An improved quality control chart to monitor the mean based on ranked sets

TL;DR

This paper introduces an improved control chart method using neoteric ranked set sampling (NRSS) for monitoring normal processes, demonstrating superior performance over traditional RSS methods through simulation and real data application.

Contribution

The paper develops a new control chart based on NRSS, enhancing process monitoring accuracy compared to existing ranked set sampling techniques.

Findings

NRSS control charts outperform RSS in most scenarios

Simulation shows improved average run length (ARL) performance

Application on concrete data illustrates practical effectiveness

Abstract

In this study, we considered the design and performance of control charts using neoteric ranked set sampling (NRSS) in monitoring normal distributed processes. NRSS is a recently proposed sampling design, based on the traditional ranked set sampling (RSS). We evaluated NRSS control charts by average run length (ARL), based on Monte Carlo simulation results. NRSS control charts performed the best, compared to RSS and some of its extensions, in most simulated scenarios. The impact of imperfect ranking was also evaluated. An application on strength concrete data serves as an illustration of the proposed method.