AI-Driven Sustainable Supply Chain Framework for Polymer Composite Production

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Notice

nThis is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.n

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Year : 2025 [if 2224 equals=””]01/09/2025 at 4:42 PM[/if 2224] | [if 1553 equals=””] Volume : 13 [else] Volume : 13[/if 1553] | [if 424 equals=”Regular Issue”]Issue : [/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] 05 | Page : 219 235

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    Indrajit Ghosal, Deepika Saxena, Ritam Rajak, Kapil Gulati, Seema Kaloria,

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  1. Associate Professor, Associate Professor, Assistant Professor, Assistant Professor, Assistant Professor, Department of Management, Brainware University, Kolkata, Department of Computer science and Engineering, Poornima University, Jaipur, Department of CSE AI&ML, Moodlakatte Institute of Technology, Kundapur, Department of Computer science and Application, Poornima University, Jaipur, Department of Computer science and Application, Poornima University, Jaipur, West Bengal, Rajasthan, Karnataka, Rajasthan, Rajasthan, India, India, India, India, India

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Abstract

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nAs polymer composite processes become more difficult and environmental concerns increase, old supply chain models that just look at cost and operations have shown significant weaknesses when it comes to sustainability. The rising demand for environmentally friendly practices throughout a product’s life cycle requires a new process that makes sustainability a key element in making supply chain choices. The proposed framework was developed in response to this need by using AI to support sustainable supply chain management in the polymer composite sector and includes strong environmentally focused elements throughout the process. The proposed framework is structured into five interdependent layers, encompassing real-time data acquisition, sustainability-embedded predictive modeling, multi-objective optimization, adaptive feedback monitoring, and automated sustainability assessment. It seeks to replace traditional, budget-focused supply chains with clever, flexible, and green options. Applying advanced AI to the handling of materials’ life cycles, the framework assists organizations in better managing environmental problems, maximizing resources, and making their operations follow circular economy theories. The benefits of using the framework can be improved operational sustainability, major cuts in carbon emissions, an increase in material recycling, and compliance with anticipated global sustainability rules. The model is recommended for use and refinement by industrial practitioners, members of academia, policymakers, and system developers. Adding AI into the supply of composite materials greatly contributes to the goal of having intelligent, flexible, and sustainable industrial systems.nn

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Keywords: Polymer composite, sustainable supply chain management, environmental sustainability, circular economy, artificial intelligence.

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How to cite this article:
nIndrajit Ghosal, Deepika Saxena, Ritam Rajak, Kapil Gulati, Seema Kaloria. [if 2584 equals=”][226 wpautop=0 striphtml=1][else]AI-Driven Sustainable Supply Chain Framework for Polymer Composite Production[/if 2584]. Journal of Polymer and Composites. 19/08/2025; 13(05):219-235.

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How to cite this URL:
nIndrajit Ghosal, Deepika Saxena, Ritam Rajak, Kapil Gulati, Seema Kaloria. [if 2584 equals=”][226 striphtml=1][else]AI-Driven Sustainable Supply Chain Framework for Polymer Composite Production[/if 2584]. Journal of Polymer and Composites. 19/08/2025; 13(05):219-235. Available from: https://journals.stmjournals.com/jopc/article=19/08/2025/view=0

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Journal of Polymer and Composites

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[if 344 not_equal=””]ISSN: 2321–2810[/if 344]

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Volume 13
[if 424 equals=”Regular Issue”]Issue[/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] 05
Received 27/06/2025
Accepted 02/08/2025
Published 19/08/2025
Retracted
Publication Time 53 Days

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