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