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Identifying rail asset maintenance processes: a human-centric and sensemaking approach

Lookup NU author(s): Dr David GolightlyORCiD



This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


© 2018, The Author(s). Efficient asset maintenance is key for delivering services such as transport. Current rail maintenance processes have been mostly reactive with a recent shift towards exploring proactive modes. The introduction of new ubiquitous technologies and advanced data analytics facilitates the embedding of a ‘predict-and-prevent’ approach to managing assets. Successful, user-centred integration of such technology is still, however, a sparsely understood area. This study reports results from a set of interviews, based on critical decision method, with rail asset maintenance and management experts regarding current procedural aspects of asset management and maintenance. We analyse and present the results from a human-centric sensemaking timeline perspective. We found that within a complex socio-technical environment such as rail transport, asset maintenance processes apply not only just at local levels, but also at broader, strategic levels that involve different stakeholders and necessitate different levels of expertise. This is a particularly interesting aspect within maintenance that has not been discussed as of yet within a process-based and timeline-based models of asset maintenance. We argue that it is important to consider asset maintenance activities within both micro (local)- and macro (broader)-levels to ensure reliability and stability in transport services. We also propose that the traditionally distinct notions of individual, collaborative and artefact-based sensemaking are in fact all in evidence in this sensemaking context, and argue that a more holistic view of sensemaking is therefore appropriate by placing these results within an amended recognition-primed decision-making model.

Publication metadata

Author(s): Kefalidou G, Golightly D, Sharples S

Publication type: Article

Publication status: Published

Journal: Cognition, Technology and Work

Year: 2018

Volume: 20

Issue: 1

Pages: 73-92

Print publication date: 01/02/2018

Online publication date: 16/01/2018

Acceptance date: 30/11/2017

Date deposited: 09/07/2019

ISSN (print): 1435-5558

ISSN (electronic): 1435-5566

Publisher: Springer


DOI: 10.1007/s10111-017-0452-0


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