## 6 Conclusions and Outlook  
As described in the introduction, industry is facing major challenges due to increasing energy prices. Energy flexibility measures can lead to cost reductions by adapting the load to the available energy. Faults in the often complex production system lead to high additional costs, so that risk treatment of production schedules is recommended.  
This approach can offer significant added value with manageable effort through the structured and reliable consideration and treatment of risks. Applied in industrial practice, this can encourage the willingness of industry to be energy-flexible. Furthermore, risk treatment leads to the better handling of faults and lower subsequent costs for the company. Thus, planning security for the operation of the energy system with a high proportion of volatile feed-in increases.  
To improve the approach in terms of ease of use and reliability, the following options have been identified:  
* The approach assumes that the final selection of measures is monitored by a human decision-maker. It is also assumed that a manual definition of risk preferences and classification of measures is desired. As a result, the approach cannot be carried out fully automatically. This would require AHP, as well as the selection of the utility function to be replaced by data-based processes and machine learning.
* The optimization considered uses constraints to describe the restrictions of the production system. In the case of more complex production systems, it may be necessary to use meta-heuristics such as genetic algorithms in order to be able to map all boundary conditions and interactions.
* After the reactive measures have been carried out, the effects should be put in a feedback loop in order to take the findings into account when developing reactive measures in risk treatment. The approach should be expanded to include this functionality. The results of the feedback loop can, i.e., be used for the calculation of Bayesian networks described in [PERSON] et al. (2016).
* The input and output data of the approach can be adapted in such a way that interfaces to the common systems in industrial companies can be implemented more easily. For example, order data for scheduling can be transferred from enterprise resource planning systems and load profiles of workstations from the energy management system, and the risk-treated production plans can be visualized by the manufacturing execution system. Thereby, the effort required for the application can be further reduced.  
Conceptualization: S.R.; methodology: S.R. and M.H.; software: M.H.; validation: [PERSON] and M.H.; formal analysis: S.R. and M.H.; investigation: [PERSON] and M.H.; resources: S.R. and M.H.; data curation: S.R. and M.H.; writing--original draft preparation: [PERSON] and M.H.; writing--review and editing: [PERSON] and G.R.; visualization: S.R. and M.H.; supervision: [PERSON] and G.R.; project administration: S.R.; funding acquisition: G.R. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the German Federal Ministry of Education and Research (BMBF), grant number 03 SFK3G1-2.  
The authors gratefully acknowledge the financial support of the Kopernikus-Project \"SynErgie\" from the Federal Ministry of Education and Research (BMBF), and the supervision of the project by the Projekttrager Julich (PTI) project management organization. Further thanks are due to [PERSON] and [PERSON] from Fraunhofer IGCV for their support with the use case of the foundry.  
The authors declare no conflict of interest.