Ahmed Maged

Construction megaprojects, large‐scale, complex, and capital‐intensive, are particularly prone to inefficiencies, cost overruns, delays, and environmental degradation due to fragmented workflows, stakeholder misalignment, and resource intensity. Lean Construction Practices (LCPs), with their focus on waste elimination, value stream optimization, and collaborative planning, offer a targeted response to these megaproject‐specific challenges, directly supporting sustainability goals. However, empirical evidence on the systematic integration of LCPs into sustainable megaproject delivery, especially in emerging economies, remains sparse. This study addresses this gap by (1) quantifying the causal impact of LCPs on Overall Sustainable Success (OSS), defined as a tripartite construct encompassing environmental resilience (e.g., waste/energy reduction, pollution control), social inclusivity (e.g., worker safety, collaboration, equity), and economic efficiency (e.g., cost control, rework minimization, productivity gains), using PLS‐SEM; and (2) identifying the most predictive LCPs for OSS using explainable Machine Learning (ML), with a focus on China, the world's largest megaproject market. Using survey data from 379 randomly sampled professionals engaged in megaprojects across Mainland China and Hong Kong, results confirm a strong LCP‐OSS relationship ( β = 0.748), with Gradient Boosting achieving 82% accuracy and 88% ROC‐AUC. Crucially, SHAP analysis is innovatively applied at both indicator and construct levels, enabling actionable prioritization of LCPs, a methodological advance for sustainability research. Top practices include Safety and Quality Assurance (22.2%), Customer Focus and Waste Elimination (20.8%), and Standardization and Process Transparency (18.8%). While contextually grounded in China, findings align with SDGs 9, 11, and 12, suggesting transferability to similar emerging economies. The framework provides policymakers and practitioners with evidence‐based levers to integrate sustainability into megaproject delivery, without compromising efficiency or equity.

Life testing of engineering systems with dependent components requires robust multivariate methods. Parametric approaches depend on restrictive assumptions, limiting their use in complex or unknown lifetime distribution settings. This study evaluates nonparametric methods for comparing parallel system lifetimes under minimal sample requirements. Three distribution‐free tests are considered: the rank‐energy test, the Wilcoxon‐type rank‐sum precedence test and the Lepage test, the latter two applied to Minkowski distances from lifetime vectors. Results suggest that the Lepage test on Minkowski distances generally outperforms the other two, offering a robust method to compare multi‐component system lifetimes.

In today's competitive landscape, fulfilling customer expectations and achieving a competitive edge are crucial for business success. These objectives can be attained by effective monitoring in both manufacturing and service sectors to enhance quality, reduce variation, and augment productivity. The control chart, a widely used tool for this purpose, has attracted significant attention from researchers for its ability to detect anomalies and manage out‐of‐control situations. The optimization of control charts, a central focus of this review, not only enhances the detection effectiveness but also maintains the desired false alarm rate, thus ensuring efficient process control without additional cost, complexity, or operational challenges for shop floor personnel. The optimization process involves adjusting charting parameters like the sample size, sampling interval, and control limits within a hypothesis testing framework, thereby achieving optimal system performance. Numerous optimization models have been developed to enhance control chart performance. This paper introduces a classification scheme to analyze and categorize the existing research on control chart optimization. By conducting a thorough review of more than 240 articles, the study pinpoints research gaps and offers valuable insights, thereby advancing the future research in this domain.

Companies are harnessing the power of artificial intelligence to analyze Big Data and improve their operations. On the other hand, Six Sigma has been used for quality improvement since the 1980s. The use of Six Sigma may redefine industrial precision and provide more practical ways to handle Big Data when aided by machine learning practices. In this research, we explore how these two concepts can work together effectively to further enhance productivity, improve quality, and reduce variation. This article focuses on the ability of define, measure, analyze, improve, and control methodology to adapt to this synergy. We suggest the use of three machine learning methods, namely recurrent neural networks, genetic algorithms, and convolution neural networks, as an aiding tools to the design of experiments and control chart that are commonly used in the analyze and control phases. We also provide a case study to demonstrate the use of machine learning aided Six Sigma in a real-world industrial setting. The proposed machine learning aided Six Sigma applied in this research was found to accurately help make use of the Big Data collected from preinstalled sensors in the manufacturing processes. Machine learning can assist in overcoming Six Sigma constraints caused by human mistakes, inadequate information, and restricted analytical skills. Moreover, it can entertain and utilize massive data collected resulted from technological advancement of sensors and Internet of Things.