AI-Driven Automation Enhances Sustainability Efforts in Supply Chain Management

Strategic Insights -

Researchers at the Vellore Institute of Technology in Chennai, India, have developed a novel hybrid approach to optimize supply chain sustainability by combining Genetic Algorithms with Long Short-Term Memory networks. This innovative method leverages publicly available Carbon Disclosure Project (CDP)-reported data to predict emissions and optimize resource allocation, reducing emissions, improving operational efficiency, and ensuring regulatory compliance. The proposed system achieved a 23.67% reduction in total emissions, with the most significant improvements in indirect emissions, and improved operational efficiency by 10.98% while ensuring 100% compliance with environmental regulations.

Key Takeaways:

  • The hybrid approach combines Genetic Algorithms with Long Short-Term Memory networks to optimize supply chain sustainability, reducing emissions, and improving operational efficiency.
  • The system leverages publicly available CDP-reported data to predict emissions and optimize resource allocation.
  • The proposed approach achieved a 23.67% reduction in total emissions, with the most significant improvements in indirect emissions.
  • Operational efficiency improved by 10.98%, while ensuring 100% compliance with environmental regulations.
  • The hybrid GA-LSTM framework offers valuable insights for businesses seeking to meet sustainability targets and provides a practical, data-driven method for improving supply chain performance.
  • The system is not only applicable to large corporations but can also be scaled for use in small and medium-sized enterprises.
  • The research concluded that the proposed system can be widely adopted across industries, including those in Asia, particularly in India, where sustainability research is gaining momentum.

Statistics:

  • 23.67% reduction in total emissions achieved by the proposed approach.
  • 10.98% improvement in operational efficiency.
  • 100% compliance with environmental regulations ensured by the system.
  • The system was able to predict emissions based on historical data and optimize resource management to minimize emissions and operational costs.

Sources:

  • "AI Driven Automation for Enhancing Sustainability Efforts in CDP Report Analysis"
  • Scientific Reports, 2025;15(1):24266.
  • Nature Portfolio, Heidelberger Platz 3, Berlin, 14197, Germany.
  • Ramya Rangarajan, School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, India.
  • Tamilarasi Kathirvel Murugan, Logeswari Govindaraj, Venyaa Venkataraman, Krithik Shankar, Vellore Institute of Technology, Chennai, India.
  • Ecology, Environment & Conservation, 2025; p 624.