Production & Manufacturing Research

The current industrial scenario is characterized by the continuous research of optimization and efficiency in the scope of maximising productivity while reducing environmental, economic, and social impacts. Especially in view of the increasing importance that sustainability is gaining for society, companies are more and more interested in transitioning towards sustainable processes.

Maintenance plays a crucial role in this context since, when correctly programmed and executed, it allows for the continuous operation of equipment while minimizing productivity losses. Additionally, well-maintained equipment ensures minimal resource consumption, waste generation, and increased safety for workers. Not only, maintenance processes also can undergo a sustainable transition by rethinking the approach and the frequency maintenance is carried out, whilst always considering the match with the production necessities. In support, manufacturing companies have recently focused their efforts on integrating data-driven and intelligent decision-making approaches into their maintenance processes to achieve optimal decision-making for its management and execution and higher sustainability.

For maintenance, data-driven decision-making has characterized the last decade of research, with the uprising importance of themes related preventive, condition-based and predictive maintenance. In the past few years we are also seeing a surge of contributions in the field of artificial intelligence supported approaches for tackling a wide range of maintenance problems. Therefore jointly tackling the themes of data-driven and intelligent decision-making for sustainability in maintenance is necessary for companies who want to remain competitive and, at the same time, include the sustainability aspect in their processes as a selling point. In particular, maintenance could leverage data-driven decision-making for introducing analytics aimed to optimize resource allocation, anticipate maintenance needs, and enhance overall system resilience.

Literature is requiring additional studies on the integration of sustainability in maintenance, with the development of data-driven decision-making processes able to overcome current barriers and challenges towards its effective and sustainable management and execution.

The aim of this article collection is to encourage original and latest research dealing with data-driven & intelligent decision-making for sustainability in maintenance. The article collection welcomes both theoretical and practical contributions aimed at providing an overview of the current research while detailing future research paths on the topic. In particular, the article collection aims at exploring studies focused on the introduction of data-driven decision-making processes supporting higher sustainability in maintenance, also contributing to the definition of sustainability index for maintenance impact evaluation or in support to the knowledge management for improved sustainability in maintenance. Thus, this article collection seeks to explore new approaches and perspectives on sustainability in maintenance, to help companies in defining effective and efficient transition paths.

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