Quality and Reliability Engineering International· 2026Q2
Optimal Design of an Attribute Control Chart for Monitoring the Mean of Auto‐Correlated and Fine‐Quality Manufacturing Processes by Group Inspection
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- Q2SCImago
- 2026year
Short summary
A new attribute control chart, modeled on an AR(1) process and using a Cumulative Count of Conforming (CCC) scheme, precisely monitors the mean of autocorrelated manufacturing processes, outperforming existing charts.
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Key points
- Proposes a new attribute control chart for autocorrelated manufacturing processes, unlike traditional charts assuming independence.
- Models observations using a first-order autoregressive [AR(1)] process and integrates a Cumulative Count of Conforming (CCC) scheme.
- The chart is optimized for precise Average Run Length (ARL) monitoring.
- Demonstrates superior performance in detecting shifts in high-quality, autocorrelated processes via comparative analysis and a case study.
AI-generated from the title and abstract; the full text is not read.
Abstract
ABSTRACT Numerous attribute control charts have been developed to monitor process means; however, most rely on the assumption of statistical independence, rendering them inadequate for modern industrial environments where process variables exhibit significant autocorrelation. This study proposes a novel attribute control chart specifically designed for autocorrelated processes by modeling observations as a first‐order autoregressive [AR(1)] process. By integrating a Cumulative Count of Conforming (CCC) scheme, the proposed chart achieves precise monitoring of the Average Run Length (ARL). The chart's architecture is refined through a rigorous optimization model. To evaluate performance, we provide a comparative analysis assessing the impact of autocorrelation on monitoring effectiveness. Furthermore, a practical manufacturing case study demonstrates the chart's application. This proposed framework facilitates the prompt and accurate surveillance of high‐quality processes, addressing the inherent serial correlation often found in contemporary manufacturing systems.
The authors' abstract, as published at the source. Quality and Reliability Engineering International, 2026 · DOI ↗
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Statistics, Probability and UncertaintyDecision Sciences