## 8 Conclusions and Outlook  
Strategic network design poses several challenges, such as a large solution space comprising many highly uncertain design parameters, making the related decisions challenging without decision support. Data farming offers a potential solution, as synthetic data can be generated by running multiple simulation experiments spanning the solution space and then analyzed using data mining techniques. However, the foundation of data farming is an experiment design that spans the solution space. Therefore, solution space management is necessary to support the generation of alternative network solutions to be simulated and to create the experiment design. This paper presents a structured solution management approach to support data-farming-based strategic network design. The presented approach transfers concepts from product development to the context of strategic network design. Therefore, two main steps are required. First, the solution space structuring step identifies fixed and variable degrees of freedom and builds sub-solution spaces that are individually addressed in the second step. The second step of solution space planning and control builds alternatives for each sub-solution space and combines all sub-solution space alternatives into a comprehensive experiment design that is processed into input data sets for simulation models. A case from the automotive industry demonstrates the industrial applicability and validity of the solution space approach and leads to several implications. It has been shown that by \begin{table}
\begin{tabular}{c c} \hline
**Requirements** & **Fulfillment** \\ \hline _General requirements_ & \\ \hline _G-RQ1_ & Focus on operational decisions within the strategic network design & \\ _G-RQ2_ & Focus on value-stream-relevant parameters & \\ \hline _Methodology-based requirements_ & \\ \hline _M-RQ1_ & Comprehensive representation of the solution space & \\ _M-RQ2_ & Identification of alternative solutions & \\ \hline _Application-based requirements_ & \\ \hline _A-RQ1_ & Possibility to customize the approach to a problem-specific solution space & \\ _A-RQ2_ & Structured approach and understandable, transparent solution path & \\ \hline _Production-network-operations-based requirements_ & \\ \hline _O-RQ1_ & Material flow maintenance across customer-supplier-relationships & \\ _O-RQ2_ & Fixed demand volume for capacity planning & \\ _O-RQ3_ & Complete demand fulfillment of customers on a weekly basis & \\ \hline \end{tabular}
\end{table} 
Table 4: Summary of the fulfillment of requirements. applying the proposed approach, it is possible to generate a set of alternative network solutions spanning the solution space. Moreover, this leads to alternative network solutions that can be processed into input data sets for simulation models. In addition to the application results, the solution space management approach also needs to be validated as a part of the data-farming-based planning approach by [PERSON] et al. [46], which will be the subject of future publications. Several points have to be addressed in future research activities. First, applying solution space management to either a tactical or operational planning horizon could provide beneficial insights into the solution space structure of these planning problems. Second, the solution space structure could be used as an action space for a reinforcement learning algorithm to find an optimal alternative network solution with respect to the reward function. A pre-structured action space could influence the optimization time of the reinforcement algorithm. Third, until now, the application steps and the entire solution space management approach have been carried out manually. To integrate the approach into an industrial-standard network planning process, continuous digital support is required. Methodology: S.K. ; Writing original draft: S.K. ; Writing--Review and Editing: S.K., M.W., C.S. and M.F.Z. All authors have read and agreed to the published version of the manuscript. This research received no external funding.