As the global market becomes increasingly competitive and demanding, companies face the challenge of responding swiftly and efficiently to customer needs. To maintain a competitive advantage, organisations must optimise the usage of their assets. This study focuses on the critical role of maintenance management and presents a novel, cost-effective, and easily applicable model that integrates Industry 4.0 (I4.0) and Total Productive Maintenance (TPM) principles to enhance production processes. The proposed model incorporates a real-time monitoring system equipped with sensors, a gateway, and Internet of Things (IoT) services. These components enable data acquisition, transmission, storage, and visualisation through both mobile and fixed devices. The model’s effectiveness was validated through its implementation on a conveyor belt in a feed mill. The availability of the conveyor belt was around 89.5% before TPM implementation. After the implementation of TPM, it was possible to observe that the availability of the treadmill increased to 92.7% in a workweek of 105 h. On the other hand, it was possible to observe a considerable improvement in terms of the average time of each intervention, with a reduction of around 53%. Moreover, the model also facilitated improvements in the maintenance plan for the conveyor belt. Operators were trained to effortlessly comply with the new autonomous maintenance plan, enhancing operational efficiency. The joint application of I4.0 and TPM demonstrated significant advancements in maintenance management. The flexibility of the developed model allows for easy adaptation to monitor other condition parameters, enabling real-time decision-making for both the maintenance and production departments. This integrated approach holds the potential to drive process optimisation, ultimately contributing to improved productivity, cost-effectiveness, and competitiveness.

Companies actively seek innovative tools and methodologies to enhance operations and meet customer demands. Maintenance plays a crucial role in achieving such objectives. This study identifies existing models that combine Lean Philosophy and Industry 4.0 principles to enhance decision-making and activities related to maintenance management. A comprehensive literature review on key concepts of Lean Philosophy and Industry 4.0, as well as an in-depth analysis of existing models that integrate these principles, is performed. An innovative model based on the synergies between Lean Philosophy and Industry 4.0, named the Maintenance Management in Sustainable Operations (MMSO) model, is proposed. A pilot test of the application of the MMSO model on a conveyor belt led to an operational time increase from 82.3% to 87.7%, indicating a notable 6.6% improvement. The MMSO model significantly enhanced maintenance management, facilitating the collection, processing, and visualization of data via internet-connected devices. Through this integration, various benefits are achieved, including improved flexibility, efficiency, and effectiveness in addressing market needs. This study highlights the value of integrating Lean Philosophy and Industry 4.0 principles to improve maintenance management practices. The proposed MMSO model effectively leverages these principles, fostering agility, optimized resource utilization, heightened productivity and quality, and reduced energy consumption. The model not only serves as a tool for operational optimization and customer demand enhancement but also aligns with sustainability principles within the energy transition. Its successful application in the pilot test phase further reinforces its potential as a reliable approach for maintenance management and sustainable operations in both production and decision-making processes.

Conveyor belts (CBs) are widely used for the continuous transport of bulk materials. CBs must be extremely reliable due to the cost associated with their failure in continuous production systems. Thus, it is highly relevant in terms of maintenance and planning to find solutions to reduce the existing stoppages from these assets. In this sense, it is essential to monitor and collect real-time data from this piece of equipment. This work presents a case study, where a model that combines the Lean Philosophy, Total Productive Maintenance (TPM), and the enabling technologies of Industry 4.0 is applied to a CB. The proposed model monitors the CB and provides data on its operation, which, using the calculation of indicators, allows a more accurate and thorough view and evaluation, contributing to improving and supporting decision making by those responsible for maintenance. The data collected by the sensor help those responsible for maintenance and production, in the readjustment of more accurate and optimized planning, programming, and execution, supporting decision making in these areas. During the field test of a two-hour monitoring period (10 a.m. to 12 p.m.), the model identified six stoppages, resulting in approximately 88.6% of operational time for the conveyor. The field test showed that this model can result in more accurate maintenance decision making than conventional approaches. This research also contributes to the advancement of electronics and industrial automation sectors by empowering companies to transform maintenance methodologies. The potential of this approach and its implications for enhanced productivity and overall performance are therefore highlighted.

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.