Design of a new adaptive EWMA control chart

Abstract

The memory‐based control charts (CCs) such as exponentially weighted moving average (EWMA) and Cumulative sum (CUSUM) are good for quick detection of small or moderate shifts in the process mean. This study proposes a memory‐based CC to monitor the mean of a normally distributed process. The idea is to estimate the mean shift using an estimator based on an EWMA statistic and after that determine the smoothing constant of plotting EWMA statistics through the proposed continuous function. The performance of the proposed adaptive EWMA (AEWMA) chart is focused on utilizing extensive Monte Carlo simulations method with various shifts in the process mean. Taking into account the run‐length (RL) profiles, as an evaluation tool, the proposed chart is efficient as compared to the competitor charts. The demonstration of the proposed chart is presented by using a real dataset from the manufacturing process of substrates where the quality characteristic is the resistance of flow width.

Monitoring the mean of autocorrelated data with long memory from cable production using one‐sided runs rules schemes with ARFIMA (1,d,1) model

“…This implies that the ZSEARL is larger than the corresponding SSEARL and that the difference in the detection ability between the ZS and SS cases of the RR 5-of-5 /RR 2-of-5 schemes is higher than that of the IRR 5-of-5 /IRR 2-of-5 schemes. We further compare the RR and IRR ("RR 5-of-5 and IRR 5-of-5 " and "RR 2-of-5 and IRR 2-of-5 ") schemes for ZS and SS cases using Equation (17). For the RR 5-of-5 and IRR 5-of-5 schemes in Table 2 (with d = 0.1), ZSPCI ranges from 1.10 to 1.20 and SSPCI ranges from 1.04 to 1.08 for d = 0.1.…”

“…14,15 have led to other improved control charts for detecting small shifts. [16][17][18] Despite the sensitivity of CUSUM and EWMA charts in detecting small shifts, their applications have been limited due to the complex computations involved in the construction of both charts. 4 This study, therefore, focuses on much simpler runs rules charts with the capability of effectively detecting small and moderate shifts in the manufacturing process.…”

“…The works of refs 14,15 . have led to other improved control charts for detecting small shifts 16–18 . Despite the sensitivity of CUSUM and EWMA charts in detecting small shifts, their applications have been limited due to the complex computations involved in the construction of both charts 4 .…”

New adaptive EWMA CV control chart with application to the sintering process

“…In the context of SPC, this study introduces a novel approach for effectively monitoring the process CV through the development of an AAEWMA CV control chart by using a novel function approach. The design of the proposed chart aims to enhance sensitivity by incorporating the CV as a monitoring tool within the adaptive framework recommended by Sarwar and Noor-ul-Amin 29 , Arshad et al 30 , Noor et al 31 , and others. The primary objective is to determine the SC value by predicting the shift size of the process CV parametric value.…”

“…The primary objective is to determine the SC value by predicting the shift size of the process CV parametric value. The incorporation of the function which was given by Sarwar and Noor-ul-Amin 29 has been identified to improve the computations within the architecture of the proposed AAEWMA CV chart. Run-length (RL) values are a tool to assess the performance of any chart so, the suggested AAEWMA CV chart undergoes evaluation based on these RL values.…”