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

Massoumeh Nazari (), Mahmoud Dehghan Nayeri () and Kiamars Fathi Hafshjani ()
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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

SN Operations Research Forum, 2025, vol. 6, issue 1, 1-25

Abstract: 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.

Keywords: Sustainable supply chain; Artificial intelligence; Uncertainty; Non-linear programming (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s43069-024-00408-7

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