Afshan Naseem

Failure mode and effect analysis (FMEA) is widely used in numerous manufacturing processes to identify and minimize various problems; nevertheless, its usefulness is dubious until it is more directly associated with the resolution of complex issues. In addition, the disruptions experienced in the production process are interrelated and cannot be considered independent of each other. It is, therefore, pertinent to determine not only the significance of the faults, but also the nature of faults. This research includes the use of cloud model theory and evaluation of decision‐making experiments and test models to cater to the needs of industry. The current research is offering three things. First, the issue of random and uncertain decisions is resolved by applying Cloud Model Theory. Second, in order to identify critical faults, the Decision‐Making Trial and Evaluation Laboratory (DEMATEL) has been extended to take cloud model theory into account. Third, a case study is provided to highlight the advantages and effectiveness of this integrated approach. This integrated approach ensures that managers are cognizant of major risks and vulnerable areas to take precautionary measures in advance. As a result, losses resulting from detrimental effects and their deep‐rooted causes are minimized, and production processes become efficient and effective.

Purpose: In today's rapidly evolving environment, organizational survival and growth highly depend upon the ability of supply chains to adapt with agility and responsiveness. Traditional supplier selection models that focus solely on cost, quality, and delivery are no longer sufficient, especially in sectors where disruptions and uncertainties are frequent. The study addresses this problem by integrating agility and responsiveness into the supplier evaluation framework and uses a combination of Best-Worst Method (BWM), Decision Making Trial and Evaluation Laboratory (DEMATEL) and VIKOR techniques. Design/methodology/approach: The study uses a combination of BWM, DEMATEL, and VIKOR. For model validation, profiles of five suppliers were established and analyzed by a panel of 11 experts using 34 criteria. These criteria were categorized into Agility, Responsiveness, Quality Management, Cost Efficiency, Supply Chain Innovation, Supplier Relationship, Sustainability, Risk Management, Technological Adaptability, and Innovation Capability. The BWM served to establish the relative priority of the criteria; DEMATEL identified the structure of interrelationships among the criteria and the causal influence among them; and VIKOR provided a compromise ranking of the suppliers based on their overall performance. Findings: This study highlights the significance of agility and responsiveness in supplier selection and demonstrates the efficacy of a hybrid BWM-DEMATEL-VIKOR approach. Among the 34 criteria analyzed, several emerged as particularly critical: dynamic resource reallocation, product durability, customer satisfaction, and supplier responsiveness. These criteria were not only given the highest weights through the BWM process but were also shown to influence several other capabilities in the DEMATEL analysis, indicating their role as foundational drivers of supply chain agility. Originality/value: The study draws upon the Resource-Based View (RBV), Contingency Theory, and Transaction Cost Economics (TCE) and extends traditional supplier selection models by explicitly incorporating the critical dimensions of agility and responsiveness, which are essential in today's dynamic and uncertain supply chain environments. By integrating the BWM, VIKOR, and DEMATEL methods, the research presents a systematic, multidimensional decision-making approach that addresses both strategic and operational challenges. Such a framework enables supply chain managers and procurement specialists to more effectively manage vulnerabilities while enhancing the agility of their supplier networks.

Purpose: This research aims to develop and validate a comprehensive machine learning (ML)-driven Quality 4.0 maturity assessment framework for service organizations grounded in Socio-Technical Systems (STS) theory. Design/methodology/approach: An STS-integrated framework comprising 18 Quality 4.0 dimensions across People, Process, and Technology subsystems was developed through systematic literature synthesis and expert validation. Survey data were collected from 285 service organizations and analysed using Latent Class Analysis to empirically derive five maturity levels. Six supervised machine learning algorithms were evaluated, with the Random Forest classifier achieving the highest predictive performance (accuracy = 0.963). Model interpretability was ensured using SHAP (Shapley Additive Explanations) to quantify the relative importance of individual maturity dimensions. Findings: The ML-based model reliably classifies organisations into five empirically derived maturity levels. The SHAP analysis highlights Big Data Analytics, Digital Quality Management, and Technology Integration as the strongest predictive signals for Quality 4.0 maturity classification. The empirical results show that 41% of service organizations operate at a Basic Quality level, whereas only 7% demonstrate maturity aligned with Quality 4.0 excellence. Practical implications: Managers can use an ML-driven model to assess their current Quality 4.0 maturity, identify critical improvement areas, and prioritize investments in AI, digital quality management, cultural readiness, and capability development. The model's explainable ML outputs also support evidence-based decision-making and clearer alignment between technological adoption and organizational development. Originality/value: This study introduces an empirically grounded, socio-technical, and explainable ML based Quality 4.0 maturity assessment framework tailored to service organizations in a developing economy context. By combining Latent Class Analysis with interpretable machine learning, the framework advances both methodological rigor and practical transparency in maturity assessment.

