Data-Driven Decision Support for Maintenance Prioritisation - Connecting Maintenance to Productivity
Doctoral thesis, 2018
data-driven maintenance planning
Chalmers, Industrial and Materials Science, Production Systems
Machine criticality based maintenance prioritization: Identifying productivity improvement potential
International Journal of Productivity and Performance Management,; Vol. 67(2018)p. 654-672
Identification of maintenance improvement potential using OEE assessment
International Journal of Productivity and Performance Management,; Vol. 66(2017)p. 126-143
Buffer Utilization Based Scheduling of Maintenance Activities by a Shifting Priority Approach – A Simulation Study
Proceedings of the 2016 Winter Simulation Conference, December 11 - 14, 2016,; (2016)p. 2797-2808
Paper in proceeding
Real-Time data-driven average active period method for bottleneck detection
International Journal of Design and Nature and Ecodynamics,; Vol. 11(2016)p. 428-437
The transformation should enable maintenance organisations to move from focusing on a component-level to achieving a systems perspective for solving maintenance problems. This is needed mainly in maintenance decision making in order to make fact-based decisions. This thesis contributes towards this transformation through studying the maintenance prioritisation decisions and their decision support systems. The research was conducted with the help of five empirical research studies using theoretical inputs, survey, interviews, experiments and case studies. The thesis contributes to,
(i) Identifying gaps between maintenance prioritisation practice and research.
(ii) Assessing real-time data for decision support and assessing maintenance prioritisation decisions for effectiveness.
(iii) Developing a data-driven machine criticality assessment framework to guide prioritisation decisions.
Furthermore, the framework also provides guidelines on how to approach the data needed, the analysis required and the type of decisions that can be made. An example from one of the studies showed a five percent increase in productivity when maintenance was prioritised based on machine criticality. The results obtained in the thesis will help maintenance organisations to transform from having a narrow focus (solving machine-level maintenance problems) to achieve a factory-focus (systems perspective), thus supporting an increase in productivity. By connecting maintenance to productivity, maintenance organisations can help manufacturing companies compete in global production.
Streamlined Modeling and Decision Support for Fact-based Production Development (StreaMod)
VINNOVA (2013-04726), 2013-12-02 -- 2016-12-01.
DAIMP - Data Analytics in Maintenance Planning
VINNOVA (2015-06887), 2016-03-01 -- 2019-02-28.
Production Engineering, Human Work Science and Ergonomics
Reliability and Maintenance
Areas of Advance
Doktorsavhandlingar vid Chalmers tekniska högskola. Ny serie: 4456
Virtual Development Laboratory, Chalmers tvärgata 4C
Opponent: Professor Anna Syberfeldt, University of Skövde.