Demand Management and Forecasting Program

This DIXONTECH course equips participants with advanced knowledge and analytical tools to manage and forecast demand effectively within complex supply chains. It focuses on integrating forecasting techniques, demand planning processes, and data-driven decision-making to achieve accuracy, efficiency, and agility. The course combines quantitative methods with strategic planning to help professionals align demand forecasts with production, inventory, and distribution objectives.

TRAINING TOPICS

• Principles of demand management and forecasting
• Qualitative and quantitative forecasting models
• Data collection, analysis, and validation
• Demand planning and sales forecasting integration
• Collaborative forecasting and S&OP (Sales & Operations Planning)
• Managing demand variability and uncertainty
• Forecast accuracy measurement and improvement
• Technology and AI applications in demand forecasting
• Best practices and global case studies

BY THE END OF THIS TRAINING COURSE, DELEGATES WILL BE ABLE TO:

• Apply modern forecasting methods effectively
• Integrate forecasting into business planning
• Use data analytics for demand optimization
• Reduce forecasting errors and biases
• Align supply and demand strategies
• Collaborate efficiently across departments
• Utilize AI tools for predictive analysis
• Enhance decision-making through forecast accuracy

TRAINING IS TAILORED TO:

• Demand and supply planners
• Forecasting analysts and business strategists
• Operations and production managers
• Procurement and inventory specialists
• Data and business intelligence professionals
• Sales and operations planning (S&OP) teams
• Logistics and distribution coordinators
• Supply chain consultants and analysts

TRAINING METHODOLOGY:

This DIXONTECH training combines theoretical instruction with practical forecasting simulations. Participants engage in interactive workshops using real datasets, forecasting software, and case-based exercises. The methodology integrates collaborative group work, scenario-based modeling, and AI-supported demand analysis for high-impact learning outcomes.

DAY 1:

INTRODUCTION TO DEMAND MANAGEMENT AND FORECASTING BASICS

• Overview of demand management in supply chains
• The role of forecasting in business performance
• Demand planning frameworks and principles
• Key drivers of demand and market influences
• Forecasting process and life cycle
• Demand management vs. demand fulfillment
• Types of forecasts: operational, tactical, strategic
• Linking forecasting to customer satisfaction
• Global trends in demand planning excellence

DAY 2:

QUALITATIVE AND QUANTITATIVE FORECASTING TECHNIQUES

• Overview of forecasting methods and approaches
• Time series models and moving averages
• Regression and correlation analysis
• Exponential smoothing and seasonal adjustments
• Delphi and expert judgment forecasting methods
• Scenario building and simulation techniques
• Choosing the right forecasting model
• Combining qualitative and quantitative methods
• Case study: applying hybrid forecasting models

DAY 3:

DATA MANAGEMENT AND FORECAST ACCURACY IMPROVEMENT

• Data quality and preprocessing techniques
• Identifying and managing data outliers
• Forecast error analysis (MAD, MAPE, RMSE)
• Bias detection and correction methods
• Statistical tools for forecast improvement
• Integrating ERP and forecasting systems
• Demand sensing and real-time data analytics
• Dashboards and visualization for monitoring
• Group exercise: improving forecast accuracy

DAY 4:

COLLABORATIVE DEMAND PLANNING AND S&OP ALIGNMENT

• Sales and Operations Planning (S&OP) fundamentals
• Demand collaboration across departments
• Aligning marketing, production, and logistics inputs
• Consensus forecasting and stakeholder engagement
• Communication tools for planning synchronization
• Managing forecast uncertainty and variability
• Key metrics for collaborative forecasting success
• Continuous improvement and feedback loops
• Case study: building an integrated S&OP model

DAY 5:

AI, TECHNOLOGY, AND FUTURE TRENDS IN FORECASTING

• Role of AI and machine learning in forecasting
• Predictive analytics for demand prediction
• Cloud-based forecasting and digital platforms
• Integrating IoT data into forecast models
• Automation and cognitive forecasting tools
• Future of demand planning: predictive vs. prescriptive
• Sustainable and resilient demand management
• Building a digital forecasting roadmap
• Final workshop and certification presentation