AI Essentials of Supply Chain Planning Course Overview
The AI Essentials of Supply Chain Planning Training Course provides participants with a practical introduction to applying artificial intelligence in supply chain planning. It explains how AI can support demand forecasting, inventory optimization, supply planning, production planning, logistics, risk management, and decision-making.
Participants will explore essential AI concepts, common business applications, data requirements, implementation considerations, and responsible-use principles. The course focuses on business understanding and practical application rather than advanced programming or complex mathematical models.
Duration 2 Days – 14 hrs.
Objectives
- Explain the fundamental concepts of artificial intelligence and machine learning.
- Identify suitable AI applications across the supply chain planning process.
- Describe how AI improves demand forecasting and demand sensing.
- Explain the role of AI in inventory, supply, production, and capacity planning.
- Recognize the importance of accurate, complete, and reliable supply chain data.
- Use AI-supported insights to improve planning decisions and business responsiveness.
- Identify opportunities for automating routine planning and analysis activities.
- Evaluate the business value and operational feasibility of proposed AI use cases.
- Recognize common risks, limitations, ethical concerns, and governance requirements associated with AI.
- Develop a practical roadmap for adopting AI in supply chain planning.
Target Audience
- Supply chain managers and supervisors
- Demand planners and forecasting professionals
- Supply and replenishment planners
- Inventory planners and analysts
- Production and capacity planners
- Sales and operations planning professionals
- Integrated business planning professionals
- Procurement and sourcing professionals
- Logistics and distribution professionals
- Supply chain analysts and business analysts
- Operations managers
- Digital transformation and process-improvement teams
- Enterprise resource planning system users and functional consultants
- Professionals involved in supply chain technology selection or implementation
Prerequisites
- Participants should have a basic understanding of supply chain operations or planning processes. Familiarity with demand planning, inventory management, procurement, production, or logistics is helpful but not mandatory. No previous artificial intelligence, machine learning, programming, or data science experience is required.
Course Outline
Day 1 — AI Foundations and Demand-Driven Planning
Module 1: Introduction to Artificial Intelligence in Supply Chain Planning
- Meaning and scope of artificial intelligence
- AI, machine learning, deep learning, generative AI, and automation
- Traditional analytics compared with AI-driven planning
- Evolution of supply chain planning technologies
- Benefits and limitations of AI
- Common AI applications across the supply chain
- Human expertise and AI-supported decision-making
Module 2: Supply Chain Planning Fundamentals
- Overview of end-to-end supply chain planning
- Strategic, tactical, and operational planning
- Demand, supply, inventory, production, and distribution planning
- Sales and operations planning
- Integrated business planning
- Planning horizons and decision levels
- Common supply chain planning challenges
- Sources of uncertainty, variability, and disruption
Module 3: Data Foundations for AI-Enabled Planning
- Importance of data in AI applications
- Internal and external supply chain data
- Structured and unstructured data
- Master data and transactional data
- Historical sales, inventory, supplier, production, and logistics data
- Data quality, completeness, accuracy, consistency, and timeliness
- Data integration across supply chain systems
- Identifying and addressing common data problems
- Data ownership, security, privacy, and governance
Module 4: AI in Demand Forecasting and Demand Sensing
- Traditional forecasting and its limitations
- Machine-learning approaches to forecasting
- Demand patterns, trends, seasonality, and variability
- Use of external demand signals
- Short-term demand sensing
- Forecast accuracy and forecast-bias measures
- New-product and intermittent-demand forecasting
- Scenario-based demand planning
- Human judgment and AI-generated forecasts
- Managing forecast exceptions
Module 5: AI in Inventory and Replenishment Planning
- Inventory planning objectives
- Balancing service levels, cost, and working capital
- AI-supported safety-stock optimization
- Reorder-point and replenishment recommendations
- Multi-echelon inventory concepts
- Inventory segmentation and prioritization
- Detecting slow-moving, excess, and obsolete inventory
- Managing stockout and overstock risks
- Responding to changing lead times and demand patterns
Day 2 — AI Applications, Governance, and Adoption
Module 6: AI in Supply, Production, and Capacity Planning
- AI-supported supply planning
- Material and resource requirements
- Production scheduling and sequencing
- Capacity planning and constraint identification
- Supplier lead-time and performance prediction
- Allocation during supply shortages
- What-if analysis and scenario planning
- Balancing cost, service, capacity, and inventory
- Planner review and approval of AI recommendations
Module 7: AI in Procurement, Logistics, and Distribution Planning
- Supplier selection and performance monitoring
- Purchase-price and lead-time analysis
- Supplier-risk identification
- Transportation demand and capacity forecasting
- Route and load optimization concepts
- Warehouse labor and workload planning
- Distribution network planning
- Estimated time of arrival prediction
- Shipment-delay and disruption alerts
- Logistics cost and service optimization
Module 8: Generative AI for Supply Chain Planners
- Generative AI capabilities and limitations
- Using natural-language prompts effectively
- Summarizing planning reports and operational data
- Drafting supply chain communications
- Generating scenario descriptions and management summaries
- Supporting root-cause analysis
- Creating standard operating procedures and planning documents
- Asking AI to explain trends, exceptions, and recommendations
- Validating AI-generated content
- Protecting confidential and sensitive business information
Module 9: AI for Risk Management and Supply Chain Resilience
- Sources of supply chain risk
- Predictive risk monitoring
- Early-warning indicators
- Supplier, inventory, production, and logistics risks
- Disruption detection and impact analysis
- Scenario modeling and contingency planning
- Prescriptive recommendations and response options
- Building resilient and responsive supply chains
- Limits of AI during unexpected events
Module 10: Responsible AI, Governance, and Controls
- Responsible and ethical use of AI
- Accuracy, transparency, explainability, and accountability
- Bias in data and AI recommendations
- Data privacy and cybersecurity considerations
- Human oversight and decision rights
- Validation and monitoring of AI models
- Managing errors, hallucinations, and unreliable outputs
- AI policies, standards, and approval controls
- Regulatory and organizational considerations
- Building trust in AI-supported planning
Module 11: Evaluating AI Use Cases and Business Value
- Identifying suitable planning problems for AI
- Distinguishing AI opportunities from conventional automation
- Assessing data readiness
- Evaluating operational and technical feasibility
- Defining expected business benefits
- Relevant supply chain performance indicators
- Cost, value, risk, and implementation considerations
- Prioritizing high-impact and achievable use cases
- Defining success criteria
Module 12: Developing an AI Adoption Roadmap
- Assessing current supply chain planning maturity
- Identifying planning pain points and opportunities
- Selecting an initial AI use case
- Establishing roles and responsibilities
- Preparing data, processes, people, and technology
- Starting with pilot projects
- Managing organizational change
- Developing AI skills and planner capabilities
- Scaling successful applications
- Creating a practical AI adoption roadmap
