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Academic Presentation University West, Sweden

Knowledge Management Combining AI & Circular Economy

for Sustainable Supply Chains

Shravan Karkalai Gosala Krishnan
·
Jainullabeddin Nassyam Mohammed

Supply Chain Management · University West

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Background

The Problem with
Linear Supply Chains

Linear Waste Crisis

Traditional supply chains follow a take-make-dispose model, generating massive waste and inefficient resource use.

Climate Pressure

Environmental concerns and resource scarcity are forcing organizations to rethink their operational models urgently.

Circular Economy Rise

Circular models promoting reduce, reuse, and recycle are emerging as the sustainable alternative to linear systems.

AI & Industry 4.0

Digital technologies and AI are transforming supply chain operations, creating new opportunities for sustainability.

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Purpose

Aim of the Study

To explore how AI and Knowledge Management together can drive sustainable supply chains through circular economy integration.

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Examine how AI and KM support sustainable supply chains

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Understand how circular economy capabilities can be improved

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Analyze how digital technologies improve knowledge sharing

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Explore how KM supports resilience, efficiency and innovation

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Theoretical Foundation

Knowledge Management
in Supply Chains

"KM involves creating, sharing, and applying knowledge to improve collaboration, decision-making, and innovation across the supply chain."

Supports coordination between suppliers, manufacturers and customers

Improves decision-making, innovation and operational efficiency

Knowledge sharing strengthens sustainability and resilience

Green knowledge sharing encourages sustainable innovation

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Technology

Role of AI &
Digital Technologies

AI Systems

Improves forecasting, inventory management and logistics optimization

IoT & Blockchain

Enhance traceability, transparency and real-time data across supply chains

Digital Product Passports

Enable sustainability tracking and lifecycle visibility for products

Metaverse & Collaboration

Support virtual collaboration, monitoring and organizational learning

AI-supported systems strengthen organizational learning and adaptive capacity
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Sustainability

Circular Economy
& Sustainability

Reduce
Reuse
Recycle
Circular
Economy
Reverse Logistics

AI and IoT track returned products and recyclable materials efficiently

Resource Efficiency

KM helps organizations adopt circular practices and extend product life cycles

Competitiveness

Circular economy improves long-term sustainability and market position

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Research Design

Methodology

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Literature Selection

Selected recent peer-reviewed academic journal articles focused on AI, KM, circular economy and sustainable supply chains

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Thematic Analysis

Applied qualitative thematic literature review to identify recurring themes and patterns across studies

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KM as Central Lens

Used Knowledge Management as the theoretical framework to connect AI, circular economy and sustainability findings

Qualitative
Secondary Data
Thematic Review
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Results

Key Findings

AI-Supported KM Drives Sustainability

Organizations using AI-supported knowledge management systems show measurably improved sustainability performance and waste reduction.

Enhanced Transparency & Decision-Making

Digital technologies improve real-time visibility and data-driven decisions across the entire supply chain network.

Stronger Resilience & Adaptability

KM-enabled organizations adapt faster to environmental uncertainty and disruptions in global supply chains.

Collaboration Accelerates Circular Practices

Knowledge sharing between supply chain partners significantly improves circular economy implementation outcomes.

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Limitations & Outlook

Challenges &
Future Work

Current Challenges

  • Weak technological infrastructure in many organizations
  • Poor coordination between supply chain stakeholders
  • Limited employee engagement and digital literacy
  • Resistance to organizational change
  • Lack of effective knowledge-sharing systems

Future Research

  • Empirical studies using real company case examples
  • AI implementation challenges in SMEs
  • Industry-specific circular economy applications
  • Long-term impact of AI-driven KM systems
  • Sustainability practices in global supply chains
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Conclusion

Key Takeaways

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KM is critical for sustainable supply chain management and long-term resilience

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AI and digital technologies strengthen knowledge processes and sustainability performance

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Circular economy success depends on collaboration and active knowledge sharing

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Technology alone is insufficient — a strong learning culture is equally essential