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<p>Data analytics and machine learning are emerging as leading technologies to develop next-generation data-driven decision-making tools in supply chain management. Predictive analytics play an important role in providing deep insights to lower uncertainty and boost overall efficiency in terms of demand fulfillment, inventory management, and resource allocation, thereby enhancing informed decision-making. By leveraging historical data, efficient forecasting can further be developed, guiding supply chain design and operational decisions. In this work, we focus on reverse logistics in support of development of closed-loop supply chains for photovoltaic panels (PV), and employ advanced statistical and deep learning forecasting techniques for predicting demand and commodity prices. The selected prediction models are integrated into the supply chain optimization model, and the impact on the whole system performance is investigated. The proposed methodology streamlines operations, reduces costs, and allows for quick adjustments to shifting market dynamics. This work underscores the transformative potential and competitive advantage of AI-employed data-driven analytics in ensuring sustainable and resilient supply chains within the circular economy, particularly for critical materials in PV recycling.</p>

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AI-based predictive analytics for enhancing data-driven supply chain optimization

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月2日

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  • Funda Iseri,
  • Halil Iseri,
  • Natasha J. Chrisandina,
  • Eleftherios Iakovou,
  • Efstratios N. Pistikopoulos

摘要

Data analytics and machine learning are emerging as leading technologies to develop next-generation data-driven decision-making tools in supply chain management. Predictive analytics play an important role in providing deep insights to lower uncertainty and boost overall efficiency in terms of demand fulfillment, inventory management, and resource allocation, thereby enhancing informed decision-making. By leveraging historical data, efficient forecasting can further be developed, guiding supply chain design and operational decisions. In this work, we focus on reverse logistics in support of development of closed-loop supply chains for photovoltaic panels (PV), and employ advanced statistical and deep learning forecasting techniques for predicting demand and commodity prices. The selected prediction models are integrated into the supply chain optimization model, and the impact on the whole system performance is investigated. The proposed methodology streamlines operations, reduces costs, and allows for quick adjustments to shifting market dynamics. This work underscores the transformative potential and competitive advantage of AI-employed data-driven analytics in ensuring sustainable and resilient supply chains within the circular economy, particularly for critical materials in PV recycling.