AI Supply Chain Planning Tools & Techniques Course Overview
This two-day training course introduces the practical application of artificial intelligence in supply chain planning. It explores how AI-powered tools and techniques can improve demand forecasting, inventory optimization, supply planning, production scheduling, logistics coordination, and decision-making.
Participants will examine the data requirements, analytical techniques, implementation considerations, and governance practices associated with AI-enabled planning. Through supply chain scenarios and practical examples, they will learn how to identify suitable AI use cases, interpret AI-generated recommendations, and develop an achievable roadmap for adopting AI within their organizations.
Duration 2 Days – 14 hrs.
Objectives
- Explain the role of artificial intelligence in modern supply chain planning.
- Distinguish AI-enabled planning from traditional rules-based and spreadsheet-driven planning.
- Identify suitable AI use cases across demand, supply, inventory, production, and logistics planning.
- Describe the data requirements for reliable AI-powered supply chain decisions.
- Apply AI-supported techniques for demand forecasting and demand sensing.
- Use planning concepts to optimize inventory and service levels.
- Explain how AI supports supply planning, capacity planning, and production scheduling.
- Interpret AI-generated forecasts, alerts, scenarios, and recommendations.
- Evaluate AI supply chain planning tools using relevant business and technical criteria.
- Recognize common implementation risks, limitations, and governance requirements.
- Define relevant performance indicators for AI-enabled planning.
- Develop a practical roadmap for adopting AI supply chain planning capabilities.
Target Audience
- Supply chain managers and supervisors
- Demand planners and forecasting professionals
- Supply and replenishment planners
- Inventory managers and analysts
- Production planners and schedulers
- Procurement and sourcing professionals
- Logistics and distribution professionals
- Sales and operations planning personnel
- Integrated business planning professionals
- Supply chain analysts and data analysts
- Operations managers
- Enterprise resource planning and planning-system users
- Digital transformation and continuous improvement teams
- Consultants involved in supply chain transformation
- Business leaders responsible for supply chain performance
Prerequisites
- A basic understanding of supply chain processes and terminology.
- General familiarity with demand, supply, inventory, production, or logistics planning.
- Basic spreadsheet and data interpretation skills.
- No programming, data science, or advanced AI experience is required.
Course Outline
Day 1 — AI Foundations, Data, Demand Forecasting, and Inventory Planning
Module 1: Artificial Intelligence in Supply Chain Planning
- Current challenges in supply chain planning
- Traditional planning versus AI-enabled planning
- Artificial intelligence, machine learning, and predictive analytics
- Descriptive, predictive, and prescriptive planning capabilities
- The role of planners in an AI-enabled environment
- Benefits, limitations, and common misconceptions
- High-value AI use cases across the supply chain
Module 2: Supply Chain Data Readiness
- The importance of data in AI-powered planning
- Internal and external supply chain data sources
- Master data, transactional data, and time-series data
- Demand drivers and causal variables
- Data quality, completeness, consistency, and timeliness
- Handling missing values, anomalies, and outliers
- Data integration across enterprise and planning systems
- Establishing a reliable planning-data foundation
Module 3: AI-Powered Demand Forecasting and Demand Sensing
- Foundations of demand forecasting
- Traditional statistical forecasting and machine learning approaches
- Time-series patterns, trends, and seasonality
- Using causal factors and external signals
- Short-term demand sensing
- New-product and intermittent-demand forecasting
- Forecast segmentation and model selection
- Forecast accuracy measures and forecast bias
- Human judgment and AI forecast overrides
- Interpreting forecast confidence and exceptions
Module 4: AI-Enabled Inventory Planning and Optimization
- Balancing inventory cost and customer service
- Inventory classifications and segmentation
- Safety stock and reorder-point concepts
- Service-level optimization
- Multi-echelon inventory considerations
- Slow-moving, excess, and obsolete inventory
- AI-based replenishment recommendations
- Inventory risk alerts and exception management
- Scenario analysis for inventory decisions
Day 2 — Supply, Production, Logistics, Tools, and Implementation
Module 5: AI-Powered Supply and Capacity Planning
- Translating demand forecasts into supply requirements
- Material, supplier, and capacity constraints
- Supplier performance and lead-time prediction
- Supply shortage and disruption-risk identification
- Constrained versus unconstrained planning
- Allocation and prioritization decisions
- AI-supported capacity balancing
- What-if analysis and supply scenarios
- Recommendations for resolving demand-and-supply imbalances
Module 6: Intelligent Production Planning and Scheduling
- Production planning and scheduling fundamentals
- Production sequence and resource optimization
- Bottleneck detection and constraint management
- Changeover and setup-time considerations
- Predictive maintenance inputs for production planning
- Dynamic rescheduling in response to disruptions
- Evaluating AI-generated schedules
- Trade-offs among cost, capacity, service, and efficiency
Module 7: AI in Logistics and Distribution Planning
- Transportation and distribution planning challenges
- Route and load optimization
- Estimated arrival-time prediction
- Warehouse demand and labor planning
- Distribution network inventory positioning
- Carrier selection and logistics-cost analysis
- Disruption monitoring and predictive alerts
- Last-mile delivery planning
- Control towers and end-to-end supply chain visibility
Module 8: AI Supply Chain Planning Tools and Selection Criteria
- Categories of AI-enabled planning tools
- Advanced planning and scheduling platforms
- Enterprise planning and supply chain suites
- Specialized forecasting and optimization tools
- Generative AI assistants for supply chain planning
- Tool capabilities, integration, scalability, and usability
- Cloud-based and on-premises considerations
- Build-versus-buy considerations
- Vendor and solution evaluation criteria
- Selecting tools based on business requirements and maturity
Module 9: Responsible AI, Governance, and Performance Management
- Transparency and explainability in planning decisions
- Data privacy, security, and access control
- Model bias, drift, and declining performance
- Human oversight and approval controls
- Managing planner overrides
- Audit trails and decision accountability
- Supply chain performance indicators
- Measuring forecast, inventory, service, and planning improvements
- Monitoring business value after implementation
Module 10: AI Supply Chain Planning Adoption Roadmap
- Assessing current planning maturity
- Identifying and prioritizing AI use cases
- Defining business problems and expected outcomes
- Evaluating data and technology readiness
- Selecting a pilot area
- Establishing roles, ownership, and governance
- Managing organizational change and user adoption
- Scaling from pilot to enterprise deployment
- Developing a phased AI supply chain planning roadmap
