AI-Driven Supply Chain Resilience

• AI-driven supply chain resilience is an integrated system combining IoT, blockchain, digital twins, and LLMs to enhance real-time disruption management.
• The framework employs layered architectures, anomaly detection, and automated decision-making to improve recovery speed and mitigate risks.
• Empirical studies confirm that integrating these technologies boosts operational visibility, reduces recovery times, and enhances overall supply network coordination.

AI-driven supply chain resilience (SCRES) is the property of a supply chain system to anticipate, withstand, adapt to, and rapidly recover from disruptions by leveraging real-time data, intelligent decision-making, and digital twin simulation technologies. The integration of artificial intelligence, machine learning, IoT, blockchain, LLMs, and immersive simulation platforms (such as the Metaverse) fundamentally changes the design, monitoring, and dynamic control of complex supply networks.

1. Conceptual Foundations and Core Architectures

The latest frameworks delineate SCRES as a layered stack that combines operational technology, data provenance, AI-driven orchestration, and immersive visualization (Sarhir, 1 Jan 2025). The canonical architecture comprises:

• Physical layer: IoT sensors and edge devices for real-time state extraction.
• Data and blockchain: Tamper-proof ledgers for distributed data auditing.
• Computing and connectivity: Scalable cloud/edge compute to accommodate streaming data and computationally intensive analytics.
• 3D Metaverse simulation: Digital twins providing a continuous, interactive, and scenario-rich visualization of the network.
• NLP/decision-support (e.g., ChatGPT): Large models trained on domain-specific data for summarizing KPIs, generating risk alerts, translating technical metrics to actionable recommendations, and facilitating natural language interactions.
• User interfaces: VR/AR dashboards, chatbots, and mobile/web portals.

Traditional ERP, MES, and digital-twin modules are augmented by these AI and simulation layers, creating a tightly coupled system that can inject, simulate, and respond to disruption events in real time.

2. Methodological Approaches and Key Metrics

AI-driven SCRES systems operationalize resilience using a set of interlocking mathematical and algorithmic approaches:

Resilience Metrics:

• Resilience index: $$R = \frac{T_\mathrm{recovery}}{T_\mathrm{disruption}}$$.
• Node risk: $R_i = 1 - \exp(-\lambda_i \Delta t)$, with $\lambda_i$ as the historical disruption rate.
• Network robustness: $$\phi = 1 - \frac{|\mathcal{C}_\mathrm{max}|}{|\mathcal{N}|}$$.
• Aggregate SCRES: $$SCRES = w_1 R + w_2 \overline{R_i} + w_3 \phi,\ \sum w_j = 1$$.

System workflow (event-driven pseudocode):

loop: ingest real-time IoT data update digital twin state if anomaly detected (e.g., lead time jump): simulate disruption in Metaverse ChatGPT generates mitigation plan notify stakeholders via AR/VR and chatbots upon user approval: actuate recovery commands monitor system recovery

(Sarhir, 1 Jan 2025)

The methodology combines real-time anomaly detection, dynamic scenario simulation, cross-stakeholder communication, and closed-loop actuation.

3. Criteria, Maturity Models, and Prioritization

An essential element of SCRES is the formal assessment and prioritization of resilience capabilities. Using Analytic Hierarchy Process (AHP) and Fuzzy Nonlinear AHP, critical criteria include:

• Visibility (real-time end-to-end tracking)
• Agility (reconfiguration speed)
• Technological integration (adoption of IoT, AI, and blockchain)
• Flexibility (multi-sourcing, modularity)
• Risk management (scenario planning, prediction)
• Transparency is the most highly weighted factor per AIoT-based assessments (Aliahmadi et al., 2022).

Maturity is quantitatively scored via G-TOPSIS, with "Technological Proficiency" (score: 0.787) and "Agility & Responsiveness" (score: 0.651) as the high-impact axes for improving resilience (Sarhir, 1 Jan 2025).

Table: Key SCRES Criteria and Top Maturity Factors

SCRES Criterion	Description	Weight
Transparency	End-to-end real-time visibility	0.145
Power of Prediction	Forecasting and anomaly detection	0.133
Risk Management	Scenario planning and control	0.111
Flexibility	Dynamic reconfiguration	0.109

4. Empirical Evidence and Performance Benchmarks

Recent simulation pilots demonstrate measurable improvements:

• Case: Port closure simulation (Sarhir, 1 Jan 2025)
  • Detected a 47% spike in lead time.
  • Metaverse simulation proposed rerouting via rail.
  • Projected recovery time reduced by 28%.
  • Average resilience index $R$ improved from 1.8 to 1.2 (20% faster recovery).
  • Risk index $\overline{R_i}$ reduced by 15%.
  • Planners reported 85% increase in situational awareness.
  • ChatGPT-assisted reporting reduced time by 60%.

AIoT prioritization simulations (Aliahmadi et al., 2022) and SD models (Hu, 30 Mar 2024) confirm that technology layers delivering visibility, prediction, and risk response are critical for accelerated recovery and minimized operational downtime.

5. Best Practices, Limitations, and Deployment Considerations

Best practices include:

• Initial pilot deployments at regional scale before global expansion to manage complexity.
• Tailored training of LLMs like ChatGPT on proprietary supply chain terms and operational dialects.
• Continuous adjustment of anomaly detection thresholds to mitigate alert fatigue.
• Governance structures for data privacy, output accuracy, and incident auditability.

Limitations:

• Metaverse 3D rendering can exhibit latency under heavy IoT workloads.
• ChatGPT and similar LLMs are prone to hallucination or erroneous outputs if not anchored to verifiable live data.
• Integration with legacy ERP systems remains a challenge.
• Collaborative virtual environments expose new cybersecurity risks.

Deployment:

• Secure, permissioned data pipelines (MQTT, Kafka) feed model and simulation layers.
• Blockchain ensures audit trails and trust among ecosystem participants.
• VR/AR interfaces and chatbots enable both operational and managerial integration.

Research trends indicate movement toward hybrid digital twin architectures (combining classical and generative AI simulation), real-time federated learning for ongoing LLM adaptation, and prescriptive reinforcement learning analytics for semi- or fully-automated response execution (Sarhir, 1 Jan 2025).

6. Synthesis and Future Directions

AI-driven supply chain resilience now encompasses a stack of digital and cyber-physical technologies, with a central role for simulation, prediction, and natural language–driven orchestration. The convergence of immersive Metaverse systems, LLMs, and AIoT infrastructure delivers demonstrable gains in agility, visibility, robustness, and recovery speed.

Emerging focus areas are:

• Deep integration of economic, environmental, and social sustainability dimensions into quantitative resilience indices.
• Online learning capabilities for LLMs and twins (e.g., federated or continual learning).
• Automated, data-driven governance via blockchain smart contracts and transparent audit trails.
• Cross-disciplinary expansion: further blending of AI, operations research, human factors, and regulatory compliance for full-spectrum resilient supply chains.

The strict prioritization of transparency, predictive analytics, and risk management enables organizations to transition from reactive crisis management to proactive, adaptive, and quantifiably resilient supply chain operations.