Current global challenges, such as climate change and resource depletion, demand a shift toward sustainable practices in all aspects of industrial domain. Maintenance, as one of the keys supports to keep the manufacturing performance, has a large potential impact on industrial environmental and social responsibilities. Given the current state of the literature, the assessment of maintenance's environmental and social impacts widely remains to be explored.

In the context of Industry 4.0, which involves the digitalization, automation, and interconnectedness of manufacturing and maintenance processes, Sustainable Maintenance 4.0 leverages technologies such as artificial intelligence (AI), big data analytics, data-driven prognostics, and predictive maintenance to enhance the sustainability of maintenance practices. Furthermore, the sustainability impact in maintenance is seldom evaluated beyond economic perspectives in the current literature, despite the availability of methods specific to the context of sustainability. Benefitting from these methods and the real-time capabilities of Industry 4.0 should allow defining and estimating new Key Performance Indicators (KPIs) that provide insight into the sustainability of industrial processes and maintenance beyond economic performance.

This Special Issue aims to highlight innovative research contributions and practical applications in modelling and estimating the impact of reliability analysis, maintenance actions, and related processes, on the domains of sustainability (economic, environmental and social).