In this research, we consider a fuzzy decision rule of out-of-control states as a criterion in statistical process control. It is used for deciding if a process has exceeded its natural specifications limits. We propose a new framework for modeling control charts based on adaptive (non-asymptotic) fuzzy estimators. The adaptive fuzzy estimators introduced are for dealing with short production run processes. These approaches assume that the process parameters, i.e. the process mean and control parameters are fuzzy numbers estimated from statistical data. In particular, we develop: (i) a fuzzy p-chart, where the proportion of non-conforming units is fuzzy number, and (ii) a fuzzy decision rule as a way for flexible inspection that effectively allows the detection of the real out-of-control states. An application example of the proposed chart is illustrated. Moreover, the differences with prior linguistic-based and α-level fuzzy control charts are discussed.