## 6 Case Study
### Results of Reactive and Proactive Approach
In the proactive approach, however, parts only had to be transshipped in 100 out of 135 periods. The number of transshipped parts that are independent of demand is shown in Figure 8.  
In the first periods of the proactive approach, a large number of parts were transshipped proactively. Whenever no transshipment was necessary to satisfy demand and the internal warehouse was fully utilized, the incoming parts were transshipped to the external warehouse. As soon as a demand for assembly with parts from the external warehouse occurred, additional parts with a high assembly demand probability were transshipped from the incoming parts of the previous periods to the internal warehouse. Parts from the internal warehouse with a higher probability of being scrapped were transshipped from the internal to the external warehouse. For this reason, peaks of transshipped parts repeatedly occur in Figure 8 after periods in which transshipments were avoided. In the  
Figure 5: Warehouse utilization of the internal warehouse per period. Figure 6: Percentage of optimally allocated parts per period. reactive approach, parts are only transshipped regardless of demand if the minimum cost solution of the period cannot be realized due to capacity bottlenecks in a warehouse, as is the case from period 80 onwards (see Figure 8). Table 7 reports further key figures with average values per period for both approaches. The deviation between the reactive and proactive approach is also reported. Of particular interest is the reduction of the number of transshipped parts to satisfy demand. From the reduction in parts by an average of 77.5%, it can be interpreted that significantly more parts were already stored in the internal warehouse at the time of demand for assembly. At the same time, this means that in the proactive model, the waiting times until customers can pick up the parts have decreased for a large number of parts. The engineers and the assemblers have to wait for transshipments from the external to the \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{2}{c}{**Average per Period \(\boldsymbol{\mathcal{O}}\) (\(\boldsymbol{\alpha}\))**} & \multicolumn{2}{c}{**Deviation between the Reactive**} \\ \cline{2-4}  & **Reactive Approach** & **Proactive Approach** & **and Proactive Approach** \\ \hline Number of transshipments between the internal and external warehouse & 0.99 (0.12) & 0.74 (0.45) & \(-\)24.3\% \\ Number of transshipped parts to satisfy demand & 18.2 (13.3) & 4.1 (17.9) & \(-\)77.5\% \\ Number of transshipped parts independent of demand & 40.7 (104.3) & 282.9 (367.9) & 695.1\% \\ Utilization of the internal warehouse [\%] & 0.94 (0.07) & 0.99 (0.03) & 5.3\% \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Key figures with average values per period. Figure 8: Number of transshipped parts independent of demand per period. Figure 7: Number of transshipped parts to satisfy demand per period. internal warehouse in fewer cases and can therefore pick up the ordered parts more quickly than in the reactive approach. The average computational time per period for the reactive and proactive approach is shown in Table 8. The result shows that solving the reactive approach takes less time than solving the proactive approach. However, the solution time for the proactive approach is also appropriate for operational use.