Training course on AI-Powered Supply Chain Visibility

Module 1: The Evolution of Supply Chain Visibility and the Role of AI
Overview:
Exploring the transformation of supply chains from linear models to digital, data-driven ecosystems.
Key Focus Areas:
The concept of supply chain visibility and its business impact
Evolution from traditional to intelligent supply chains

Key enablers: AI, IoT, cloud computing, and blockchain
Challenges in achieving real-time visibility
Global trends and benchmarks in AI-powered logistics
Learning Outcome:
Participants will understand the strategic importance of AI in enhancing transparency and responsiveness across the supply chain.

Module 2: Data Integration and Real-Time Monitoring
Overview:
Building a unified data infrastructure for continuous visibility and decision-making.
Key Focus Areas:
Integrating ERP, WMS, TMS, and IoT systems for unified visibility
Real-time tracking using IoT sensors and telematics
Data lakes and APIs for supply chain data consolidation
Edge computing for real-time decision support
Case study: Building a live supply chain control tower
Learning Outcome:
Participants will learn how to aggregate and analyze data from multiple sources to achieve seamless real-time supply chain monitoring.

Module 3: Predictive Analytics and AI Models for Supply Chain Optimization
Overview:
Applying machine learning and predictive models to enhance forecasting, inventory control, and logistics efficiency.
Key Focus Areas:
Predictive demand forecasting using AI
Inventory optimization through ML algorithms
AI in route planning and logistics automation
Scenario modeling using digital twins
Predictive maintenance and risk detection in supply chains
Learning Outcome:
Participants will be able to deploy predictive models to anticipate disruptions, forecast demand, and optimize operations.

Module 4: Supply Chain Risk Management and Anomaly Detection with AI
Overview:
Utilizing AI for identifying, assessing, and mitigating risks across complex supply networks.
Key Focus Areas:
Building AI-driven risk models
Using natural language processing (NLP) for market intelligence
Detecting anomalies in supply chain operations
Early warning systems for disruptions
Case study: AI-based supply chain risk prediction
Learning Outcome:
Participants will understand how to use AI to improve supply chain resilience and preempt potential disruptions.

Module 5: Implementing AI-Powered Visibility Solutions
Overview:
Developing an AI-driven visibility roadmap and ensuring ethical, secure, and scalable deployment.
Key Focus Areas:
Designing a digital transformation strategy for AI in supply chains
Selecting appropriate AI tools and platforms
Data governance, security, and ethical considerations
Measuring ROI and performance impact
Building the future-ready intelligent supply chain
Learning Outcome:
Participants will gain the ability to design, implement, and manage an AI-powered visibility strategy aligned with organizational objectives.
Practical Activities and Case Studies
Simulation: Setting up an AI-based supply chain dashboard
Group exercise: Designing a predictive analytics model for demand planning
Case study: Real-world implementation of AI-powered logistics visibility
Workshop: Risk detection using anomaly detection algorithms
Discussion: Ethical challenges in data-driven global supply chain