Day 1:
Introduction to Sales Forecasting
Fundamentals of sales forecasting and its strategic importance.
Overview of qualitative vs. quantitative forecasting methods.
Identifying key sales drivers and performance indicators.
Common challenges in sales forecasting and how to overcome them.
Day 2:
Data-Driven Forecasting Techniques
Time-series analysis and trend forecasting.
Regression models and correlation analysis for sales predictions.
AI and machine learning applications in sales forecasting.
Leveraging CRM and business intelligence tools for forecasting.
Day 3:
Sales Planning and Strategy Development
Developing a structured sales planning framework.
Setting sales targets, quotas, and key performance indicators (KPIs).
Aligning sales strategies with business objectives.
Risk assessment and contingency planning in sales.
Day 4:
Demand Planning and Inventory Management
Understanding demand fluctuations and seasonal trends.
Integrating sales forecasts with supply chain management.
Optimizing inventory levels to meet customer demand.
Case studies on successful demand planning strategies.
Day 5:
Advanced Sales Forecasting and Adaptability
Scenario analysis and forecasting under uncertainty.
Adapting sales strategies to market shifts and economic changes.
Measuring and improving forecast accuracy.
Final project: Developing a sales forecasting and planning model.
