monitoring a two-stage process using adaptive control charts with a markov - monte carlo chain approach

One of the effective approaches to quality improvement is the application of statistical science within the framework of total quality management (tqm). statistical process control (spc), as a key component of tqm, utilizes tools such as control sheets, histograms, pareto charts, cause-and-effect diagrams, defect concentration charts, correlation diagrams, and control charts to detect and prevent defective products. this study focuses on control charts as instruments for identifying variations and out-of-control conditions in process means. in traditional methods, there is usually a delay between the occurrence of a process change and its detection on shewhart control charts. this research aims to minimize such delay by proposing the use of adaptive control charts based on markov chain models, which enhance the capability of rapid detection of assignable causes. to evaluate the proposed approach, one of the machines in a tea bag production company-characterized by a two-stage production process—was selected for case analysis. sampling was conducted in two modes: once with fixed sample sizes and intervals, and again using adaptive sampling with variable sizes and intervals, to compare the efficiency of the proposed method.