### Data Envelopment Analysis  
DEA is an operational research technique focused on measuring and analyzing the efficiency with which goods and services are produced. Its main applications have been in sectors such as finance, health, services, education or to measure the effectiveness of organizational indicators [11]. DEA is a relatively new data-oriented approach for evaluating the performance of a set of peer entities called Decision Making Units (DMUs) which convert multiple inputs into multiple outputs. The definition of a DMU is generic and flexible. Recent years have seen a great variety of applications of DEA for use in evaluating the performance of different kinds of entities engaged in different activities in certain contexts in lots of countries [12]. To differentiate and rank the performance of efficient DMU, [13] proposed the concept of super-efficiency. DEA is capable of transforming a productive situation in which diverse resources generate multiple products in a single index of efficiency. This index is identified with the value that maximizes the quotient between the weighted sum of the outputs and the weighted sum of the inputs of the entity analyzed, to estimate the level of relative efficiency of a company or production unit with respect to the rest of the units that are evaluated simultaneously, making the best use of resources. It provides useful information to improve efficiency by providing tools establishing which cases do not use resources efficiently and which are the recommendations on the use of resources to improve the efficiency of the organization [14]. Analyzing the performance of a set of homogeneous items is very important in the manufacturing processes. Parametric and non-parametric methods are proposed for evaluating the performance of DMUs. DEA is one of the most famous and applicable parametric techniques in this field. It has many applications for interpreting the productivity of complex systems manufacturing [15]. In this project, we made an extensive review covering the literature from 2000 to 2019 and several analyses about DEA. [16] applied the DEA in the design of production lines. In [17], due to advances in data collection systems and analysis tools, data mining (DM) applied DEA and Quality Improvement (QI), in manufacturing. In [18], it is demonstrated how the DEA model might be useful to screen training data so a subset of examples that satisfy the monotony property can be identified. They used real-world health care and software engineering data, managerial monotony assumption, and Artificial Neural Network (ANN) as a forecasting model. [19] used DEA and an analytic hierarchy process (AHP) to measure the performance of ball valve production. In [20], taking into account non-operational factors, the DEA was proposed to measure the technical efficiency, scale efficiency and pure technical efficiency of innovation in Semiconductor industry of China. [21] analyzed the effect of reducing energy savings on the efficiency of innovation and performed the DEA and Tobit regression analysis. This was in agreement with [22], who developed for the Chinese Government a model on the allocation of carbon emission reduction quotas through DEA. [23] established improved manufacturing methods using DEA. In addition, [24] studied recent advances and trends in predictive manufacturing systems in big data environments using DEA. Table 1 illustrates the 23-AWG wire. We intend to analyze the performance of a 3 DND die-nozzle model of Mexican manufacturing versus four foreign-made die-nozzle models, and its impact on the variation in the thickness of the insulation to determine the capability and performance of the process of each die-nozzle model, with the range of \(\pm\)0.06 mm for a base dimension of 0.58 mm. \begin{table}
\begin{tabular}{c c c c c c c} \hline
**AWG** & **Diameter (mm)** & **Section mm\({}^{2}\)** & **Number of Turns/cm** & **kg/km** & **Resistance (f)/km** & **Capacity (A)** \\ \hline
23 & 0.58 & 0.26 & 16.0 & 2.29 & 56.4 & 0.73 \\ \hline \end{tabular}
\end{table}