Author ORCID Identifier
Laharish Guntuka: 0000-0003-2640-5754
Abstract
This paper explores the application of generative artificial intelligence (AI) in designing resilient and flexible "plastic supply networks" that adapt structurally in response to disruptions. Traditional supply chain management approaches often aim to restore pre-disruption conditions, lacking the capacity for significant structural reconfiguration. By contrast, plastic networks enable adaptability, allowing for proactive restructuring to meet evolving demands and counter unforeseen challenges. Generative AI enhances this adaptability, offering data-driven solutions that continuously optimize supply chain configurations through real-time analyses of supplier performance, logistics, and market trends. These AI-driven networks can dynamically alter routes, supplier relationships, and inventory strategies, responding efficiently to geopolitical, environmental, and economic shifts. This study contributes to supply chain resilience literature by positioning AI as a catalyst for network plasticity, fostering agility and sustainability within supply ecosystems. Finally, we address the computational, data quality, and ethical challenges associated with AI implementation, highlighting areas for future research and governance in creating resilient, adaptive supply chains.
Keywords
Generative AI, Plastic Supply Networks, Supply Chain Resilience
Recommended Citation
Guntuka, Laharish and Carnovale, Steven
(2025)
"Plasticity in Supply Networks: Leveraging Generative AI for Flexible and Resilient Supply Chain Design,"
Management Dynamics: Vol. 25:
No.
1, Article 4:
DOI: https://doi.org/10.57198/2583-4932.1345
Available at:
https://managementdynamics.researchcommons.org/journal/vol25/iss1/4
