Predictive Maintenance: Adoption Barriers and Perceived Value

Sammanfattning

Predictive maintenance is increasingly presented as an important application of artificialintelligence in industrial production, with the potential to reduce unplanned downtime,improve maintenance planning, and strengthen operational efficiency. However, despite itstechnological potential, predictive maintenance has not become an established standardacross goods-producing firms. This thesis examines how those firms perceive the value ofpredictive maintenance and what factors influence its adoption.The study is based on a qualitative research design, using semi-structured interviews andwritten responses from nine respondents with roles related to maintenance, operations,condition monitoring, and industrial technology. The empirical material was analyzedthrough reflexive thematic analysis in order to identify recurring patterns in how predictivemaintenance is understood, evaluated, and discussed within organizational contexts.The findings show that predictive maintenance is mainly valued in production-criticalsettings where downtime is costly and early intervention can improve operational stability.However, this perceived value is highly context-dependent and does not automatically lead toadoption. Several interconnected barriers influence implementation, including dataavailability, uncertainty regarding return on investment, limited organizational slack, andchallenges related to trust, interpretation, and human expertise. The findings further indicatethat predictive maintenance requires coordination between maintenance, production, IT, andmanagement, as adoption involves changes in routines, responsibilities, and decision-makingprocesses.The thesis concludes that predictive maintenance adoption should not be interpreted as apurely technological decision or as a direct consequence of perceived value. Instead, adoptionis best understood as an alignment problem. Successful implementation depends on theinteraction between perceived value, organizational capabilities, and socio-technicalintegration. This perspective helps explain why predictive maintenance, despite itsrecognized potential, often remains limited to evaluation.