## 1 Introduction  
The renewed impetus for the 2030 Agenda [1], combined with the emergence of Industry 4.0, aimed to modernize manufacturing via sustainable processes, cyber-physical systems, the Internet of Things (IoT), and cloud computing [2]. These developments are particularly relevant to asset maintenance, which traditionally falls into three categories: (1) corrective maintenance: reactive repairs after unexpected failures; (2) preventive maintenance: scheduled component replacements based on time or usage intervals, and (3) condition-based maintenance: real-time monitoring using sensors to predict failures without physical inspection [3].  
The rise in Industry 4.0 technologies (e.g., IoT, big data analytics) has revolutionized condition-based approaches by enabling continuous data collection, fault prognosis, and dynamic maintenance planning. This shift enhances productivity, sustainability, and asset lifespan estimation while reducing unplanned downtime. This predictive approach is only applicable to monitored equipment, as it relies on sensor-derived data to accurately estimate asset lifespan [4]. By integrating real-time data, maintenance strategies--including condition monitoring, fault diagnosis, prognosis, and planning--are dynamically optimized [5]. The evolution of manufacturing paradigms, from mass production to lean manufacturing, reflects growing societal demands for greener and more sustainable industrial practices. Modern companies must balance economic growth with social and environmental responsibility [6]. Sustainable manufacturing embodies this shift, emphasizing resource efficiency, cleaner technologies, and safeguards for both environmental integrity and human health [7; 8]. To achieve these goals, a transition from a linear to a circular economy is essential. This systemic change prioritizes waste reduction through policies promoting recycling, reuse, remanufacturing, and material recovery--all while maintaining economic viability [9; 10; 11].  
The EN 16646 standard [12] emphasizes that effective maintenance policies must integrate diverse perspectives by understanding the interdependencies between operations and value creation across the asset lifecycle. The transition from preventive to predictive and sustainable maintenance can be framed within the continuous improvement paradigm. Continuous improvement traces its origins to the mid-20 th century with [PERSON]'s development of the PDCA (Plan-Do-Check-Act) cycle [13], a four-stage iterative problem-solving framework. This methodology can be systematically applied through the following stages: diagnosis (problem description and analysis), root cause analysis, definition of countermeasures and target values, implementation, monitoring, and standardization [14]. Therefore, the \"Plan\" phase comprises: (1) redefinition of key performance indicators (KPIs) and target asset selection, (2) sensor selection and deployment, and (3) data collection, analysis, and storage. The \"Do\" phase involves: (4) predictive model development, and (5) staff training and competency development. The \"Check\" phase includes: (6) solution validation and performance monitoring within a defined temporal scope. Finally, the \"Act\" phase involves: (7) scaling the implementation to additional assets, the standardization of procedures, and monitoring to prevent performance degradation over time (as illustrated in Figure 1). Once the \"Act\" phase is completed, a decision is made either to replicate the solution in new facilities and/or assets or--if the results have not met expectations--to restart the cycle to achieve the objectives initially planned. For comprehensive guidance on implementation tools, methodologies, and critical success factors, refer to [15; 16; 17; 18; 19].