Synapse
⌘+K
Synapse
PulseExploreClubsResearchersJournals
Instagram
HomeClubsExplore
September 16, 2025World Journal of Advanced Research and Reviews

AI Driven Resilience Framework for U.S. Manufacturing Supply Chain Optimization: Bridging technological excellence with intelligent automation and advanced analytics

View Full Paper
Ask AI
Bookmark
Share

Authors

SOSamuel Sunday Omotoso

Discussion

Loading...

Member takes

Implication

Simulation experiments show a 40% reduction in recovery time in supply chains, highlighting AI's role in optimization.

Key Points

  • Simulation findings reveal deep reinforcement learning reduces recovery time by nearly 40%, enhancing efficiency.
  • Long short-term memory networks achieved superior forecasting accuracy compared to random forest and XGBoost models.
  • Integration of digital thread architectures improved supply chain visibility and coordination by over 25 points on average.
  • AI-driven resilience frameworks could support reshoring strategies and strengthen U.S. supply chain readiness.

Cite This Study

Samuel Sunday Omotoso (2025) studied this question.

synapsesocial.com/papers/68d4565b31b076d99fa5b46dhttps://doi.org/10.30574/wjarr.2025.27.3.3113
View Full Paper
Ask AI
Bookmark
Share

Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Predictive Supply Chain Analytics: MIS-Integrated AI Models for U.S. Manufacturing Resilience2025 · 1 citations
  2. 2ENHANCING SUPPLY CHAIN RESILIENCE THROUGH AI-DRIVEN PREDICTIVE ANALYTICS2026
  3. 3AI-powered risk mitigation and organizational sensemaking for supply chain resilience2026 · 4 citations
  4. 4End-to-end supply chain resilience management using deep learning, survival analysis, and explainable artificial intelligence2024 · 78 citations
  5. 5The Impact of Artificial Intelligence on Supply Chain Resilience: A Study of AI-Based Demand Forecasting and Inventory Optimization2026