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

Author

Listed:
  • Zhe Wee Ng

    (Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA 94720, USA)

  • Biswajit Debnath

    (School of Sustainability, New Age Makers’ Institute of Technology, NAMTECH, Gandhinagar 382055, Gujarat, India)

  • Amit K Chattopadhyay

    (School of Business, National College of Ireland, D01 K6W2 Dublin, Ireland)

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.

Suggested Citation

  • Zhe Wee Ng & Biswajit Debnath & Amit K Chattopadhyay, 2025. "Machine Learning-Aided Supply Chain Analysis of Waste Management Systems: System Optimization for Sustainable Production," Sustainability, MDPI, vol. 17(19), pages 1-23, October.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:19:p:8848-:d:1764047
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    References listed on IDEAS

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    1. Hajar Fatorachian & Hadi Kazemi & Kulwant Pawar, 2025. "Digital Technologies in Food Supply Chain Waste Management: A Case Study on Sustainable Practices in Smart Cities," Sustainability, MDPI, vol. 17(5), pages 1-25, February.
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    4. Freitas, Emmanuelle Soares de Carvalho & Xavier, Lúcia Helena, 2025. "System dynamics applied to the e-waste value chain: A brazilian case study," Ecological Modelling, Elsevier, vol. 506(C).
    5. Biswajit Debnath & Amit K. Chattopadhyay & T. Krishna Kumar, 2024. "An Economic Optimization Model of an E-Waste Supply Chain Network: Machine Learned Kinetic Modelling for Sustainable Production," Sustainability, MDPI, vol. 16(15), pages 1-25, July.
    6. Won Hee Choi & Kook Pyo Pae & Nam Seok Kim & Hong Yoon Kang & Yong Woo Hwang, 2023. "Feasibility Study of Closed-Loop Recycling for Plastic Generated from Waste Electrical and Electronic Equipment (WEEE) in South Korea," Energies, MDPI, vol. 16(17), pages 1-19, September.
    7. John C Frain, 2010. "An Introduction to Matlab for Econometrics," Trinity Economics Papers tep0110, Trinity College Dublin, Department of Economics.
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    9. Zeinab Farshadfar & Tomasz Mucha & Kari Tanskanen, 2024. "Leveraging Machine Learning for Advancing Circular Supply Chains: A Systematic Literature Review," Logistics, MDPI, vol. 8(4), pages 1-25, October.
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