## 2 Methodology
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Model** & **January** & **February** & **March** & **April** & **May** & **June** \\ \hline
1 DNL & 58 & 60 & 57 & 63 & 66 & 80 \\
2 DNV & 53 & 48 & 61 & 45 & 56 & 50 \\
3 DND & 62 & 64 & 56 & 73 & 76 & 69 \\
4 DNK & 39 & 46 & 44 & 51 & 37 & 43 \\
5 DNS & 49 & 36 & 54 & 40 & 47 & 38 \\ \hline \hline \multicolumn{8}{c}{**B. Average production per unit in meters per month**} \\ \multicolumn{8}{c}{for each model of die and nozzle} \\ \hline
**Model** & **January** & **February** & **March** & **April** & **May** & **June** \\ \hline
1 DNL & 719,368 & 699,942 & 769,032 & 783,215 & 694,142 & 793,167 \\
2 DNV & 879,598 & 759,975 & 869,432 & 779,216 & 866,774 & 894,302 \\
3 DND & 689,050 & 659,920 & 679,106 & 746,627 & 653,511 & 696,131 \\
4 DNK & 798,534 & 789,231 & 898,535 & 899,477 & 775,281 & 892,387 \\
5 DNS & 899,506 & 799,926 & 877,924 & 895,685 & 864,309 & 894,033 \\ \hline \hline \multicolumn{8}{c}{**C. Amount of average scrap per unit in meters, generated each month**} \\ \multicolumn{8}{c}{for each model of die and nozzle, used in the extrusion process**} \\ \hline
**Model** & **January** & **February** & **March** & **April** & **May** & **June** \\ \hline
1 DNL & 7366 & 7620 & 7239 & 8001 & 8382 & 101,607 \\
2 DNV & 9129 & 8268 & 10,507 & 7751 & 9646 & 8613 \\
3 DND & 6402 & 6608 & 5782 & 7537 & 7847 & 7124 \\
4 DNK & 7493 & 8838 & 8454 & 9798 & 7109 & 8661 \\
5 DNS & 10,161 & 7466 & 11,198 & 8295 & 9747 & 7880 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Collected data from January to June 2018. Figure 4: Explain the steps to apply the proposed methodology, data analysis through ANOVA and validation of results with DEA model. Due to the manufacture efficiency of the 23-AWG wire can be measured through three indicators: its lifetime, the amount of wire manufactured, and the amount of wire's scrap generated. These indicators were measured during six months for 5 different manufacturing models in order to determine if there was a change at manufacture efficiency of the 23-AWG. However, manufacturing model is not the only factor can affect manufacture efficiency of the 23-AWG wire. Several noisy factors such as: machine, operator, supervisor, shift and, color can alter the manufacturing efficiency [28]. In this way, the following expression can be used to modeling the changes in the manufacturing efficiency:  
\[E=\delta_{Model}+\delta_{Machine}+\delta_{Operator}+\delta_{Shift}+\delta_{Color }+\varepsilon \tag{1}\]  
where: \(\delta_{i}\) is the effect of the factor into the manufacture efficiency and \(\in N(0,\sigma^{2})\). Nevertheless, as a result of quality control of the company the previous model can be reduced as follows:  
\[E=\delta+\delta_{Model}+\varepsilon^{\prime} \tag{2}\]  
where: \(\in N(0,\sigma^{2})\) with \(\sigma<\gamma\). Therefore, the effect of the model can be explored by using a 1-way ANOVA. In Table 2 is presented the measures of the three indicators for every model during the 6-months period and, Figure 5 shows the data analysis workflow. Table 3 presents number of km produced by cable vs. the number of die-nozzles/month, used for each production line.