João Carlos Oliveira Matias

The assembly line balancing problem (ALBP) is an optimization problem which involves assigning tasks to stations to maximize line efficiency. In the context of Industry 4.0, assembly lines must be flexible and adaptable to stochastic and real-world conditions. However, in the literature, line balancing is mostly studied in deterministic environments. As a result, this paper presents a robust optimization approach to bridge the gap between theory and practice by addressing a real-world ALBP in the furniture industry. A mixed integer programming (MIP) model is proposed, considering uncertainty in task execution times, which are expressed as intervals of possible values, and the sequence of module assembly in addition to the standard ALBP constraints. Then, it is linearized by duality to be solved by Gurobi. Two objectives, (1) cycle time minimization and (2) tool efficiency, are optimized lexicographically using the proposed MILP models. Computational experiments and Monte Carlo simulations demonstrate that robust solutions outperform deterministic ones, ensuring stable throughput under variability. The study highlights the trade-off between efficiency and robustness, offering practical insights for furniture manufacturers.

Maintenance management increasingly depends on effective information flow and coordination between internal teams and external service providers. This study investigates the use of Business Process Model and Notation (BPMN) to support the formalization of Computerized Maintenance Management System (CMMS) workflows and improve transparency, decision-making, and interorganizational coordination. A single case study was conducted in the maintenance department of an electricity distribution company characterized by tacit knowledge, informal communication practices, and limited process formalization. Existing corrective maintenance workflows were analyzed and modeled using BPMN to identify inefficiencies, decision points, and opportunities for improvement. The proposed BPMN models were aligned with CMMS operational states associated with anomaly management and work-order execution processes and supported by a procedural manual. Results obtained during a three-month observation period suggest reductions in training time, email communications, and dependence on individual decision-makers, together with increased use of CMMS workflow functionalities and improved process traceability. These findings provide preliminary evidence, derived from operational indicators within a single case study, that BPMN-supported process formalization may contribute to workflow standardization, operational clarity, and knowledge management in maintenance-intensive environments. Given the single-case design and limited observation period, the results should be interpreted as context-specific and not directly generalizable to the broader energy sector.

In the context of the energy transition and increasing environmental pressures, firms’ competitiveness increasingly depends on effective energy–cost management and environmental performance. However, integrating energy, financial, and environmental dimensions into performance evaluation remains methodologically challenging. This study develops an exergy-based two-stage Network Data Envelopment Analysis (NDEA) framework linking energy and cost management in Stage 1 to financial performance under environmental constraints in Stage 2. Using Refinitiv/London Stock Exchange Group (LSEG) data for 45 firms across 18 industries in Portugal and Spain in 2023, the model integrates thermodynamic, financial, and environmental indicators within a unified efficiency framework. The Exergy-to-Sales ratio serves as a fixed intermediate link between thermodynamic and financial efficiency. Results show that incorporating environmental performance increases the number of fully efficient firms in overall efficiency from 3 to 5, while 27 firms move closer to the efficiency frontier. The environmental specification reduces the average improvement required for Return on Sales (ROS) and Return on Equity (ROE) but increases the adjustment needed for Return on Assets (ROA), indicating heterogeneous profitability responses. The study contributes to sustainable performance assessment literature by integrating exergy analysis and NDEA within a unified decision-support framework for managers and policymakers pursuing competitiveness and decarbonization objectives.

