## 5 Implementation
### Phase 3--Selected Methodology Application
#### 5.3.1 Data Preparation and Modeling/Analyze/Evaluation
Sample size determination is a way of selecting the number of observations to comprise in a sample. It is an important feature of any study in which the objective is to make implications about the population from a sample [85]. The sample size for this study was selected as 330 because the default sample size used in medical device industries for capability studies is 59 at the minimum [86]. Cpk and Ppk values were calculated for each DIM and it was observed that the data were not normal (\(p<0.005\)) for any DIM, but Cpk and Ppk values were greater than 1.67 for all except 18 DIMs (detailed in Table 5). The Anderson-Darling test was used to check if the data fit normal distribution because it is sensitive to data variability [87]. The test was based on null (H\({}_{0}\)) and an alternative hypothesis (H\({}_{1}\)), where [88],  
**Hypothesis 0** (H\({}_{0}\)): _the data follows the normal distribution._  
**Hypothesis 1** (H\({}_{1}\)): _the data does not follow the normal distribution._  
\(\alpha\): level of significance (normally set at 5%, \(\alpha=0.05\)). If \(p<\alpha\), then the null hypothesis is rejected and the alternative hypothesis is true i.e., the data does not follow the normal distribution. Figure 11: Combined methodology—Phase 2. Thorough analysis of the capability index showed that only 11 DIMs have low capability (capability index \(\leq\) 1), which accounts for 31.4% of the total 35 DIMs. Only seven DIMs showed good capability (1 < capability index \(\leq\) 1.67). The DIMs with low capability were analyzed on a case-by-case basis and the following conclusions were derived after discussion with the process engineers: DIM 2 and 10 MAX had an outlier, DIM 13 MAX had very narrow limits which made it a critical dimension, DIM 3, 4, 9, and B (RR-TT) might have had the wrong machine setup and plenty of variation was observed for DIM 8, A (M-N), A (Q-R), and A (S-T) due to unknown reasons. The histograms for the 11 DIMs with low capabilities are shown in Figure 14.  
Since the number of parts machined for each size were not consistent, the analysis was difficult, and more consistent data were required for proper analysis. Traditional Six Sigma would not be able to incorporate a large unfiltered dataset into its framework and  
Figure 14: Histogram for low capability DIMs. Figure 13: Plot of capability index for all DIMs. integrating CRISP-DM methodology to prepare the data from digitization for analysis is the novel aspect of this study. The CRISP-DM data preparation step was used again to prepare the data for evaluating whether the different sizes of the implants in the batches was a factor contributing to low capability index. It was assumed that a smaller dataset with reduced variables will provide better consistency. Therefore, the data were filtered out for each size using the data for the DIM with lowest index (DIM 13 Max). It was found that the data were not normal for only four instances (\(p<0.005\)) as shown in Table 6. A plot of the capability index for all the DIMs based on sizes is shown in \[\text{Cp}=\frac{\text{Tolerance band}}{6\sigma} \tag{1}\]0 All capability index values were very high, except for the instance when an outlier was detected. The data were then filtered out for the DIM with lowest Ppk (DIM 13 Max) based on both sizes and LengthA (tool adjustment) changes. LengthA is the measurement of the tip of the tool to the roof of the toolxture. As the tool wore down during operation, LengthA shortened and the associated CNC tool required an adjustment in the \(y\)-axis. The changes