Abstract
Prescriptive maintenance planning is an essential enabler of smart and highly flexible production processes. Due to increasing complexity, traditional maintenance strategies lack in fulfilling present-day production requirements. This paper proposes a novel procedural approach for prescriptive maintenance planning in manufacturing companies. Multivariate data analysis and simulation tools are utilized to analyse historical data (product quality data, machine failure data and production program data). Based on identified data correlations and incoming real-time machine data, system failures are predicted and prescriptive maintenance measures are proposed. Results from real implementations in the automotive manufacturing industry are presented to demonstrate the effectiveness of the proposed approach.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
