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Prediction of Uncertain Parameters of a Sustainable Supply Chain Using an Artificial Intelligence Approach

Author

Listed:
  • Massoumeh Nazari

    (Islamic Azad University, Tehran South Branch, Management Faculty)

  • Mahmoud Dehghan Nayeri

    (Tarbiat Modares University, Management and Economics Faculty)

  • Kiamars Fathi Hafshjani

    (Islamic Azad University, Tehran South Branch, Management Faculty)

Abstract

Automotive companies have a stable supply chain due to extensive vehicle production and global supply networks. The purpose of sustainable supply chain intelligence in this study is to minimize system costs and environmental pollution. This study is descriptive-analytical, and transportation costs, which have a significant role in environmental pollution, were considered the main parameter, using time series forecasting by Narnet. The results showed significant differences between the predicted shipping costs from the supplier to the factory, from the factory to the distributor, from the distributor to the customer, from the customer to recycling, and from recycling back to the factory. The findings show artificial intelligence in the sustainable automotive supply chain can improve efficiency, reduce resource waste, enhance risk management, and maintain the sustainability of the supply chain.

Suggested Citation

  • Massoumeh Nazari & Mahmoud Dehghan Nayeri & Kiamars Fathi Hafshjani, 2025. "Prediction of Uncertain Parameters of a Sustainable Supply Chain Using an Artificial Intelligence Approach," SN Operations Research Forum, Springer, vol. 6(1), pages 1-25, March.
    • Handle: RePEc:spr:snopef:v:6:y:2025:i:1:d:10.1007_s43069-024-00408-7
    DOI: 10.1007/s43069-024-00408-7
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    References listed on IDEAS

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    1. Yasanur Kayikci & Yigit Kazancoglu & Nazlican Gozacan‐Chase & Cisem Lafci, 2022. "Analyzing the drivers of smart sustainable circular supply chain for sustainable development goals through stakeholder theory," Business Strategy and the Environment, Wiley Blackwell, vol. 31(7), pages 3335-3353, November.
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