Analysis of Cleaner Production Performance in Manufacturing Companies Employing Artificial Neural Networks
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/6/4029 |
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| author | Rafael Abrantes Penchel Ivan Aldaya Lucas Marim Mirian Paula dos Santos Lucio Cardozo-Filho Veeriah Jegatheesan José Augusto de Oliveira |
| author_facet | Rafael Abrantes Penchel Ivan Aldaya Lucas Marim Mirian Paula dos Santos Lucio Cardozo-Filho Veeriah Jegatheesan José Augusto de Oliveira |
| author_sort | Rafael Abrantes Penchel |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T06:57:26Z |
| format | Article |
| id | doaj.art-5bb83ceab1084b3a8028507042a63104 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T06:57:26Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-5bb83ceab1084b3a8028507042a631042023-11-17T09:30:30ZengMDPI AGApplied Sciences2076-34172023-03-01136402910.3390/app13064029Analysis of Cleaner Production Performance in Manufacturing Companies Employing Artificial Neural NetworksRafael Abrantes Penchel0Ivan Aldaya1Lucas Marim2Mirian Paula dos Santos3Lucio Cardozo-Filho4Veeriah Jegatheesan5José Augusto de Oliveira6School of Engineering, São Paulo State University (Unesp), Campus of São João da Boa Vista, São João da Boa Vista 13876-750, BrazilSchool of Engineering, São Paulo State University (Unesp), Campus of São João da Boa Vista, São João da Boa Vista 13876-750, BrazilSchool of Engineering, São Paulo State University (Unesp), Campus of São João da Boa Vista, São João da Boa Vista 13876-750, BrazilSchool of Engineering, São Paulo State University (Unesp), Campus of São João da Boa Vista, São João da Boa Vista 13876-750, BrazilSchool of Engineering, São Paulo State University (Unesp), Campus of São João da Boa Vista, São João da Boa Vista 13876-750, BrazilSchool of Engineering and Water: Effective Technologies and Tools (WETT) Research Centre, RMIT University, Melbourne, VIC 3000, AustraliaSchool of Engineering, São Paulo State University (Unesp), Campus of São João da Boa Vista, São João da Boa Vista 13876-750, BrazilCleaner 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.https://www.mdpi.com/2076-3417/13/6/4029artificial neural networkcleaner productionenvironmental performanceeconomic performance |
| spellingShingle | Rafael Abrantes Penchel Ivan Aldaya Lucas Marim Mirian Paula dos Santos Lucio Cardozo-Filho Veeriah Jegatheesan José Augusto de Oliveira Analysis of Cleaner Production Performance in Manufacturing Companies Employing Artificial Neural Networks Applied Sciences artificial neural network cleaner production environmental performance economic performance |
| title | Analysis of Cleaner Production Performance in Manufacturing Companies Employing Artificial Neural Networks |
| title_full | Analysis of Cleaner Production Performance in Manufacturing Companies Employing Artificial Neural Networks |
| title_fullStr | Analysis of Cleaner Production Performance in Manufacturing Companies Employing Artificial Neural Networks |
| title_full_unstemmed | Analysis of Cleaner Production Performance in Manufacturing Companies Employing Artificial Neural Networks |
| title_short | Analysis of Cleaner Production Performance in Manufacturing Companies Employing Artificial Neural Networks |
| title_sort | analysis of cleaner production performance in manufacturing companies employing artificial neural networks |
| topic | artificial neural network cleaner production environmental performance economic performance |
| url | https://www.mdpi.com/2076-3417/13/6/4029 |
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