Purpose: Following the COVID-19 pandemic, the home-textile industry has experienced a decline in production orders, heightening competition and making it more difficult to maintain consistent customer satisfaction and operational performance. To address these challenges, this study employs a structured, simulation-based Quality Function Deployment (QFD) framework integrated with Monte Carlo Simulation (MCS) to identify, quantify and prioritize key process risks within textile manufacturing. Through this integrated approach, strategic performance alignment is achieved by mapping customer-driven requirements to operational risk data, enabling the systematic evaluation of process efficiency and reliability under uncertainty. The framework thus contributes to Strategic Alignment Theory by providing an empirical foundation for evaluating and maintaining a strategic fit between external market demands and plant-level operational capabilities. Design/methodology/approach: The study starts with the identification of core customer requirements, specifically targeting improved quality, prompt delivery schedules, better working conditions, cost-effectiveness and overall safety in the workplace. These requirements are translated into technical requirements through QFD. MCS is then used to prioritize the identified risks by modeling uncertainty in the risk matrix. The study is conducted in the home-textile industry, where the following integrated approach remains underexplored. Findings: The risks were categorized according to severity, and four high-priority risks, i.e. R1, R9, R11 and R3 were identified at the 95th percentile level. These risks represent major operational vulnerabilities, ranging from production-process disruptions to efficiency bottlenecks. Originality/value: This study contributes to Strategic Alignment Theory (specifically the concept of Strategic Fit) within the performance management field by introducing a structured, probabilistic framework that operationalizes customer-driven requirements. By integrating QFD with MCS, it extends the literature on Strategic Performance Measurement Systems (SPMS) beyond traditional deterministic scoring. Specifically, existing theories of Strategic Fit are amended to incorporate statistical variability, effectively bridging the theoretical gap between static quality planning and stochastic operational risk management. The study proposes a novel extension to the conventional House of Quality by embedding simulated probability distributions directly into risk prioritization. This advancement shifts the theoretical focus from static, descriptive risk assessments to dynamic, probability-driven evaluations, redefining how operational vulnerabilities are quantified and managed in process-driven manufacturing sectors.

Workplace safety remains a critical concern across industries, with self-reported and unreported work injuries posing significant risks to employee well-being and organizational sustainability. This study examines the role of moral disengagement as a mediating factor in the relationship between psychological safety climate and both self-reported and unreported work injuries. Drawing on social cognitive theory, this study argues that a strong psychological safety climate discourages moral disengagement, which, in turn, influences employees’ likelihood of reporting or concealing workplace injuries. The study also considers the specific reporting practices found in Pakistani industrial settings, where safety procedures are often informal and shaped by hierarchical relations. Data was collected from 303 employees working in large-scale industries in Pakistan, including the woolen, textile, shoe, sugar, and seed sectors. The analysis was conducted using partial least squares structural equation modeling (PLS-SEM). The psychological safety climate was negatively associated with moral disengagement, whereas moral disengagement had an inverse relationship with self-reported work injuries and a positive relationship with self-underreported work injuries. Moreover, moral disengagement mediates the relationship between psychological safety climate and workplace injuries. The model explained .078 variance ( R ² ) in MD, .394 in SR, and .357 in SU. Similarly, the effect size f ² was 0.163 for MD, 0.025 for SR, and 0.027 for SU. This study contributes to the workplace safety literature by integrating moral disengagement as an intervening mechanism between psychological safety climate and workplace injuries. The contribution is context-specific, offering insight into how cognitive mechanisms operate in Pakistani manufacturing environments where reporting norms are often informal. The findings emphasize the need for organizations to cultivate a robust psychological safety climate to minimize moral disengagement and encourage accurate injury reporting. The theoretical and practical implication of the study shows that by fostering psychological safety and ethical practices, workplace injuries are reduced, and firms are urged to make safety a core priority, especially in high-risk settings. Limitations and future research directions are also discussed. The study also acknowledges limitations such as purposive sampling, cross-sectional design, and reliance on self-reports, which can be addressed in future research. Limitations and future research directions are also discussed.

The application of Industry 4.0 technologies specifically the internet of things (IoT) and big data (BD) have changed the global industrial system. Many emerging economies are increasingly investing in Industry 4.0 technologies but these economies have not achieved the expected benefits due to insufficient digital infrastructure, financial liquidity and manpower inadequacy. This study reveals the impact of BD and IoT on operational performance (OP) in manufacturing context, with the mediating role of employee training (ET). Drawing upon the resource-based view (RBV) theory, the research examines how the strategic incorporation of technological abilities and human capital leads to improve productivity, quality, flexibility, and cost efficiency in manufacturing sector. A quantitative method was used to analyse survey data collected from 320 managers and executives representing manufacturing firms engaged in Industry 4.0 implementation. To analyse the relationship among variables statistical analyses were performed using SPSS 26 and Smart PLS 4. The findings indicate that BD and IoT have a positive and significant impact on OP, while ET further strengthens these effects by establishing the connection between technological aspects and performance outcomes. The results underscore the importance of ongoing employee training and digital innovation endeavours. This study recommends valuable information for practitioners and policymakers to enhance sustainable competitiveness through advanced technology and human resource abilities. Empirically, this research is among the first to study the impact of Industry 4.0 adoption with employee training in developing economies, providing valuable insights for both researchers and practitioners.