statistic, and a combination of the last two statistics that are used to monitor olr profiles in phaseii. performances of these four methods are evaluated using an average run length (arl) criterion. twodifferent case studies involving customer satisfaction in the tourist industry and sensory measurementsof an electronic nose are used to demonstrate application of the proposed methods in practice.