AI+ Supply Chain Practitioner™

Certificate Code: AP-710

Passing Score: 70% (35/50)

Exam Info: 50 MCQs, 90 Minutes

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

Course Overview:

Prerequisites:

Tools Used:

• LeewayHertz (ZBrain)
• C3.ai
• Coupa (LLamasoft)
• Zebra (Workcloud Demand Intelligence Suite)

Modules:

• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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

What You’ll Learn:

• Supply Chain Digitization — Learners will gain skills in applying AI to digitize and automate supply chain operations, enhancing overall efficiency and enabling data-driven decision-making.
• AI for Logistics Management — Expertise in integrating AI to enhance logistics planning, warehousing, and transportation, leading to streamlined operations and cost reduction.
• Smart Supply Chain Management (SCM) — Learners will acquire knowledge of intelligent SCM systems powered by AI, enabling real-time monitoring, automation, and optimization of supply chain functions.
• AI-Driven Supply Chain Optimization — Ability to implement AI techniques such as machine learning and predictive analytics to optimize supply chain processes, including demand forecasting, inventory management, and logistics.

Career Opportunities:

• Supply Chain Automation Specialist — Focuses on automating supply chain functions such as procurement, logistics, and inventory management through AI-powered tools.
• AI Supply Chain Strategist — Develops and implements AI-driven strategies to improve supply chain efficiency, cost-effectiveness, and resilience.
• Supply Chain Data Scientist — Utilizes AI and data analytics to gather insights from supply chain data, predicting trends, optimizing performance, and solving operational challenges.
• AI Procurement Specialist — Use AI tools to improve procurement strategies by optimizing supplier evaluation and reducing costs effectively.

Exam Blueprint:

• Fundamental Concepts of Supply Chain Management - 7%
• AI Concepts, Techniques, and Tools for SCM - 15%
• LLM and Generative AI Applications in SCM - 15%
• Ethical Considerations and Responsible AI in SCM - 15%
• Supply Chain Digitization, Orchestration, and Intelligent Systems - 12%
• Industrial Applications, Case Studies, and Business Value - 12%
• Strategic SCM, Logistics Policies, and Sustainability - 12%
• Agentic AI and the Future of Autonomous Supply Chains - 12%

Self-Study Materials:

• Videos: Engaging visual content to enhance understanding and learning experience.
• Podcasts: Insightful audio sessions featuring expert discussions and real-world cases.
• Audiobooks: Listen and learn anytime with convenient audio-based knowledge sharing.
• E-Books: Comprehensive digital guides offering in-depth knowledge and learning support.
• Hands-on: Practical experience through real-world exercises, case studies, and applied learning.
• Module Wise Quizzes: Interactive assessments to reinforce learning and test conceptual clarity.
• Additional Resources: Supplementary references and list of tools to deepen knowledge and practical application.

Frequently Asked Questions:

• Q: What are the key AI technologies taught?
  A: You’ll learn about predictive analytics, machine learning for demand planning, and AI-driven logistics management.
• Q: What real-world applications will I explore?
  A: You’ll work on projects like optimizing warehouse operations and forecasting supply chain disruptions using AI.
• Q: What industries benefit most from this course?
  A: Retail, manufacturing, and logistics industries can significantly benefit from AI-driven supply chain improvements.
• Q: What tools will I use in the course?
  A: You’ll use tools like AI-based optimization software and predictive analytics platforms.
• Q: What does the AI+ Supply Chain Practitioner™ course cover?
  A: This course teaches how AI can be used to optimize supply chain operations, including demand forecasting and inventory management.

Certificate Features:

• High-Quality Video, E-book & Audiobook
• Modules Quizzes
• AI Mentor
• Access for Tablet & Phone
• Online Proctored Exam with One Free Retake
• Hands-on Practices