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

AI+ Supply Chain Practitioner™

Study Type

Self Paced

No of Exam

1

Modules

8

Exam Time

50 MCQs, 90 Minutes

Passing Score

70% (35/50)

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  • 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

  • Basic understanding of supply chain concepts: Knowledge of core logistics and operations 
  • Familiarity with data analysis tools: Ability to interpret basic reports and dashboards 
  • Introductory AI knowledge: Awareness of fundamental artificial intelligence concepts 
  • Understanding of business operations: Grasp of end-to-end organizational workflows 
  • Spreadsheet proficiency: Comfortable using Excel or similar digital tools

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

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.

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.

Ques:- What are the key AI technologies taught?

Ans:-You’ll learn about predictive analytics, machine learning for demand planning, and AI-driven logistics management.

Ques:- What real-world applications will I explore?

Ans:-You’ll work on projects like optimizing warehouse operations and forecasting supply chain disruptions using AI.

Ques:- What industries benefit most from this course?

Ans:-Retail, manufacturing, and logistics industries can significantly benefit from AI-driven supply chain improvements.

Ques:- What tools will I use in the course?

Ans:-You’ll use tools like AI-based optimization software and predictive analytics platforms.

Ques:- What does the AI+ Supply Chain Practitioner™ course cover?

Ans:-This course teaches how AI can be used to optimize supply chain operations, including demand forecasting and inventory management.

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