Journal
INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING
Volume 32, Issue 4-5, Pages 482-503Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1080/0951192X.2019.1571236
Keywords
Maintenance; prescriptive analytics; predictive analytics; production planning; CPPS; smart factory
Categories
Funding
- Horizon 2020 Framework Programme [739592]
- Austrian Research Promotion Agency (FFG) [843668]
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Cyber-physical production systems (CPPS), as an emerging Industry 4.0's technology, trigger a paradigm shift from descriptive to prescriptive maintenance. In particular, maintenance management approaches nowadays are more and more transformed to (semi-) automated knowledge-based decision support systems. This paper is intended to examine existing approaches and challenges towards rethinking maintenance in the context of Industry 4.0 and thus contributes to the literature of production management and planning, by introducing a novel prescriptive maintenance model (PriMa). PriMa is comprising of four layers (i.e. data management, predictive data analytic toolbox, recommender and decision support dashboard as well as an overarching layer for semantic-based learning and reasoning). The integrated approach of PriMa enhances two functional capabilities, namely i) efficiently processing large amount of multi-modal and heterogeneous data collected from multidimensional data sources and ii) effectively generating decision support measures and recommendations for improving and optimising forthcoming maintenance plans correlated with production planning and control (PPC) systems. An industry-oriented proof-of-concept study has been conducted to explore the feasibility of applying PriMa in real production systems by implementing a decision support solution and achieving a significant reduction of downtime. Finally, future research directions in this area are outlined.
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