The transition from Industry 4.0 to Industry 5.0 represents a shift toward human-centric work systems that prioritize employee well-being and meaningful human–technology collaboration. Research examining employee well-being, job satisfaction, and organizational performance in Industry 5.0 contexts remains conceptually fragmented and methodologically heterogeneous, limiting cumulative theoretical development. This study addresses how fragmented insights on employee well-being, job satisfaction, and organizational performance can be conceptually integrated through a human-centric operational excellence perspective. Accordingly, an integrative review was conducted using PRISMA 2020-guided screening and reporting procedures, resulting in a final sample of 84 peer-reviewed studies published between 2015 and 2025. The literature was analyzed through inductive thematic synthesis to identify recurring patterns, tensions, and conceptual configurations within digitally mediated work environments. The findings indicate that employee well-being and job satisfaction Industry 5.0 contexts are multidimensional, dynamic, and frequently paradoxical: digital technologies simultaneously function as enablers of autonomy, meaningful work, and cognitive support while also generating technostress, algorithmic control, and cognitive overload. Relationships between well-being, satisfaction, and performance appear non-linear and context-dependent, with high performance sometimes coexisting with employee strain. In this sense, this study contributes to the Industry 5.0 literature by advancing human-centric operational excellence (HCOE) as an interpretive lens for reconciling human–technology tensions without presuming linear causal relationships.

Wearable technologies have emerged as promising tools for supporting Occupational Safety and Health through continuous and multimodal monitoring of physiological, biomechanical, and environmental risk factors. However, evidence regarding their real-world effectiveness and implementation remains fragmented. This study presents a systematic literature review conducted in accordance with PRISMA 2020 guidelines, synthesizing evidence from 60 studies addressing wearable-based monitoring, assessment, and intervention in occupational contexts. The review examines the types of technologies applied, the risks and functions addressed, the evidence on effectiveness, the evaluation metrics used, and the main barriers affecting implementation. The findings show that wearable technologies are mainly applied to ergonomic, physiological, environmental, and critical-event risks, using devices such as inertial sensors, biosensors, smart personal protective equipment, and exoskeletons. While the evidence indicates strong potential for real-time monitoring, risk detection, and data-informed decision-making, most studies rely on controlled or short-term evaluations, and consistent evidence of sustained accident reduction remains limited. The results also highlight technical, organizational, ethical, and human-related barriers, including usability, interoperability, privacy concerns, worker acceptance, and data governance. Based on this synthesis, a conceptual human-centered implementation model is proposed to support responsible and context-sensitive adoption.

The wind energy sector continuously improves manufacturing processes to meet the growing demand for renewable energy. Recognizing the complexity and extended duration of the wind turbine blade production process, alongside the need to uphold high-quality standards, this work aims to increase the efficiency of the production process of wind turbine blades by improving its cycle time production. To attain this objective, bottlenecks and critical subprocesses were identified, lead time and process cycle time were reduced, and production throughput was increased by minimizing non-value-added activities. To achieve these objectives, researchers employed action research and implemented a set of lean manufacturing tools. After implementing the suggested tools and improvements, the company experienced positive impacts including cost and waste reduction, better workplace organization, enhanced safety and final product quality, and improved efficiency and productivity. This study is relevant to firms seeking to enhance operational efficiency and quality by implementing lean practices. Thus, advancements in materials, automation, quality control, and digital technologies are driving improvements in the production of wind turbine blades. This work examines an alternative method for enhancing the production process of wind turbine blades by employing various lean manufacturing tools. These tools are relatively easy to implement, do not necessitate significant investments, and align well with the realities faced by many companies.

Small and medium-sized enterprises (SMEs) struggle to integrate Lean practices with sustainability due to high methodological complexity and the frequent neglect of the social dimension. This study develops the Simple, Sustainable, and Inclusive Lean Model (SSILM), a conceptual framework designed to bridge these gaps. The methodology involved a systematic meta-evaluation of 31 existing Lean–Sustainability models against 14 operational criteria tailored for SMEs. Findings reveal that current models lack social integration and practical scalability for resource-constrained environments. The proposed SSILM is structured in six phases, from characterization to analysis, prioritizing low-cost participatory tools and strategic innovation. This study contributes a theoretical bridge between operational efficiency and the Triple Bottom Line, specifically emphasizing the social pillar. As a conceptual paper, its primary limitation is the lack of empirical field testing, which establishes a clear roadmap for future longitudinal research in industrial contexts.