Enterprise AI Analysis
Technological Pathways to Low-Carbon Supply Chains: Evaluating the Decarbonization Impact of AI and Robotics
This report provides a comprehensive analysis of the potential of Artificial Intelligence (AI) and robotics to drive decarbonization across global supply chains. It synthesizes findings from 83 peer-reviewed articles (2013-2025), highlighting key applications, impact metrics, and strategic implications for achieving net-zero objectives while enhancing operational efficiency and resilience.
Key Executive Impact Metrics
AI and robotics offer tangible benefits beyond compliance, driving significant operational efficiencies and competitive advantages for forward-thinking enterprises.
Projected CO2 Reduction
Operational Energy Savings
Supply Chain Visibility Enhancement
Waste Reduction Potential
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Transportation & Logistics
Energy-Intensive SCs
Waste Management
Manufacturing SCs
AI-Driven Efficiency in Transportation
Transportation and logistics is the most extensively studied domain (42.2% of studies), focusing on AI-driven route optimization, intelligent scheduling, and intermodal systems to reduce emissions. AI consistently reduces transport emissions by enhancing routing efficiency, load consolidation, and multimodal coordination.
Optimizing Energy-Intensive Supply Chains
Energy-intensive supply chains (33.7% of studies) investigate AI-driven demand forecasting and low-carbon energy transition strategies. This includes sector-specific applications in industries such as oil and gas, aiming to minimize energy consumption and environmental impact.
Robotics in Waste Management & Circular Economy
Waste management and reverse logistics (24.1% of studies) focus on IoT- and AI-enabled automated waste sorting, valorization techniques, and circular economy implementation. Robotics significantly improves sorting accuracy and resource recovery.
Green Transitions in Manufacturing
Manufacturing supply chains (18.1% of studies) emphasize industrial automation and process optimization for enhanced resource efficiency. AI supports predictive maintenance and energy-efficient operations in smart factories.
Enterprise Process Flow: Research Design & Data Collection
Define Research Questions
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Conduct Scopus Search
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Apply Eligibility Criteria
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Assess Study Quality
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Extract & Code Data
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Synthesize Findings
78.3%
of studies utilize AI/Machine Learning/Deep Learning for SCM decarbonization.
Case Study: AI-Driven Route Optimization in Logistics
A study by Juliet (2025) demonstrated a 20-30% reduction in fuel use and delivery time through smart logistics routing optimized by AI. This highlights the direct environmental impact of AI in optimizing critical logistics functions, particularly in high-emission areas like transportation, showcasing its immediate value for decarbonization.
66.3%
of research focuses on Process Optimization strategies for emission reduction.
| Validation Category | Key Characteristics | Prevalence in Studies |
|---|---|---|
| Quantitative Validation |
|
ML Performance: 42.2% Optimization Performance: 30.1% |
| Conceptual/Illustrative Validation |
|
Small-scale Examples: 36.1% Conceptual Validation: 33.7% |
| Empirical/Rigorous Validation |
|
Simulation: 21.7% Experiment: 12.0% |
Case Study: Robotics for Energy-Efficient Warehousing
Research by Iqdymat et al. (2025) reported 5-22% energy savings in pick-and-place operations using reinforcement learning and robotic manipulators. This demonstrates how robotic systems improve energy efficiency and precision in warehouse management, contributing to substantial indirect emission reductions while enhancing throughput.
Calculate Your Potential AI ROI
Estimate the economic benefits of AI and robotics for decarbonization within your enterprise. Adjust the parameters to reflect your organizational context.
Estimated Annual Savings
Annual Hours Reclaimed
Your AI & Robotics Decarbonization Roadmap
A phased approach to integrate AI and robotics for low-carbon supply chains, leveraging proven strategies for sustainable impact.
Phase 1: Data Analytics Foundation
Prioritize AI-enabled data analytics as a foundational capability. Implement predictive analytics, demand forecasting, and route optimization algorithms to reduce energy consumption and carbon emissions, especially in transportation and logistics operations.
Phase 2: Strategic Robotic Automation
Strategically deploy robotic automation in energy-intensive areas like warehousing, order picking, and material handling. Focus on AGVs and mobile robots to deliver measurable energy savings while improving accuracy and throughput.
Phase 3: Digital Ecosystem Integration
Integrate AI and robotics with complementary digital technologies like IoT platforms and blockchain. Enhance real-time visibility of energy usage and emissions, support traceability, supplier compliance, and transparent sustainability reporting.
Phase 4: Human Capital Development
Invest in continuous training programs, cross-functional digital competencies, and change management initiatives. Ensure an inclusive and ethical digital transformation, addressing workforce reskilling and organizational resistance.
Phase 5: Governance & Long-term Strategy
Proactively address governance, cybersecurity, and data-quality challenges. Develop robust data governance frameworks and align decarbonization objectives with business competitiveness for sustained advantage and compliance.
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