On designing an efficient control chart to monitor fraction nonconforming. (20th November 2019)

• Record Type: Journal Article
• Authors: Abbas, Zameer; Nazir, Hafiz Zafar; Akhtar, Noureen; Abid, Muhammad; Riaz, Muhammad
• Is Part Of: Quality and reliability engineering international. Volume 36:Number 2(2020)
• Page Start: 547
• Page End: 564
• Publication Date: 2019-11-20
• Subjects: attribute -- average run length -- control charts -- fraction nonconforming -- p‐chart -- process -- progressive mean; Reliability (Engineering) -- Periodicals; Quality control -- Periodicals; High technology -- Periodicals; 620.00452
• DOI: 10.1002/qre.2590
• Languages: English
• ISSNs: 0748-8017

Abstract

Abstract: Process control measures are mostly applied in production and manufacturing industries. The most important tool used in these disciplines is control chart. In manufacturing and production processes, when the quality characteristic of interest cannot be directly measured, it becomes essential to apply attribute control charts. To monitor fraction nonconforming of the output, quality practitioners mostly prefer p ‐chart. In this article, a new progressive mean (PM) control chart is being proposed for monitoring drift in proportion of nonconforming products. The design evaluations of the proposed chart are made and compared through different properties of run length distribution, such as average run length (ARL), standard deviation of run length (SDRL), and some percentile points. The performance of the proposed chart is assessed under zero‐state and steady‐state scenarios. The proposed PM chart is compared with p ‐chart, moving average (MA) chart, optimal CUSUM chart, modified exponentially weighted moving average (EWMA) chart, and runs rules p ‐charts for monitoring fraction nonconforming. The proposed chart spots efficiently sustained disturbances in the process as compared with their existing counterparts. Two illustrative examples are also provided; one from real‐life application of nonconforming bearing and seal assemblies data and the other from simulated data for the implementation of PM chart.