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AI-Driven Numerical Optimization for Carbon Footprint Reduction and Sustainable Supply Chain Management in the Fashion Industry

Year: 2024

Type: article

Abstract: This study explores how AI-driven optimization can help lower the carbon footprint of the fashion industry's supply chain. By using advanced AI models, the research delves into sustainable strategies across core areas—sourcing, production, logistics, and retail—focusing on reducing emissions, energy use, water consumption, and waste. Techniques like linear programming, genetic algorithms, and reinforcement learning showcase how AI can bring about real, measurable environmental benefits. The findings reveal that these optimized processes boost energy efficiency, save water, and cut down waste, making them valuable tools for sustainable supply chain management. The study wraps up with practical recommendations for adopting AI-based models to help the fashion industry embrace sustainability more effectively.

Source: South Eastern European Journal of Public Health

DOI: 10.70135/seejph.vi.2023

Authors: Dr. Ranadheer Donthi, Dr. B. Prasanna Lakshmi, G. Srinivas, Sunkesula Sudhakar, Harisivasri Phanindra Koneru, Dr. Prasanna Kumar Yekula

Institutions: Mahatma Gandhi Institute of Technology, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Papua New Guinea University of Technology

Language: English

Topic: Sustainable Supply Chain Management

Subfield: Strategy and Management

Field: Business, Management and Accounting

Domain: Social Sciences

SDGs: Responsible consumption and production, Climate action, Industry, innovation and infrastructure

Open Access status: diamond

OpenAlex ID: W4404223074

FWCI: 1.744

Cites:

Cited by: 5

Related to: 10