Enhancing Decision-Making in Enterprise Modeling: A Comparative Analysis of Artificial Intelligence Techniques in Supply Chain Network Design
DOI:
https://doi.org/10.35335/emod.v13i2.9Keywords:
Supply chain network design, Optimization, Artificial intelligence, Hybrid approaches, Comparative studyAbstract
This research focuses on enhancing decision-making in enterprise modeling through a comparative analysis of artificial intelligence (AI) techniques in supply chain network design. The objective is to provide decision-makers with insights into the application, performance, and implications of different AI techniques in this domain. The research conducts a comprehensive comparative analysis of AI techniques, including machine learning algorithms, optimization algorithms, and expert systems. Performance evaluation metrics such as computational efficiency, accuracy, scalability, interpretability, and adaptability are established to assess the performance of these techniques. Real-world case studies are also presented to showcase the practical implementation and impact of AI techniques in supply chain network design. The findings contribute to informed decision-making by guiding decision-makers in selecting and implementing appropriate AI techniques. The research also identifies future research directions, including hybrid approaches, dynamic environment considerations, and the integration of AI with big data and the Internet of Things. Overall, this research provides valuable insights and guidelines for leveraging AI in supply chain network design, enabling decision-makers to optimize facility location, transportation routing, and inventory management, leading to improved operational efficiency and customer satisfaction.
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