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Article

Machine Learning-Aided Supply Chain Analysis of Waste Management Systems: System Optimization for Sustainable Production

1
Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA 94720, USA
2
School of Sustainability, New Age Makers’ Institute of Technology, NAMTECH, Gandhinagar 382055, Gujarat, India
3
School of Business, National College of Ireland, D01 K6W2 Dublin, Ireland
*
Authors to whom correspondence should be addressed.
Sustainability 2025, 17(19), 8848; https://doi.org/10.3390/su17198848
Submission received: 1 July 2025 / Revised: 24 September 2025 / Accepted: 30 September 2025 / Published: 2 October 2025

Abstract

Electronic-waste (e-waste) management is a key challenge in engineering smart cities due to its rapid accumulation, complex composition, sparse data availability, and significant environmental and economic impacts. This study employs a bespoke machine learning infrastructure on an Indian e-waste supply chain network (SCN) focusing on the three pillars of sustainability—environmental, economic, and social. The economic resilience of the SCN is investigated against external perturbations, like market fluctuations or policy changes, by analyzing six stochastically perturbed modules, generated from the optimal point of the original dataset using Monte Carlo Simulation (MCS). In the process, MCS is demonstrated as a powerful technique to deal with sparse statistics in SCN modeling. The perturbed model is then analyzed to uncover “hidden” non-linear relationships between key variables and their sensitivity in dictating economic arbitrage. Two complementary ensemble-based approaches have been used—Feedforward Neural Network (FNN) model and Random Forest (RF) model. While FNN excels in regressing the model performance against the industry-specified target, RF is better in dealing with feature engineering and dimensional reduction, thus identifying the most influential variables. Our results demonstrate that the FNN model is a superior predictor of arbitrage conditions compared to the RF model. The tangible deliverable is a data-driven toolkit for smart engineering solutions to ensure sustainable e-waste management.
Keywords: supply chain network (SCN); machine learning (ML); Monte Carlo (MC) simulation; feedforward neural network (FNN); Random Forest Model (RFM); waste management; Sustainability supply chain network (SCN); machine learning (ML); Monte Carlo (MC) simulation; feedforward neural network (FNN); Random Forest Model (RFM); waste management; Sustainability

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MDPI and ACS Style

Ng, Z.W.; Debnath, B.; Chattopadhyay, A.K. Machine Learning-Aided Supply Chain Analysis of Waste Management Systems: System Optimization for Sustainable Production. Sustainability 2025, 17, 8848. https://doi.org/10.3390/su17198848

AMA Style

Ng ZW, Debnath B, Chattopadhyay AK. Machine Learning-Aided Supply Chain Analysis of Waste Management Systems: System Optimization for Sustainable Production. Sustainability. 2025; 17(19):8848. https://doi.org/10.3390/su17198848

Chicago/Turabian Style

Ng, Zhe Wee, Biswajit Debnath, and Amit K Chattopadhyay. 2025. "Machine Learning-Aided Supply Chain Analysis of Waste Management Systems: System Optimization for Sustainable Production" Sustainability 17, no. 19: 8848. https://doi.org/10.3390/su17198848

APA Style

Ng, Z. W., Debnath, B., & Chattopadhyay, A. K. (2025). Machine Learning-Aided Supply Chain Analysis of Waste Management Systems: System Optimization for Sustainable Production. Sustainability, 17(19), 8848. https://doi.org/10.3390/su17198848

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