## 2 Materials and Methods
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One expert confirmed that maintenance intervals have so far often been based on empirical values and that flexible, data-driven adjustments hardly ever take place. Intelligent planning could help to schedule maintenance more in line with demand, reduce unplanned downtime and lower external maintenance costs. A more precise maintenance strategy would be of great benefit, especially for companies with critical machines whose failure would affect the entire production process. However, the introduction of such a model requires better integration into existing IT systems as well as a consistent and reliable database. Many companies lack a centralised overview of machine conditions and maintenance measures, which means that information on necessary maintenance is not consistently recorded or used. A digital dashboard could help here to enable maintenance teams to plan more clearly. ### Potential Further Developments and Optimisation Approaches

A combination of improved data integration, adaptive algorithms and AI-supported forecasting models can be used to further optimise the challenges of the area under consideration. A central further development could be the automated collection and evaluation of sensor data and the integration of machine conditions. This would expand the database and enable more precise planning. Machine learning could also be used to recognise patterns in machine loads and failures and improve maintenance decisions based on historical data and real-time information. This can further reduce unplanned downtime and enable more targeted deployment of maintenance measures. The current model works with a short-term tactical planning horizon and supports iterative replanning based on updated machine conditions. However, it does not include adaptive learning from historical performance data. Future extensions could explore the integration of reinforcement learning or rolling horizon strategies to enable continuous improvement and self-adaptive behaviour in dynamic production environments. Expert interviews show that maintenance decisions are often based on empirical values and do not always reflect the actual machine load. It would therefore make sense to integrate manual maintenance decisions directly into the model--for example, through user-defined entries or automatic reminders based on specific utilisation values. In addition, the integration of real-time information from production and the consideration of input from planners could further improve the quality of decisions. Heuristic or metaheuristic procedures could also reduce the calculation time for complex optimisation problems. In the long term, the model could conceivably be developed into a self-learning system that continuously optimises existing maintenance intervals and avoids inefficient cycles. Such data-driven optimisation would enable a more targeted and needs-based maintenance strategy. Another possible extension would be to consider machine-specific operating costs or energy consumption in the objective function or the constraints. This would enable more differentiated machine allocation, as the optimisation could then reconcile machine availability with cost efficiency or sustainability targets. Such extensions would enable the model to take into account not only technical feasibility but also economic and ecological criteria in future planning scenarios. ## 5 Conclusions

The presented model offers an optimised approach to integrating production planning and maintenance scheduling, outperforming conventional methods. By proactively incorporating preventive maintenance, it reduces machine downtime and enhances operational efficiency. The results of this study confirm that this planning approach improves both machine availability and adherence to delivery deadlines, as demonstrated in Table 3. By scheduling maintenance tasks based on machine wear and available idle times, the model effectively minimises unplanned machine failures.