## 1 Introduction  
Cleaner Production (CP) is the major productive strategy to prevent the environmental impacts in the manufacturing of products [1, 2]. CP was introduced in 1990, and has been developed and implemented throughout the globe [3]. Recognized worldwide by the reach of ecoefficiency indicators, the implementation of its practices is strongly stimulated by improvements in economic, environmental, and production performances [4, 5, 6]. Although CP has no market certification or accreditation, productive organizations are encouraging the implementation of CP to attain four groups of motivators [5]: legislative and governmental pressures; regulatory pressures from customers; demands from customers; economic opportunities for cost reduction [2]. CP studies can be roughly divided into three major approaches [2]. The first is the development of technologies for application in products and production processes. The second approach focuses on exploring successful case studies from companies that have adopted CP practices. Finally, the third approach deals with the application of surveys to characterize the adoption of CP by companies. Since its foundation, this environmental production strategy has presented hierarchical levels, organized into groups of practical possibilities that indicate as to which of these groups suggests greater environmental performance [6]. In decreasing scale, these groups comprise product design, productive processes, reuse, internal recycling, and external recycling [3]. In addition, within each group, there is a wide range of practical possibilities to be implemented by companies. With these application levels, CP can help industries improve their product performance, environmental processes, and economic performance. Nevertheless, the success of a CP approach relies on an accurate multivariate decision-making process. In this context, two main model types have been proposed to assist in this decision-making task [1; 2; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19]. On the one hand, maturity models use a segmented, conceptual proposition to evaluate the maturity of a given type of performance, depending on the use of resources employed. The literature that approaches maturity models has converged to some applications, such as the product development process, seen for example in [9; 10; 13; 14; 15; 18; 19; 20; 21]. This literature also includes applications in management systems' certification, as can be seen in [4; 11; 16; 22]. Additionally, there is also a search for correlations of input variables (practices) with output variables (performance) by Structural Equation Modeling (SEM) in CP, as cited in studies by [1; 2; 5]. In brief, the maturity models have been employed, for instance, in order to optimize system performance by identifying the best practices qualitatively [22], and the SEM presents a correlational framework between these constructs and does not allow the establishment of a hierarchical relationship between these variables. Alternatively, Multi-Criteria Decision-Making (MCDM) models have been explored to assess operational practices' different scales and/or levels and/or maturity. Several types of MCDM have been studied in the literature, highlighting the following methods: PROMETHEE, TOPSIS, ELECTRE, and the Analytic Hierarchy Process (AHP) proposed by [23]. However, none of these works on MCDM adopted CP as an object of study. In addition, these models establish a framework but do not map the input and output variables, making a sensitivity analysis unfeasible. Since its beginning, one of the main applications of machine learning was to map inputs into outputs without requiring a deep knowledge of the system [24]. Machine learning has been successfully applied in areas as diverse as geosciences [25; 26], telecommunications [27; 28; 29], medicine [30; 31], and the economy [32; 33], among others. In this sense, operation management is not an exception.