With emerging modern technology, production machines become more complex, and these systems' maintenance costs increase (Jardine et al. 2006). Therefore, more and more companies decide to enact a Predictive Maintenance (PdM) strategy that leads to reduced costs by only maintaining systems when it is really necessary (Ladj et al. 2017). With PdM, breakdowns can be predicted by assessing the condition of a degrading system and proposing maintenance interventions at the right time (Selcuk 2017).

PdM is not a new concept anymore, and many companies have already implemented solutions in their production plants (Mulders and Haarman 2018). However, to gain the most value from Predictive Maintenance, more mature implementations must strive for maintenance optimization, and cognitive intelligence where not only failures are predicted, but production planning is also jointly optimized (Wagner and Hellingrath 2019).

• How is production planning and control 'traditionally' conducted?
• What are the benefits and barriers of implementing predictive maintenance for production planning and control?
• How is production planning and control changed by predictive maintenance (in practice)?

This thesis' goals should be attained by investigating current literature and conducting expert interviews at companies that have already implemented PdM solutions in a production environment (e.g., consultancies). You should be fluent in German.