Design of Nonparametric Extended Exponentially Weighted Moving Average – Sign Control Chart

Abstract

The present research introduces the EEWMA-Sign chart, which incorporates the extended exponentially weighted moving average control chart with the sign control charts to detect small changes in procedures. This is a nonparametric control chart that can overcome the constraints imposed by normal assumptions. The average run lengths serve as supporting examinations for comparing the effectiveness of a monitoring scheme to the EEWMA and EWMA control charts via Monte Carlo Simulation. Besides a specific range of shift sizes, the expected ARL (EARL) remains an instrument to assess the efficiency of control charts. The overall result demonstrates that the proposed chart is the most suitable control chart for detecting small shifts between Normal, Lognormal, and Laplace distributional scenarios. Nonetheless, the EWMA chart recognizes large shifts more efficiently than others. Adapting the proposed control chart to the flow width dataset produced results consistent with the research findings.