In recent decades, the business paradigm has been transforming into the face of global challenges such as climate change and resource scarcity, consolidating sustainability as a strategic pillar that integrates the economic, environmental, and social dimensions. In parallel, Lean Philosophy, focused on eliminating waste and creating value, has been widely adopted as an effective management model. Despite the potential for its integration, literature reveals significant gaps, especially regarding the social dimension, which is often underexplored compared to the environmental and economic dimensions. To address this gap, this study identifies, analyzes, and synthesizes scientific literature on the integration between Lean and sustainability, with a special focus on the social dimension, using a systematic literature review conducted according to the PRISMA guidelines. A total of 132 articles published between 2011 and 2024 were analyzed, obtained from the Scopus, Web of Science, and ScienceDirect databases. The results demonstrate a growing convergence between the two concepts, highlighting the centrality of the human factor, namely well-being, safety and health at work, and ethical practices, and identifying challenges and opportunities for future research focused on a more holistic approach to organizational sustainability.

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.

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.

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.

Industrial maintenance has increasingly evolved into a strategic function for improving asset reliability, extending asset lifecycle, and supporting sustainability objectives. However, the literature remains fragmented, with limited integration between digital twins, maintenance practices, and sustainability-oriented decision-making. To address this gap, the study performs a systematic review of 49 publications indexed in Scopus and Web of Science and introduces an integrative conceptual framework for Sustainable Maintenance 4.0. The analysis explores the role of digital twins, as a key enabling technology within the Industry 4.0 landscape, in supporting the shift from reactive and schedule-based maintenance toward predictive and prescriptive strategies. The findings suggest that digital twins can enhance maintenance decision-making, improve asset reliability, and contribute to lifecycle optimization. The reviewed studies also report improvements in operational and energy performance, although these effects vary according to digital maturity, system configuration, and implementation scope. In addition, digital twins may support safer operations and workforce development through data-driven and immersive environments. Despite these benefits, challenges remain, including high investment requirements, interoperability limitations, cybersecurity risks, and the need for interdisciplinary skills. The proposed framework positions digital twins as a mediating element between physical assets, data acquisition, advanced analytics, maintenance services, and sustainability outcomes.

Purpose: Given the growing importance of sustainability, an efficient model is proposed that allows the identification and quantification of industrial waste. Methodology/Approach: First, a bibliographic review of the existing models of joint application was carried out: Sustainability, Lean Philosophy, and Industry 4.0. Critical analysis of the existing models of joint application of these concepts. Preparation of the model proposal and analysis of the results. Findings: The proposed model, in addition to contributing to compliance with current laws, allows a better perception of the waste produced, including its characterization. This promotes the directing of waste to responsible and competent entities for its collection and recycling, showing a change in the company in environmental management policies. Research Limitation/implication: The inherent cost of reorganizing the layout design can be limiting, as it depends on the type, size, business characteristics, and flexibility of your existing production system. Originality/Value of paper: It is a new model of joint application of the three concepts and fills the void of the existing models. This allows organizations to have a better understanding of the waste produced.