Enterprise AI Analysis
Analyzing Enablers of Artificial Intelligence for Decarbonization: Implications for Circular Supply Chains
Shefali Srivastava, Vipulesh Shardeo, Ashish Dwivedi, Sanjoy Kumar Paul
Published: 14 October 2025
Executive Impact: AI for a Sustainable Enterprise
This study highlights the pivotal role of Artificial Intelligence in driving decarbonization within Circular Supply Chains (CSCs). Our findings offer a strategic roadmap for businesses aiming to reduce their environmental footprint and enhance operational efficiency through advanced AI integration.
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Decarbonization Market Size (2022)
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Market CAGR (2023-2030)
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Supply Chain Carbon Emissions
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AI Carbon Reduction Potential
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Total Variance Explained by Categories
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Top Enablers Identified
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Environmental Focus
These enablers focus on reducing negative environmental impacts within CSCs. They promote the use of sustainable materials, waste reduction, and efficient resource management through smart technologies, directly contributing to decarbonization goals.
Organizational Focus
These enablers emphasize internal organizational structures and practices. They include strong management support, online monitoring for quality, and efficient inventory systems, all crucial for successfully integrating circularity into business operations.
Institutional Focus
These enablers relate to external and internal governance frameworks. They cover modular architecture for supply chain compatibility, clear guidelines for technology transfer, robust top management and government support, and effective e-governance for transparency.
Technological Focus
These enablers involve the direct application of advanced technologies to drive decarbonization. They include standardized interfaces, ubiquitous network technologies for logistics, intelligent manufacturing for product lifecycle, and hyper-intelligent sorting systems for procurement.
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Ranked Enabler: Adopting recyclable materials to enhance supply chain efficiency (E4)
The study highlights E4, 'Adopting recyclable materials to enhance the efficiency of supply chains,' as the most crucial enabler. This directly supports circularity by reducing reliance on virgin materials, minimizing waste, and significantly lowering carbon emissions across the supply chain lifecycle.
Enterprise Process Flow: Methodology Framework
Literature Review
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Exploratory Factor Analysis (EFA)
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Categorization of Enablers
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Grey-Ordinal Priority Approach (G-OPA)
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Local and Global Weight Determination
| Enabler Code | Enabler Description | Global Weight | Rank |
|---|---|---|---|
| E4 | Adopting recyclable materials to enhance the efficiency of supply chains | 0.237 | 1 |
| E3 | Emphasizing local production for recovery practices through advanced technology | 0.106 | 2 |
| E7 | Managing product life-cycle through intelligent and additive manufacturing technologies | 0.090 | 3 |
| E8 | Optimizing sourcing and procurement processes through hyper-intelligent sorting systems | 0.088 | 4 |
| E9 | Provisions for regular value assessments for used and recycled products | 0.085 | 5 |
| E14 | Facilitating waste reduction by adapting smart technologies | 0.083 | 6 |
AI in Action: Optimizing Circularity & Decarbonization
AI-driven technologies are crucial for optimizing resource use and improving process efficiencies within Circular Supply Chains. For instance, AI algorithms can analyze real-time data to alleviate traffic congestion, optimize energy consumption in cooling services, and improve decision-making by combining operational data with maintenance records. Furthermore, AI helps in sorting plastics and enhances knowledge of recycled materials using multi-sensor data fusion, directly contributing to waste reduction and resource efficiency, thereby enabling a more sustainable supply chain ecosystem.
Calculate Your Potential AI-Driven Savings
Estimate the impact of AI implementation on your operational efficiency and cost reduction based on our research findings.
Estimated Annual Cost Savings
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Annual Man-Hours Reclaimed
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Your AI Implementation Roadmap
A phased approach to integrating AI for decarbonization in your Circular Supply Chains, based on the study's insights.
Phase 1: Identify & Assess AI Enablers
Conduct thorough literature reviews and expert consultations to identify AI enablers specific to your supply chain context and decarbonization objectives.
Phase 2: Categorize & Prioritize Enablers
Utilize advanced analytical methods like EFA and G-OPA to categorize and rank the identified enablers based on their influence and importance for achieving decarbonization and circularity.
Phase 3: Integrate AI Technologies
Implement top-priority AI-driven solutions such as hyper-intelligent sorting systems, intelligent additive manufacturing, real-time data analytics, and predictive maintenance tools.
Phase 4: Foster Organizational Alignment
Secure top management support, align corporate strategies with sustainability goals, and engage employees at all levels to drive successful AI adoption and circular practices.
Phase 5: Develop Supportive Policies
Collaborate with policymakers to advocate for regulations mandating circular practices, lifecycle emission tracking, and waste reduction strategies, creating an enabling environment for CSCs.
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