Tirkolaee et al., 2021
ViewPDF| Publication | Publication Date | Title |
|---|---|---|
| Tirkolaee et al. | Application of machine learning in supply chain management: a comprehensive overview of the main areas | |
| Ni et al. | A systematic review of the research trends of machine learning in supply chain management | |
| Rahmani et al. | Applications of artificial intelligence in the economy, including applications in stock trading, market analysis, and risk management | |
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| Agarwal et al. | Machine Learning and Natural Language Processing in Supply Chain Management: A Comprehensive Review and Future Research Directions. | |
| Polo-Triana et al. | Integration of machine learning in the supply chain for decision making: A systematic literature review | |
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| Zhu et al. | A Hybrid Approach for Predicting Corporate Financial Risk: Integrating SMOTE-ENN and NGBoost | |
| Abed | Accelerate demand forecasting by hybridizing CatBoost with the dingo optimization algorithm to support supply chain conceptual framework precisely | |
| Zimmermann et al. | Leveraging analytics for digital transformation of enterprise services and architectures | |
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| Nikseresht et al. | Proactive product warranty service planning and control: unravelling the boons of customer-generated content and multi-frequency analyses | |
| John et al. | AI-Driven Supply Chain Risk Management in the Manufacturing Sector: Tackling Data Bias, Ensuring Algorithmic Transparency, and Enhancing Human-AI Collaboration | |
| Lyu et al. | Explainable Artificial Intelligence for Business and Economics: Methods, Applications and Challenges | |
| Gokhale et al. | A binary classification approach to lead identification and qualification | |
| Sadeghi et al. | Analyzing the commercialization success of startups using the rough set theory (Case study: Kerman science and technology park) | |
| Narne | OPTIMIZING SUPPLY CHAIN MANAGEMENT WITH MACHINE LEARNING ALGORITHMS | |
| Kumar et al. | Optimizing Inventory using Ensemble Learning Algorithms in Manufacturing Environment | |
| Nguyen et al. | Digital Strategies for Aiding Ease of Decision-Making in the Services Sector | |
| Tripathy et al. | The Role of Machine Learning Techniques in SCM—An Analysis | |
| Liu | Impact of Digital Transformation of Engineering Enterprises on Enterprise Performance Based on Data Mining and Credible Bayesian Neural Network Model | |
| Schmidt Batista | Machine Learning Models: From Theory to Practical Applications | |
| Agarwal et al. | Applications of Machine Learning Techniques in Supply Chain Management | |
| Kılıç Sarıgül et al. | Increasing load factor in logistics and evaluating shipment performance with machine learning methods: A case from the automotive industry |