Exponentially Weighted Moving Average (EWMA) control charts are commonly used to detect shifts in the process mean more quickly. The Zero-Inflated Generalized Poisson (ZIGP) distribution is a valuable method of statistical analysis for modeling data with excessive and overdispersion zero excees, particularly when analyzing counting or discrete data. In numerous cases, count data often have excessive number of zero outcomes than are expected in the Poisson. The ZIGP distribution can compensate for this overdispersion by including additional parameters that allow for greater than mean. This aim of this research is to adopt Modified-EWMA to monitoring the ZIGP processes as an alternative to handle overdispersion in zero-inflated count data. Parameter estimation ZIGP distribution using the maximum likelihood and Maximization Expectation algorithm with Newton Raphson approach. To improve the effectiveness of identifying tiny process mean shifts in overdispersion data, a control chart for the ZIGP distribution was devised in this study utilizing the modified EWMA statistic. The Average Run Length (ARL) of a Markov Chain with varied weighting constant values was used to evaluate the performance of the proposed control chart. Using the modified EWMA control chart, a superior detection ability was also compared to the old control chart. This was investigated using two sets of simulation data with different amounts of excess zero. The result shows that the ARL as a performance measure, the suggested ZIGP-Modified EWMA control chart is more suitable than the ZIGP-EWMA control chart.

Overdispersion

Excess zero

ZIGP distribution

Modified EWMA

MLE

ARL

Markov Chain