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E-Commerce Supply Chain Resilience and Sustainability Through AI-Driven Demand Forecasting and Waste Reduction

Author

Listed:
  • Hanxi Dong

    (School of Economics and management, University of Science and Technology Beijing, Beijing 100083, China)

  • Daoping Wang

    (School of Economics and management, University of Science and Technology Beijing, Beijing 100083, China)

  • Shafiul Bashar

    (School of Economics and management, University of Science and Technology Beijing, Beijing 100083, China)

Abstract

The rapid growth of e-commerce demands innovative solutions for resilient and sustainable supply chains. This study explores the role of AI-driven demand forecasting (AIDF) and AI-driven waste reduction (AIDWR) in enhancing supply chain efficiency, minimizing operational waste, and fostering sustainability. Analyzing data from 539 samples via PLS-SEM, the findings highlight how AIDF optimizes demand accuracy, reduces overproduction, and minimizes stockouts, while AIDWR lowers resource consumption and mitigates environmental impacts. Operational Waste Reduction mediates AI’s effectiveness, aligning efficiency with sustainability goals and promoting adaptable, environmentally conscious supply chains. These insights guide e-commerce managers in leveraging AI for resilience and sustainable growth. The study underscores the transformative potential of AI to meet dual objectives of operational excellence and sustainability.

Suggested Citation

  • Hanxi Dong & Daoping Wang & Shafiul Bashar, 2025. "E-Commerce Supply Chain Resilience and Sustainability Through AI-Driven Demand Forecasting and Waste Reduction," Sustainability, MDPI, vol. 18(1), pages 1-24, December.
    • Handle: RePEc:gam:jsusta:v:18:y:2025:i:1:p:360-:d:1829284
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