# Evaluation of Manual Skill Degradation Due to Automation in Apparel Manufacturing
## 3 Results
For the RTG, there was no significant difference (\(t\)(19) = 1.73, \(p\) = 0.10) in average dexterity time from the initial training stage(M = 31.03, SD = 1.19) to the skill retrieval assessment (M = 30.5, SD = 1.02) phase. For the AG, there was a significant (\(t\)(19) = \(-\)6.32, \(p\) \(<\) 0.001) increase in average dexterity time from the initial training stage(M = 30.6, SD = 1.16) to the skill retrieval assessment(M = 32.01, SD = 1.29) phase. Table 4 shows the effect sizes of the automation treatment on workers' performance after initial training and after the retention period. The results showed a large effect size of automation on the manual skills of sewing operators. The positive or negative signs of effect size only show the direction of the effect. Usually, positive effect size means that the performance after the retention interval improved, and the negative sign shows the degraded performance. In the present study, in the context of the ASCT and average dexterity time, negative value effect size does not mean degraded performance; it shows the improvement of sewing operators in taking less total time to complete the task. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{1}{c}{\begin{tabular}{c} **Performance after Initial Training** \\ **Mean, (SD)** \\ \end{tabular} } & \multicolumn{1}{c}{\begin{tabular}{c} **Performance at Retrieval Assessment** \\ **Mean, (SD)** \\ \end{tabular} } & \multicolumn{1}{c}{\
\begin{tabular}{c} **Correlation** \\ **’\(\mathbf{r}^{\prime}\)** \\ \end{tabular} } & \multicolumn{1}{c}{
\begin{tabular}{c} **Effect Size** \\ **Cohen’s d** \\ \end{tabular} } \\ \hline \multicolumn{4}{c}{**1. ASCT (min)**} \\ \hline MMG & 2.43 (0.07) & 1.86 (0.08) & 0.711 & \(-\)9.974 \\ \hline RTG & 2.45 (0.06) & 2.36 (1.06) & 0.18 & \(-\)0.094 \\ \hline AG & 2.42 (0.07) & 2.71 (0.12) & 0.365 & 2.62 \\ \hline \multicolumn{4}{c}{**2. Production/day (units)**} \\ \hline MMG & 153.6 (9.13) & 188 (12.5) & \(-\)0.037 & 2.182 \\ \hline RTG & 152 (8.1) & 150 (10.7) & 0.35 & \(-\)0.185 \\ \hline AG & 153 (6.6) & 125 (9.35) & 0.086 & \(-\)3.07 \\ \hline \multicolumn{4}{c}{**2. RFT (\%)**} \\ \hline MMG & 87.3\% (2\%) & 95.9\% (3\%) & 0.40 & 3.079 \\ \hline RTG & 89.3\% (2.7\%) & 86.5\% (4.6\%) & 0.018 & \(-\)0.53 \\ \hline AG & 87.4\% (2.2\%) & 75.8\% (6.4\%) & 0.025 & \(-\)1.736 \\ \hline \multicolumn{4}{c}{**2. Average Dexterity score (sec)**} \\ \hline MMG & 31.6 (1.09) & 31.2 (1.29) & 0.88 & \(-\)0.684 \\ \hline RTG & 31.03 (1.19) & 30.5 (1.02) & 0.278 & \(-\)0.398 \\ \hline AG & 30.6 (1.16) & 32.01 (1.29) & 0.691 & 1.462 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Effect size among groups using the paired samples \(t\)-test. In our third hypothesis, we supposed that refresher training after suitable intervals helps in reducing skill loss. The results from paired sample \(t\)-test supported our hypothesis. Therefore, by introducing suitable refresher drills for the workers shows the minor impact of automation exposure on the RTG performance after the retention interval. Figure 4a-d illustrates the comparison of performance after initial training and after the retention period. The average capacity percent of the MMG increased from 60.6% to 79.4%, increasing learning by almost twenty percent in six weeks. However, this percentage increase looks small as compared to the progress in the initial six weeks. The answer lies in that the learning curve of repetitive tasks flattens after reaching an average working performance. Capacity percent was calculated by dividing the basic minute (BM) of the task with the ASCT. See Equation (3)  
\[\text{BM}=\text{ASCT}*\text{OR} \tag{2}\]  
\[\text{Cap}\ \%=\text{(BM)}/\text{ASCT} \tag{3}\]  
Basic time was calculated by multiplying the ASCT with the operator's rating (OR) as shown in Equation (2).