Organizational dimensions of e-maintenance: a multi-contextual perspective

Abstract

A key objective for e-maintenance efforts is to align maintenance processes with business-and operational processes in order to reach organizational objectives. In the context of the process-and manufacturing industry a key objective for firms is to avoid downtime and to make sure all critical production equipment is up and running. To this end, e-maintenance has become increasingly important for the process-and manufacturing industry. Successful e-maintenance is realized by the organizational use of advanced information technology-solutions which aims at moving maintenance work from being primarily reactive (e.g. to react and respond to equipment breakdowns) to predictive (e.g. to predict when equipment are in need of maintenance before it breaks down). Building on a collaborative project with industrial organizations in the pulp and paper and the mining industry this paper explores organizational opportunities and challenges associated with the design and implementation of IT-based services for remote diagnostics of industrial equipment. We observe opportunities and challenges related to organizational innovation and learning. The paper introduces a multi-contextual perspective to better understand the opportunities and challenges associated with organizational learning and innovation. We argue that in order for e-maintenance services to be successful it must not only build on leading-edge technological solutions but also be built on an explicit model for how the maintenance work is organized and how e-maintenance efforts are aligned with overall organizational objectives.

A cross-sector analysis of human and organisational factors in the deployment of data-driven predictive maintenance

“…Predictive maintenance still requires the involvement of people to interpret data, and to discuss implications to local maintenance operators in position to effect repairs (Jonsson et al 2010) or to triangulate information and maintenance strategies (Kefalidou et al 2015). Context is vital to the interpretation of data either because assets within a fleet have their own structural idiosyncrasies (Houghton and Patel 2015;Kefalidou et al 2015), or because similar assets are used in different ways and face different demands (Lee et al 2014).…”

“…Context is vital to the interpretation of data either because assets within a fleet have their own structural idiosyncrasies (Houghton and Patel 2015;Kefalidou et al 2015), or because similar assets are used in different ways and face different demands (Lee et al 2014). Therefore, despite the potential for remote monitoring to reduce the numbers of on-site maintenance teams, a reduced local presence is still vital, not only to carry out repairs but also because data sensing is not complete (Jonsson et al 2010). It must also be recognised that human interpretation of data regarding an asset state is one of the sources of uncertainty that must be anticipated in the usage of prognostic condition monitoring, another being the variability in human performance of the maintenance task itself (McDonnell et al 2015).…”

“…Equally important is the challenge of ensuring technology is deployed to best effect-that is, introduced and used in manner that will deliver actionable decisions providing benefit to asset owners, operators and maintainers. This is a combination of understanding how technology must fit with processes, and conversely, how processes and strategy will need to adapt in order to benefit from the technology (Jonsson et al 2010).…”

“…Also, work typically focuses on one domain e.g. rail (Dadashi et al 2014), urban transit (Kefalidou et al 2015;Houghton and Patel 2015) or vehicle engines (Jonsson et al 2010). The ambition in this paper is to look across a number of sectors to inform human factors models (e.g.…”

“…oil temperatures inside a running engine; data on gearboxes on an offshore windfarm) (Jonsson et al 2010), offering a great advantage in understanding asset behaviour over time. Sensors can be placed at multiple points on larger assets (e.g.…”