@article{nokey,

title = {From Sinterklaas to Smart Maintenance Organizational Enablers},

author = {Giacomo Barbieri; Alexandra Mulder; Panashe Mangezi},

year  = {2026},

date = {2026-07-01},

urldate = {2026-07-01},

journal = {Work in progress},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {The Knowledge Product Catalogue as a Tool to Support Dissemination in Inter-Organizational Research Projects},

author = {Bas Van Oudenhoven; David Sanchez-Londono; Giacomo Barbieri; Jan Braaksma},

year  = {2026},

date = {2026-07-01},

urldate = {2026-07-01},

journal = {Work in progress},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{daSilveira2025,

title = {Quantifying the suitability and feasibility of predictive maintenance approaches},

author = {Nubia Nale Alves da Silveira and Annemieke Angelique Meghoe and Tiedo Tinga},

doi = {10.1016/j.cie.2024.110342},

year  = {2025},

date = {2025-08-01},

journal = {Computers & industrial engineering},

volume = {194},

issue = {110342},

abstract = {Predictive maintenance is a promising concept for maintenance optimization which requires reliable and accurate predictions of a system's lifetime. Despite the many studies on this topic, selecting the best approach is still a topic of debate. In general, predictive maintenance approaches can be roughly classified as knowledge-based, data analytics and physics-based models. However, this classification does not provide maintainers clear guidance on how to select the most suitable approach for a specific case. This work, therefore, presents a selection method for this process. For that purpose, a list of selection criteria was established, and six predictive maintenance approaches were analysed. The proposed selection process is based on two main groups of criteria: the suitability criteria check the match with the desired ambition level of predictive maintenance, while the feasibility criteria identify whether this can be realized, given the labour, models and data available. Finally, three case studies are presented, demonstrating that the tool effectively guides to an optimal approach.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Ton2025b,

title = {Large Scale Asset Detection Within Railway Scene Point Cloud Data From Mobile Laser Scanning},

author = {Bram Ton and Rick Akster},

doi = {10.1109/ACCESS.2025.3590779},

issn = {2169-3536},

year  = {2025},

date = {2025-07-21},

journal = {IEEE Access},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Preparing for predictive maintenance: Employee perspectives on job demands and resources before and after its implementation},

author = {Bas van Oudenhoven; Evangelia Demerouti; Rob Basten; Philippe Van de Calseyde},

editor = {Elsevier},

url = {https://www.sciencedirect.com/science/article/pii/S0003687025000973},

doi = {10.1016/j.apergo.2025.104561},

year  = {2025},

date = {2025-06-05},

journal = {Applied Ergonomics},

volume = {129},

pages = {104561},

abstract = {Predictive maintenance (PdM) technologies promise to be valuable resources for maintenance employees. However, employees often view these technologies with skepticism. We conducted 35 interviews with maintenance employees at three different organizations to gauge their perspectives on PdM implementation. Using Job Demands-Resources theory and Carayon's Work System model as frameworks, we explored per work domain which demands (hindering work characteristics) and resources (facilitating work characteristics) employees experience under their current, non-PdM working procedures and what they expect to experience under PdM. The results show that employees currently view their decision-support system and organization as demands; however, they expect both to become resources under PdM. Nevertheless, employees express concerns that PdM may undermine their job autonomy and expertise, and they warn of the potential negative effects of false alarms generated by PdM technologies. Organizations can use these insights to (re)design PdM systems, thereby improving the acceptance and effectiveness of PdM technologies.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}