Enterprise AI Analysis
Research Hotspot and Trend of Artificial Intelligence Applied in Supply Chain Management-Visual Analysis Based on CiteSpace
This paper conducts a comprehensive visual analysis of 435 high-quality literatures from 2014-2024 on the application of Artificial Intelligence (AI) in Supply Chain Management (SCM) using CiteSpace software. It reveals research hotspots, evolutionary trends, and the increasing popularity of this interdisciplinary field. The analysis covers publication trends, key authors, institutions, and keyword co-occurrence to identify critical themes and future directions, emphasizing AI's role in enhancing supply chain resilience and efficiency amidst evolving market challenges.
Executive Impact
Key findings highlight the significant and growing role of AI in modern supply chain management.
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High-Quality Papers Analyzed
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Years of Data Covered
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Growth in last 4 years
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
The research highlights several key hotspots including the integration of big data analytics with AI for performance enhancement, the specific application of AI technologies like machine learning and neural networks in SCM optimization, and the design of novel supply chain frameworks leveraging AI for resilience.
The study identifies three main stages: an initial focus (2014-2016) on AI's technical aspects and system optimization, a second stage (2017-2020) exploring AI's integration with other digital technologies, and a current stage (2021-2024) emphasizing advanced applications like Industry 4.0, supply chain resilience, and predictive analytics in uncertain environments.
Analysis of authors and institutions reveals a decentralized research landscape with limited large-scale collaboration. Indian institutions like IIM System and University of Cambridge are leading in publications. This indicates a need for greater inter-institutional cooperation to foster a more cohesive research community.
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Papers Published in 2024 (projected)
Enterprise Process Flow
Data Collection (2014-2024 WoS)
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Preprocessing & Filtering (435 papers)
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CiteSpace Visual Analysis
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Identify Hotspots & Trends
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Inform SCM AI Strategy
| Topic | Traditional SCM | AI-Enhanced SCM |
|---|---|---|
| Demand Forecasting | Manual, historical data |
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| Inventory Management | Overstock/stock-outs common |
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| Logistics & Routing | Static routes, human planning |
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| Decision Making | Reactive, human-centric |
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AI in Medical Supply Chains
Long et al. (2023) proposed an AI algorithm to enrich medical supply chain research, providing methodological guidance for intelligent decision-making. Shen et al. (2024) established a hospital drug supply chain information management system based on Digital AI + Vendor-Managed Inventory (VMI). This demonstrates how AI can improve supply chain integrity and reduce costs in critical sectors by enabling precise decision-making and efficient operation.
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Your AI Implementation Roadmap
A structured approach to integrate AI and transform your supply chain operations.
Phase 1: Discovery & Strategy
Conduct a thorough assessment of current supply chain processes, identify AI opportunities, define objectives, and develop a tailored AI strategy and roadmap.
Phase 2: Pilot & Development
Develop and pilot AI solutions for high-impact areas, focusing on data integration, model training, and initial deployment. Gather feedback and refine.
Phase 3: Scaling & Optimization
Expand successful AI solutions across the enterprise, integrate with existing systems, and establish continuous monitoring and optimization processes for sustained value.
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