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AI-Driven Model to Enhance Digital Empowerment in Supply Chains for Better Planning |
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PP: 527-537 |
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doi:10.18576/amis/200218
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Author(s) |
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Hasan Abualese,
Mohammed Awad Alkhawaldeh,
Rami Hasan AL-Ta’ani,
Mohammad Rasmi Al-Mousa,
Yazeed Alsarhan,
Musab Alqudah,
Ahmad Al-Jarrah,
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Abstract |
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| The increasing complexity and unpredictability of global supply chains have posed significant challenges to their effective management. To address these challenges, this study proposes an artificial intelligence (AI)-based model for the digital empowerment of supply chains aimed at enhancing proactive planning and operational efficiency. The proposed framework integrates multiple machine learning algorithms for demand forecasting, delivery risk assessment, and inventory optimization. The model was trained and evaluated using the DataCo Smart Supply Chain dataset, which contains real-world transactional data. Experimental results indicate that the proposed approach significantly improves delivery date prediction accuracy by 38% in predicting future sales over conventional methods, reduces the risk of delayed deliveries with a precision of 94% and recall of 91%, and optimizes inventory levels within digitally enabled supply chains by a reduction of 83.3% in stockouts and expediting costs. Moreover, this research contributes to the growing body of literature on AI-driven supply chain management by providing a practical and scalable framework that organizations can adopt to achieve competitive advantage through digital transformation. The findings demonstrate that artificial intelligence enhances supply chain intelligence, transparency, and resilience, thereby supporting data-driven and sustainable decision-making processes. |
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