AI+ Supply Chain Practitioner™

AI+ Supply Chain Practitioner™

Formerly known as AI+ Supply Chain™ <br> <br> Transforming Supply Chain Management

Certificate Code: AP-710

About This Course

  • Comprehensive Learning: Covers logistics, operations, and supply chain digitization  
  • Advanced Supply Strategies: Develop innovative supply strategies and workflows
  • Sector-Specific Solutions: Tailored sessions for real-world, sector-specific challenges
  • Lead AI Supply Efficiency: Prepares learners to lead in AI-led supply chain efficiency

Certificate Overview

Included

Instructor-led OR Self-paced course + Official exam + Digital badge

Duration

  • Instructor-Led: 1 day (live or virtual)
  •  Self-Paced: 8 hours of content

Prerequisites

Foundational knowledge of supply chain, Prior experience with business management or technical tools, such as ERP systems or data analysis software, will be beneficial.

Exam Format

50 questions, 70% passing, 90 minutes, online proctored exam

Course Modules

1

Module 1: Fundamental Concepts of Supply Chain Management

  1. 1.1 SCOR Model and Core Processes (Plan, Source, Make, Deliver, Return, Enable)
  2. 1.2 Key Functions: Procurement, Inventory Management, Logistics, Warehousing, Demand Forecasting, Risk, and Resilience
  3. 1.3 Global Challenges: Volatility, Sustainability, Nearshoring, and ESG
  4. 1.4 KPIs and Performance Measurement
  5. 1.5 Activity: Analyze and Map a Real-World Supply Chain
2

Module 2: AI Concepts, Techniques, and Tools for SCM

  1. 2.1 AI/ML Fundamentals – Supervised & Unsupervised Learning, Predictive & Prescriptive Analytics, Optimization, Reinforcement Learning
  2. 2.2 Key Techniques – Neural Networks, Computer Vision, NLP, Digital Twins, Edge AI
  3. 2.3 AI Tools for SCM
  4. 2.4 Data Foundations – IoT, Real-Time Data Pipelines, Data Quality & Governance
3

Module 3: LLM and Generative AI Applications in SCM

  1. 3.1 LLM/GenAI Fundamentals and Enterprise Integration
  2. 3.2 Use Cases – Demand Planning Assistance, Contract Analysis, Supplier Communication, Scenario Simulation, Report Generation, Synthetic Data
  3. 3.3 Chat-Based Copilots for Planners and Knowledge Management
  4. 3.4 Limitations and Best Practices (Hallucinations, Grounding, Integration)
  5. 3.5 Tools – Enterprise GPT-like Models, LangChain/LlamaIndex, Amazon Business Assistant, Custom GenAI Workflows
4

Module 4: Ethical Considerations and Responsible AI in SCM

  1. 4.1 Bias in Forecasting/Procurement, Transparency, and Explainability
  2. 4.2 Privacy, Security, Regulatory Compliance
  3. 4.3 Job Displacement, Upskilling, and Human-AI Collaboration
  4. 4.4 Sustainability & ESG – AI for Ethical Sourcing and Carbon Tracking
  5. 4.5 Governance Frameworks and Risk Management
5

Module 5: Supply Chain Digitization, Orchestration, and Intelligent Systems

  1. 5.1 Digitization – ERP + SCM Platforms, Cloud Integration, Blockchain for Traceability, APIs
  2. 5.2 Orchestration – Control Towers, Real-Time Visibility, Data Pipelines, Digital Twins
  3. 5.3 Intelligent & Smart SCM – Predictive/Prescriptive Analytics, Autonomous Exception Management, Robotics + Computer Vision, Edge AI
  4. 5.4 Human + AI Collaboration Models
6

Module 6: Industrial Applications, Case Studies, and Business Value

  1. 6.1 Applications Across Industries
  2. 6.2 Real-World ROI – Efficiency Gains, Cost Reduction, and Resilience Improvements
  3. 6.3 Implementation Best Practices
  4. 6.4 Case Studies from Blue Yonder, Kinaxis, Oracle, and Others
7

Module 7: Strategic SCM, Logistics Policies, and Sustainability

  1.  7.1 Logistics Policies, Trade Regulations, Tariffs, and Geopolitical Risks
  2. 7.2 Strategic Network Design: Optimization, Resilience, Nearshoring, and Friendshoring
  3. 7.3 Sustainable SCM: Circular Economy, Green Logistics, and AI-Driven ESG Reporting
  4. 7.4 Organizational Transformation and Leadership in AI-Enabled Supply Chains
  5. 7.5 Case Studies
8

Module 8: Agentic AI and the Future of Autonomous Supply Chains

  1. 8.1 Agentic AI Concepts: Autonomous Goal-Oriented Agents, Multi-Agent Systems, and Reasoning-Action Loops
  2. 8.2 Applications: Autonomous Replenishment, Risk Mitigation, Supplier Onboarding, Dynamic Rerouting, and End-to-End Orchestration
  3. 8.3 Tools & Platforms: Kinaxis Maestro Agents, Oracle AI Agents, Blue Yonder Cognitive Agents, Custom Builds, and Automation Anywhere
  4. 8.4 Architectures, Guardrails, and Human Oversight
  5. 8.5 Future Outlook for 2026+: From Copilots to Semi-Autonomous Operations
  6. 8.6 Capstone Project: Design and Prototype a Multi-Agent Workflow for a Supply Chain
  7. 8.7 Case Studies
9

Optional Module: AI Agents in Supply Chain

  1. 1. What Are AI Agents
  2. 2. What Are AI Agents in Logistics and Supply Chain
  3. 3. Applications & Trends of AI Agents in Supply Chain
  4. 4. How Does an AI Agent Work
  5. 5. Core Characteristics of AI Agents
  6. 6. Key Advantages of AI Agents in Logistics and Supply Chain
  7. 7. Types of AI Agent
  8. 8. Case Studies
  9. 9. Hands on experiment

AI Tools You'll Learn

LeewayHertz (ZBrain)

LeewayHertz (ZBrain)

C3.ai

C3.ai

Coupa (LLamasoft)

Coupa (LLamasoft)

Zebra (Workcloud Demand Intelligence Suite)

Zebra (Workcloud Demand Intelligence Suite)