Analysis of Cleaner Production Performance in Manufacturing Companies Employing Artificial Neural Networks

• Autor(a) principal: Penchel, Rafael Abrantes [UNESP]
• Data de Publicação: 2023
• Outros Autores: Aldaya, Ivan [UNESP], Marim, Lucas [UNESP], dos Santos, Mirian Paula [UNESP], Cardozo-Filho, Lucio [UNESP], Jegatheesan, Veeriah, de Oliveira, José Augusto [UNESP]
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.3390/app13064029; http://hdl.handle.net/11449/247129
• topic: artificial neural network; cleaner production; economic performance; environmental performance

Cleaner production has emerged as a comprehensive paradigm, aiming to reduce, or even avoid, the environmental impact in the production stage, in a broad variety of fields. However, the great number of interacting factors makes the assessment of efficiency and the identification of critical factors pose significant challenges to researchers and companies. Artificial intelligence and, particularly, artificial neural networks have proven their suitability to lead with diverse multi-variable problems, but have not yet been applied to model production systems. In this work, we employ dimensionality reduction in combination with a fully connected feed-forward multi-layer perceptron to model the relation between the input (cleaner production techniques) and output variables (cleaner production performance) and, subsequently, quantify the sensibility of the different output variables on the input variables. In particular, we consider Product Design, Production Processes, and Reuse as the input latent variables, whereas the Environmental Performance of Product, Environmental Performance of Processes, and Economic Performance comprises the output variables of our model. The results, employing data collected from a direct survey of 205 Brazilian companies, reveal that the best configuration for the ANN uses eight neurons in the hidden layer. Regarding sensitivity, the obtained results show that improving practices with poor marks leads to a higher enhancement of output figures. In particular, since reuse presents mainly low marks, it can be identified as an area for improvement, in order to increase overall performance.