Forecasting and Demand Planning, Best Practice

This DIXONTECH course focuses on mastering forecasting and demand planning best practices to improve decisionmaking, accuracy, and business agility. It blends data analytics, forecasting methodologies, and collaborative planning techniques to create resilient, responsive, and efficient supply chains. Participants gain practical skills in demand forecasting, data modeling, S&OP alignment, and performance evaluation, aligned with global standards and industry benchmarks.

TRAINING TOPICS

• Fundamentals of forecasting and demand planning
• Quantitative and qualitative forecasting methods
• Data analytics and predictive modeling tools
• Sales & Operations Planning (S&OP) alignment
• Demand sensing and realtime data management
• Forecast accuracy measurement and error analysis
• Demand collaboration and communication strategies
• Technology and AI in demand forecasting
• Best practices and global benchmarking

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

• Develop reliable forecasting and planning systems
• Apply best practice forecasting methodologies
• Integrate analytics into decisionmaking
• Improve forecast accuracy and reliability
• Align forecasts with S&OP objectives
• Manage datadriven planning models
• Leverage AI for predictive forecasting
• Enhance collaboration and planning efficiency

TRAINING IS TAILORED TO:

• Supply chain and demand planners
• Forecasting and business analysts
• Sales and operations planning (S&OP) teams
• Production and inventory control managers
• Procurement and logistics professionals
• Data scientists and BI specialists
• Planning and performance managers
• Consultants in supply chain optimization

TRAINING METHODOLOGY:

The DIXONTECH training approach integrates realworld data simulations, casebased forecasting workshops, and analytical exercises. Participants will use practical tools and forecasting models to solve complex scenarios. The course emphasizes collaboration, technology integration, and the use of global case studies to apply best practices effectively.

DAY 1:

INTRODUCTION TO FORECASTING AND DEMAND PLANNING FRAMEWORKS

• Understanding forecasting and its business impact
• The role of demand planning in supply chain success
• Forecasting process and key stakeholders
• Data requirements and preparation steps
• Forecast horizons: short, medium, and long term
• Components of an effective demand plan
• Types of forecasts and applications
• Aligning forecasting with business strategy
• Global best practices in forecasting governance

DAY 2:

FORECASTING METHODS AND TECHNIQUES

• Overview of quantitative and qualitative models
• Time series, regression, and causal models
• Exponential smoothing and trend analysis
• Delphi method and collaborative estimation
• Moving averages and seasonal decomposition
• Model selection and error evaluation
• Combination forecasting and model optimization
• Machine learning and AI in forecasting accuracy
• Case study: comparative model performance

DAY 3:

FORECAST ACCURACY, ERROR ANALYSIS, AND CONTINUOUS IMPROVEMENT

• Measuring forecast performance with KPIs
• Forecast error metrics: MAD, MAPE, RMSE
• Identifying and minimizing forecasting bias
• Implementing continuous improvement in forecasting
• Root cause analysis of forecast deviations
• Tools for data validation and accuracy tracking
• Forecast reconciliation and adjustments
• Feedback loops and performance dashboards
• Group workshop: accuracy improvement exercise

DAY 4:

COLLABORATIVE PLANNING AND SALES & OPERATIONS ALIGNMENT

• Integrating forecasting with S&OP processes
• Role of crossfunctional collaboration in planning
• Consensus forecasting and stakeholder engagement
• Demand review and supply alignment meetings
• Communication and negotiation across departments
• Risk and scenario planning for uncertain demand
• Key metrics for S&OP and forecast alignment
• Case study: collaborationdriven forecast success
• Developing an integrated planning structure

DAY 5:

DIGITAL TRANSFORMATION AND FUTURE TRENDS IN DEMAND PLANNING

• Advanced analytics and predictive planning systems
• Role of AI, machine learning, and IoT in forecasting
• Demand sensing and realtime planning applications
• Cloudbased demand planning platforms
• Automation and digital twins for scenario modeling
• Integrating sustainability in demand planning
• Benchmarking leading global planning practices
• Continuous improvement and planning maturity models
• Final workshop: building a bestpractice forecast plan