## 6 Model- and Data-Driven Focused Analysis
### Analyzing the Impact of Dynamic Changes  
To determine the proper clustering to be used later by planners, further simulation experiments were run on the baseline dataset and the centroids by applying the combination of different sequencing and lot-sizing rules. Settings of the simulation model were determined by different lot-sizing (grouping and splitting) and sequencing rules. These rules were suggested by planners of the factory and by the literature [5; 13; 26; 27]. The following options were investigated:  
* Ordering was performed by: (1) A heuristics variant called the \"base\" rule, which was applied by our production planners in their everyday practice. Alternatively, (2) the \"max\" rule sorted products descending by their order quantities, and (3) an \"oscillation\" rule first took the highest and then the lowest order quantities, in an iterative manner. * Sequencing was also determined by grouping rules which considered the products' secondary attributes (such as diameter and length): (1) either products with the same diameters were put one after the other, (2) or diameters were not taken into consideration. * Lot sizes and the sequence were also determined by lot-splitting rules: (1) The \"local\" rule split orders into product-specific lots. The sequencing of these split orders was not modified. (2) Alternatively, the \"cycle\" rule split orders at a predefined lot size value, and the first part of the order stayed in the sequence, while the second part was put at the end of the sequence. This part, if longer than the predefined lot size, was repeatedly cut by the \"cycle\" rule. For example, consider three products, A, B, and C, with order quantities of 80, 120, and 40, respectively. Following the \"base\" rule, let the original sequence be A, B, and C. Assume that the predefined lot size is 40 for each product. Then, the \"local\" rule produces a sequence of A40, A40, B40, B40, and C40, by keeping the original ordering of products intact. Instead, the \"cycle\" rule produces A40, B40, C40, A40, B40, and B40. In production, this would incur more setups, however, at the same time, it would open more opportunities for parallel processing. The combination of the above rules resulted in 10 different sequencing methods, as presented in Table 2. The name of the actual rule is combined from its components, e.g., \"_base_False_cycle_\" stands for the basic heuristics, followed by no grouping and cyclic lot-splitting, while \"_oscillation_True_local_\" stands for an oscillating ordering, followed by grouping and local lot-splitting. The original question was how well a centroid represented all other members of its cluster. Degree of fit was measured in terms of a critical KPI, namely the makespan. For each cluster member in the baseline dataset, under the same parameter settings, the value of its makespan was compared to the makespan of its representative (i.e., centroid). In particular, the performance metric for a given rule and clustering is the percentage of daily input mixes for which the application of the rule resulted in a makespan that remained below 105% of the best, minimal makespan for that specific input mix. The comparison of the different rules' performance for each cluster is summarized in Figure 13. For each rule, one can compare its performance based on different clustering and on the baseline case (without clustering, pink column in each set). One can also qualitatively see that the performances of the same rules over the C5 and C10 clusters are rather different, while, as expected, fine-grained clusters such as C75 and C100 produce almost the same performance values as the original dataset. C25 or C50 suggest a compromise of representative power and compactness. Performance differences between various clusters can also be assessed quantitatively, as shown in Table 3. Here, the MAE.S column shows, in terms of the Mean Absolute Error (MAE), the performance deviation between the baseline dataset and the other centroids with applying the best sequencing rules.