Maintenance management decision making

Pintelon, Liliane; Gelders, Ludo

doi:10.1016/0377-2217(92)90062-e

Cited by 214 publications

(125 citation statements)

References 26 publications

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“…Pintelon and Gelders 1992) or on developing maintenance management toolkits that support advanced computations based on asset failure occurrences, optimising actions and in-house policies (e.g. Pintelon and Van Wassenhove (1990) or on maintenance performance analyses (e.g.…”

Section: Maintenance Models and Frameworkmentioning

confidence: 99%

“…Jones and Sharp 2007;Pintelon and Gelders 1992;Pintelon and Van Wassenhove 1990;Vanneste and van Wassenhove 1995;Hassanain et al 2001;Braaksma et al 2013;Zoeteman 2006), there is limited research that has unpacked notions of sensemaking that aim to enhance the way that asset professionals understand and interact with asset data (Sergeeva 2014;Mardiasmo et al 2008;Schippera and Gerritsb 2017). Approaching asset maintenance with such a lens can provide a further insight as to human-centric needs and requirements that emerge within asset maintenance.…”

Section: Research Scopementioning

confidence: 99%

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

2018

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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.

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Section: Maintenance Models and Frameworkmentioning

confidence: 99%

Section: Research Scopementioning

confidence: 99%

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

2018

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show abstract

“…Therefore, it is clear that policy setting and the understanding of its efficiency and effectiveness continues to be fine-tuned as any other management science. We advocate the reader, particularily interested in the underlying principles and type of models, to review McCall (1965), Geraerds (1972), Valdez-Flores and Feldman (1989), Cho and Parlar (1991), Pintelon and Gelders (1992), Dekker (1996), Dekker and Scarf (1998) and Wang (2002) for a full overview on the state-of-the-art literature.…”

Section: Cfr = Constant Failure Rate Ifr=increasing Failure Ratementioning

confidence: 99%

“…The authors note that even though Q&D charts lack the holistic view required for well-conceived and sophisticated maintenance concepts, they are still widely used in practice on specific situations thanks to their simplicity. Examples are reported in Pintelon et al (2000) and Waeyenbergh and Pintelon (2002).…”

Section: Maintenance Conceptsmentioning

confidence: 99%