Data-driven planning and prioritisation in maintenance: A case-study in the automotive industry

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dc.contributor.authorMagnusson, Johan
dc.contributor.authorSavolainen, Pontus
dc.contributor.departmentChalmers tekniska högskola / Institutionen för industri- och materialvetenskapsv
dc.contributor.departmentChalmers University of Technology / Department of Industrial and Materials Scienceen
dc.date.accessioned2019-07-05T12:03:28Z
dc.date.available2019-07-05T12:03:28Z
dc.date.issued2019
dc.description.abstractABSTRACT Developments in computerised systems and digitalisation are pushing industry into the next industrial revolution, Industrie 4.0. The developments have pushed the maintenance organisation at Volvo Cars Torslanda to evolve their maintenance practices within data-driven decisions in planning and prioritisation of maintenance, through collaborating in research projects. The result of the developments was a decision support tool that uses data to automatically in real-time identify bottlenecks in the production systems, with the aim to prioritise the bottlenecks to increase the productivity of the factory. The system was implemented in the body shop at Volvo Cars Torslanda, but the developments stopped there. The thesis picks up where the development ended in order to identify organisational factors that are constraining the use of data-driven decisions in planning and prioritisation of maintenance, both considering the earlier developments but also on a broader scale. Further, to identify if data quality is sufficient for data-driven decisions. Through a questionnaire and interview-study it was identified that low data quality and insufficient support systems affect the trust to and use of data-driven decisions, which are constraining factors for transcending into a more data-driven organisation. However, it was identified that there is a drive for wanting to become more data-driven and a need for better prioritisations. The assessment of data quality performed through interviews with experts and an analysis of datasets, concluded in that the general data quality regarding context independent problems were sufficient but that there is improvement for context dependable issues that are only identifiable by the staff working in the daily operations. The overall result is that there is a need to improve the data quality in the support systems and to educate users how to fully exploit the systems, in order for Volvo Cars Torslanda to transform into being a data-driven maintenance organisation. If the industry and Volvo Cars Torslanda overcome the identified constraints there is a potential to be succeed in the transformation becoming more data-driven within maintenance, thereby taking the step into the future of maintenance.
dc.identifier.urihttps://hdl.handle.net/20.500.12380/257445
dc.language.isoeng
dc.setspec.uppsokTechnology
dc.subjectProduktion
dc.subjectInnovation och entreprenörskap (nyttiggörande)
dc.subjectMaskinteknik
dc.subjectProduction
dc.subjectInnovation & Entrepreneurship
dc.subjectMechanical Engineering
dc.titleData-driven planning and prioritisation in maintenance: A case-study in the automotive industry
dc.type.degreeExamensarbete för masterexamensv
dc.type.degreeMaster Thesisen
dc.type.uppsokH
local.programmeProduction engineering (MPPEN), MSc
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