This study aims to design a Total Quality Management 4.0 (TQM 4.0) framework for service organizations to enhance service quality and customer satisfaction using the Plan-Do-Study-Act (PDSA) approach. The framework was evaluated through a case study in the airline industry, where service quality analysis identified online booking and online boarding as the weakest areas, supported by root cause analysis; the Economy class showed the lowest satisfaction level, at 8.37%, against an overall baseline satisfaction rate of 43.33%. Simulation-based evaluation showed that increasing the ease-of-online-booking and online-boarding scores from 2.61 to 3.14 to 3.41 and 3.74 (on a 5-point scale) raised Economy-class satisfaction to 11.51%, and this shift was assessed for internal consistency using statistical process control (SPC) applied to the simulated data. Because the framework was evaluated through simulation rather than real organizational implementation, the findings should be interpreted as indicative rather than conclusive. Additionally, a dashboard was developed to support continuous monitoring and data-driven decision making, illustrating the potential applicability of the proposed framework for continuous service improvement.
Won-Chol Kim, So-Yong Kim, Gyong-Sim Ri, Hyon-U. Kong, Myong-Chol Chae, Un-Sung Pak
Operating room (OR) scheduling remains a critical healthcare operations challenge, yet existing approaches suffer from three fundamental limitations: they either oversimplify the multi-resource synchronization requirements, lack provable performance guarantees for clinical decision-making, or fail to adequately integrate emergency responsiveness with elective efficiency. To address these gaps, we formulate the multi-resource OR scheduling problem as a parallel machine scheduling problem with multi-machine joint processing and propose a three-stage heuristic algorithm (TSA) that achieves a worst-case 4/3 approximation ratio for makespan while integrating four objectives: minimizing makespan, reducing resource costs, maximizing high-priority patient satisfaction, and minimizing emergency waiting times. Validated on 12,864 surgical records from the MIMIC-IV database, the TSA achieves a 15.79% increase in monthly surgical throughput, a 10.34% reduction in total resource costs, an 18.53% improvement in high-priority patient satisfaction, and a 50.00% reduction in critical surgery waiting time, all within 3.2 s for large-scale instances. The TSA provides a computationally efficient, empirically validated decision support tool that bridges the gap between academic optimization theory and practical OR management requirements.
Against the ongoing intelligent transformation of global healthcare, human factors engineering (HFE) is essential to the safe deployment of intelligent medical devices. Yet existing reviews largely offer chronological summaries and fragmented, scenario-specific analyses, leaving the field’s paradigm evolution and cross-scenario human–AI challenges under-synthesized. Following the PRISMA-ScR guideline, this scoping review synthesizes the literature to construct a four-stage developmental framework for medical HFE, delineate four interconnected core human–machine collaboration challenges, and clarify the prevailing theoretical, methodological, and translational constraints. It further identifies future directions—human–AI collaborative reliability evaluation, data-driven usability testing, and digital-twin-supported full-lifecycle validation. By integrating paradigm evolution, core challenges, and regulatory translation into a single analytical framework, this work provides a structured foundation for subsequent research, human-centered design, and regulatory evaluation of intelligent medical devices.
Data-driven algorithms enable the automation of management tasks that were previously performed by human managers. This paper examines the use of intelligent algorithmic systems to support managers in complex, human-related planning tasks in the work domains of production and logistics. The presented study aims to uncover how system, task, and organizational characteristics influence managers’ perceived system support for their planning performance. First, two research models are proposed, incorporating system-, task-, and organization-related variables based on the literature. Second, the models are evaluated using structural equation modeling (SEM) based on an empirical online study with 494 managers working in production and logistics. The results indicate that managers perceive greater system support when systems are integrated to a greater extent into the planning processes and are perceived as more intelligent and transparent. Additionally, the results show positive effects of the quality of organizational conditions on perceived planning support and job satisfaction. In the context of planning tasks, the positive effect of system use on planning support seems to diminish when tasks have a high impact on workers. In contrast, the positive effect becomes stronger the more relevant the tasks are considered to be for the company. The results provide insights into how intelligent planning support systems can be utilized effectively to enhance managerial planning performance, fostering not only high-quality outcomes but also fair and objective results for employees affected by the planning process.
