**5. Conclusions**

Due to stiff regulations and a large operational complexity in multi-product, batchoperated sites, pharmaceutical companies sometimes operate in suboptimal conditions. Optimizing such pharmaceutical production processes via computer simulations not only appears to be practical but is also highly recommendable, even for relatively small companies. This paper demonstrates that discrete-event simulations of pharmaceutical productions (i) are feasible with FlexSim, (ii) can be conducted by non-computer experts, and iii) may significantly improve the production performance. Modeling of the entire production process does mean some effort at first. However, by considering the entire process, this model can be applied with more flexibility compared to other bottleneck identification methods focusing only on the technical equipment. In this case study, the entire process simulation was conducted by one person and it was possible to reduce the campaign duration by 50% for both products. If more resources were available, the benefits would likely improve and be determined more quickly. From a user perspective, more easily applicable tools and possibilities to effortlessly standardize modeling are desirable, especially for data acquisition and model verification. However, this process is only compelling for software developers if the demand is high enough. A widespread use of discrete-event simulations in pharmaceutical companies may, therefore, potentiate the present possibilities of such simulations.

**Supplementary Materials:** The following are available online at https://www.mdpi.com/article/10 .3390/pr9010067/s1, Table S1: Listing of all integrated process steps with the best fitting statistical distributions according to ExpertFit; Table S2: Listing of additional work steps that are product independent.

**Author Contributions:** Conceptualization, S.H., F.S., and C.-M.L.; methodology, S.H., N.S., T.M.B., F.S., and C.-M.L.; software, S.H.; validation, S.H.; formal analysis, S.H.; investigation, S.H.; resources, T.M.B. and T.R.; data curation, S.H.; writing—original draft preparation, S.H.; writing—review and editing, S.H., N.S., T.M.B., B.L., T.R., F.S., and C.-M.L.; visualization, S.H.; supervision, N.S., T.M.B., B.L., F.S., and C.-M.L.; All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Patent protection. Restrictions apply to the availability of these data. Data was obtained from protected company information. Data sharing is not applicable to this article. **Acknowledgments:** The authors are thankful to Ralph Gruber, Ingenieurbüro für Simulationsdienstleistung, Kirchlengern, Germany for providing support in the programming and handling of FlexSim. We acknowledge support by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) and Saarland University within the funding programm Open Access Publishing.

**Conflicts of Interest:** The authors declare no conflict of interest.
