## 5 Decision Support Workflow
### Rough-Cut Sensitivity Analysis  
After the successful validation, the goal of the first series of simulation experiments was to analyze the sensitivity of CDD performance to the potential changes in the above key factors and select those ones where the chances of improvement are relatively high at the lowest possible costs. Hence, a rough-cut sensitivity analysis was performed by running the DES model of the mini-factory in a series of controlled experiments. Scenarios were systematically generated by tweaking the key external and internal factors. Specifically, we intended to find out which structural/statical or behavioral/dynamic factors have the highest and lowest impact on the output of the system determined by the confirmed delivery date performance, the makespan (MS) of production, as well as by the average net lead time (LT) of products. Compared to a baseline solution reflecting the actual situation, the following factors were changed between limits suggested by experienced planners (see Figure 4 and Table 1, where the baseline scenario is represented by the first row):  
* Operators' availability varied between 60% and 100%. * Machines' availability varied between 85% and 100%. * Batch sizes were determined as originally requested in the order (lot-for-lot), 50% of order, and 25% of order. * Sequencing was simulated with three different rules: (1) basic or original, (2) random, based on diameters, and (3) random, based on product family. * Buffer sizes were set to 200%, 100%, 50%, 25%, 1 piece, and unlimited buffer sizes. * Finally, daily demand variant patterns were generated from historical data. Figure 8: Distribution of the order lead times for different randomized simulation runs. By performing factorial experiments, i.e., systematically tweaking these parameters, we were interested in detecting their influence on the lead time of individual orders. Compared to the baseline case, ceteris paribus, always a single factor was changed, and particular instances were run ten times so as to account for the effect of random decisions within the model. Altogether, the sum of the net computational times took almost 24 h.  
As a result of this rough-cut sensitivity analysis, we obtained a so-called tornado diagram which sorts the factors according to their maximal and minimal deviation from the baseline scenario (see Figure 9). \begin{table}
\begin{tabular}{c c c c c c} \hline
**Operators\({}^{\star}\)** & \multicolumn{3}{c}{**Machine Availability**} & **Lot Size** & **Sequencing** & **Buffer Size** \\ \hline
100\% & all & 100\% & basic & basic & 100\% \\
90\% & DM2239 & 85\% & 50\% & random & 200\% \\
80\% & DM2242 & 85\% & 20\% & random & family & 50\% \\
60\% & DM2243 & 85\% & & & 25\% \\  & HELLER & 85\% & & & 1 piece \\  & **TRAINB1600** & 85\% & & & \\  & **TRAINB1657** & 85\% & & & \\  & **KHZ PRESS** & 85\% & & & \\  & **KHZ TEST** & 85\% & & & \\  & **KPZ PRESS** & 85\% & & & \\  & **KPZ TEST** & 85\% & & & \\ \hline \end{tabular}
\end{table} 
Table 1: Factors systematically changed in the rough-cut sensitivity analysis (see also Figure 4 for color coding). Figure 9: Result of the rough-cut sensitivity analysis: the tornado diagram. As the top of Figure 9 shows, the sizes of buffers between the operations have the highest influence on the lead time, and on the delivery performance, after all. The second highest influence comes from the availability of milling machines. Next, the sequencing and lot-sizing rules matter. Considerable (negative) deviations are shown in the bottom of Figure 9. Here, again, as in the top of Figure 9, buffer sizes, sequencing, and lot-sizing rules matter. However, machine capacities cannot be increased quickly and at relatively low expenses. Specifically, this option of improvement was not considered due to two reasons: (1) the urgently needed capacity cannot be assured in an urgent case, only at the price of overcapacities, and (2) additional investments would render this option significantly more expensive than any other ones.