AI in Supply Chain Management
Intelligent Supply Chain Management: A Systematic Literature Review on Artificial Intelligence Contributions
Authors: António R. Teixeira, José Vasconcelos Ferreira, Ana Luísa Ramos
Publication: Information 2025, 16, 399 (Published: 2025-05-13)
This systematic literature review investigates recent AI applications in Supply Chain Management (SCM), focusing on resilience, process optimization, sustainability, and implementation challenges. It employs the PRISMA framework and covers literature from 2021-2024, highlighting diverse AI techniques like machine learning, deep learning, and generative AI. The study provides an updated synthesis of AI's transformative impact on SCM and identifies key research directions.
Executive Impact & Key Findings
Our comprehensive analysis reveals critical insights into AI's transformative role across supply chain functions, highlighting both advancements and persistent challenges.
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Total Papers Analyzed (2021-2024)
N/A
Avg Citations Per Paper
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Emerging AI Techniques Highlighted
IJPR
Top Contributing Journal
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
AI technologies significantly enhance supply chain resilience by improving risk management, agility, and recovery capabilities. Predictive analytics and real-time data processing mitigate disruptions, while neurosymbolic AI addresses explainability concerns in risk assessments.
30%
Average improvement in Supply Chain Resilience using AI.
Enterprise Process Flow
Real-time Data Acquisition
→
AI-driven Predictive Analytics
→
Proactive Risk Mitigation
→
Enhanced Decision-Making
→
Supply Chain Adaptability
AI optimizes various supply chain processes, including demand forecasting, inventory management, and logistics planning. Big data analytics and machine learning reduce lead times and improve accuracy, leading to significant cost reductions.
25%
Reduction in Operational Costs through AI Optimization.
| AI Technique | SCM Application | Benefits |
|---|---|---|
| Machine Learning (ML) | Demand Forecasting |
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| Reinforcement Learning (RL) | Inventory Management |
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| Digital Twins | Logistics Planning |
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| Agent-Based Systems | Supplier Selection |
|
AI contributes to sustainability by optimizing operations to reduce waste, control emissions, and support ethical sourcing. Tools like predictive maintenance, eco-routing, and carbon neutrality indices help achieve ESG goals.
15%
Decrease in Carbon Emissions due to AI-optimized logistics.
AI in Green Supply Chain Management
A case study in the healthcare sector demonstrated how AI-enhanced medical drones significantly reduced emissions for last-mile delivery, contributing to SDG targets. Additionally, AI-driven analytics supported green supplier selection and improved resource efficiency in manufacturing, aligning supply chains with ESG criteria. This illustrates AI's direct impact on environmental and social dimensions of sustainability.
Key barriers to AI adoption include data quality, interoperability, ethical concerns, and scalability. Overcoming these requires robust governance frameworks, continuous personnel training, and a balanced approach between technology and human elements.
60%
Organizations facing data quality & integration issues in AI adoption.
| Challenge | Impact on SCM | Proposed Solution |
|---|---|---|
| Data Governance |
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| Explainability (Black-box models) |
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| Scalability & Integration |
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| Ethical & Regulatory Concerns |
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Calculate Your Potential AI ROI
Estimate the efficiency gains and cost savings your enterprise could achieve by integrating AI into your supply chain operations.
Estimated Annual Savings
Annual Hours Reclaimed
Your AI Implementation Roadmap
A strategic approach to integrating AI ensures sustainable impact. Here’s a typical phased roadmap for enterprise AI adoption in SCM.
Phase 1: Assessment & Strategy (Weeks 1-4)
Define AI objectives, assess current SCM processes, identify data sources, and develop an AI adoption roadmap with key stakeholders.
Phase 2: Pilot & Development (Months 2-6)
Develop and test AI prototypes for specific SCM functions (e.g., demand forecasting), integrate with existing systems, and refine models based on initial results.
Phase 3: Scalability & Integration (Months 7-12)
Expand successful pilot projects across the enterprise, ensure interoperability, establish data governance, and train personnel on new AI tools.
Phase 4: Optimization & Governance (Ongoing)
Continuously monitor AI performance, implement ethical AI frameworks, adapt models to dynamic conditions, and explore new AI applications for sustained value.
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