The Role of Artificial Intelligence in Optimizing Supply Chain Performance
Chibale M. Sakala () and
Sichone M. Bwalya ()
Journal of Procurement and Supply Chain Management, 2023, vol. 2, issue 1, 1 - 14
Abstract:
Aim: With the increasing complexity of supply chains, there is a need for better optimization to improve efficiency and reduce costs. AI has the potential to transform supply chain management by providing better insights, decision-making capabilities, and automation. This study aims to examine the benefits and challenges of integrating AI into supply chain management. Methodology: The study was based on a review of literature where various views obtained from theoretical and empirical works were evaluated. The study sought to critically review literature on the relationship between Artificial Intelligence and supply chain performance. Findings: The reviewed studies indicated that AI techniques such as machine learning, natural language processing, and predictive analytics helps organizations to make better decisions, improve planning and forecasting, and reduce costs. AI also enables automation of routine tasks, freeing up human resources to focus on more strategic activities. Conclusion: It was concluded that organizations that leverage AI to streamline their supply chains achieve significant benefits, including improved efficiency, reduced costs and increased customer satisfaction. Recommendation: Organizations should identify areas of their supply chain where AI can provide the most significant benefits, such as demand forecasting, inventory management, and logistics optimization. To ensure successful implementation, companies should invest in quality data, robust infrastructure, and skilled personnel. Additionally, companies should prioritize collaboration and communication across all departments to ensure that the benefits of AI are fully realized.
Keywords: Artificial Intelligence; supply chain; performance; inventory (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:cjk:ojpscm:v:2:y:2023:i:1:p:1-14:id:215
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