## 2 Materials and Methods
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In particular, the additional stress factor incorporates empirical experience from practitioners and accounts for machine-specific influences that are not captured by operating time or mechanical load alone. The welding machine has the highest overall load. This is due to intensive use and high mechanical stress levels. This may indicate a higher proportion of complex processing steps or longer processing times per job. The visualisation provides valuable insights for future maintenance planning, as an uneven distribution of machine loads can lead to accelerated wear. The results highlight the importance of a dynamic maintenance model that proactively detects heavily loaded machines and schedules preventive maintenance in order to reduce unplanned downtime. If multiple identical machines are available, this analysis enables a direct comparison of their workloads. For example, it is possible to determine whether one machine is handling a disproportionately high number of demanding tasks while another remains underutilised. This information allows better load balancing, reducing bottlenecks and extending machine service life by distributing wear more evenly. ## 4 Discussion

In this study, a model was developed that integrates production and maintenance planning to increase efficiency in dynamic manufacturing environments such as single- and small-batch production. Its results show that the flexible use of free time slots for preventive maintenance can significantly reduce unplanned downtime. On the whole, this is in line with previous research that has emphasised the importance of adaptive maintenance strategies. Several studies have demonstrated that integrating predictive and preventive maintenance strategies into production planning can significantly enhance machine availability and reduce unexpected downtime [10; 11; 21]. [PERSON] et al. [21] highlight the benefits of predictive maintenance integration, while [PERSON] et al. [11] emphasise multi-objective optimisation approaches for scheduling. [PERSON] et al. [10] propose a heuristic method to improve scheduling efficiency by dynamically integrating maintenance activities. These findings support the notion that adaptive strategies, such as those implemented in our model, are crucial for the improvement of overall production efficiency. Thus, the claim regarding the significance of adaptive maintenance strategies is well supported by a range of studies addressing various aspects of predictive and preventive maintenance integration in production planning. ### Advantages of the Model

The analysis of the developed model shows both its strengths and potential areas for improvement. Its ability to dynamically link production and maintenance planning and to react flexibly to changes is particularly impressive. One advantage of the model is its ability to increase machine availability by allowing targeted preventive maintenance and reducing unplanned downtimes. This helps to increase the overall productivity of production. The model also enables a high degree of flexibility in the event of short-term changes by reacting in real time to unexpected order fluctuations, for example. As a result, the production process remains stable and flexible. Figure 4: Total machine load over the planning period. The load index is calculated as a composite metric based on operational load, mechanical stress and additional stress factors (i.e., Load \(\times\) Time \(\times\) Stress). This transparency helps with resource-efficient planning and ensures that available machines are utilised to their full potential. Another positive aspect is that the model ensures better adherence to delivery deadlines by prioritising critical production orders and completing them on time. The model also provides a detailed overview of machine capacities, wear and tear statuses and free time slots. ### Limitations of the Model

At the same time, there are aspects that require further development for even broader application.