Tahir Abbas

1. Typically, control charts are crafted to monitor a single quality characteristic, whether qualitative or quantitative, within a process. However, quality attributes are interconnected with auxil...

The mixture models are used in engineering processes to monitor the multiple‐cause defect systems. Most cumulative quantity control chart studies are based on simple and two‐component produce defect systems. According to the Bayesian framework, this paper proposes a cumulative quantity control chart for a three‐component mixture of Rayleigh distribution. The control structure of the suggested chart is designed, and its performance is judged using useful measures such as average run length (ARL), average length of inspection (ALI), and extra quadric loss function (EQL). Moreover, the Bayes estimators and posterior risk under various priors such as (uniform prior, Jeffery's prior, and square root gamma prior) by using symmetric and asymmetric loss functions such as the DeGroot loss function, precautionary loss function, and squared error loss function are also derived in this study. To highlight the practical aspects of the proposal, a case study is also provided.

This study explores using a structured quality framework to enhance patient care quality and safety in healthcare environments. Assessing the performance of healthcare services is a complex task due to the wide range of health conditions among patients, making it difficult to establish consistent evaluation metrics. The study employs a support vector machine (SVM) regression model to address this issue, which helps analyze and manage patient risk factors effectively. To enhance the monitoring of healthcare service quality, this study introduces a new SVM‐based adaptive risk‐adjusted exponentially weighted moving average (SVM‐ARAEWMA) control chart. This chart is designed using the residuals generated by the SVM regression model, allowing for a more accurate assessment of variations in patient outcomes. This study demonstrates how SVM effectively assesses patient conditions by applying this methodology to real‐world data from cardiac surgery patients. Compared to traditional risk‐adjusted exponentially weighted moving average (RAEWMA) and adaptive RAEWMA (ARAEWMA) control methods, the SVM‐ARAEWMA chart is more effective in identifying critical changes in healthcare quality and patient outcomes.

The Inverse Gaussian distribution (IGD) is a probability distribution with several applications in various fields. The proposed study is prone to consider the problem of monitoring the shape parameter of IGD using the Bayesian method. The Bayesian setup effectively handles the situation in the manufacturing industry where parametric uncertainty is unavoidable. In this study, we focus on determining the monitoring threshold for the shape parameter of the IGD in phase-II and designing Shewhart control charts based on classical and Bayesian methods under different loss functions (LFs). The influence of hyperparameters on upper control limits (UCLs) has been explored. The evaluation is based on different performance measures such as average run length (ARL), the standard deviation of run length (SDRL), and the median of run length (MDRL). The simulation study illustrates the effectiveness of the proposed Bayesian Shewhart charts compared to the classical Shewhart chart. The suggested approach for Shewhart charts proves highly effective in promptly identifying shape parameter shifts and outperforming their classical counterpart in detecting faults quickly. Using a real dataset, the proposed technique is also exemplified, and the findings validate the conclusions based on simulative results.

In this article, we introduce the Bayesian adaptive Max‐EWMA control chart designed to monitor both the mean and variance of processes following a lognormal distribution. By employing various loss functions, our Bayesian control chart demonstrates robust performance in detecting shifts in both the mean and dispersion across a wide range of magnitudes. We utilize Monte Carlo simulation techniques to compute run length characteristics, providing a thorough comparative analysis of the proposed chart against existing control charts in terms of run length performance. Our findings reveal the superior sensitivity of the Bayesian control chart to shifts of different magnitudes. To illustrate the practical applicability of our approach, we present a case study on the hard‐bake process in semiconductor manufacturing, showcasing its effectiveness when applying various loss functions. In conclusion, our research highlights the enhanced performance of the proposed Bayesian chart in detecting out‐of‐control signals.

This article focuses on operationalizing a quality improvement framework to improve the quality and safety of patients in hospitals. Assessing the effectiveness of health care services, especially utilizing various patient health statuses, poses several difficulties. The artificial neural networks model assesses patient risk factors and enhances proper management. Our proposed approach extends the exponentially weighted moving average (EWMA) control chart to a risk‐adaptive EWMA chart. This chart is developed from residuals estimated from the artificial neural networks model, thus allowing an assessment of actual data from the patients undergoing cardiothoracic surgery. For patient evaluation, we employ artificial neural networks to establish the suggested control chart. The results indicate that this chart outperforms other methods, such as the risk‐adjusted EWMA chart for shift detection, which may present improvements in the healthcare system's patient care.