Demand Forecasting and Planning

Introduction

Effective demand forecasting and planning are critical components of supply chain management, enabling businesses to efficiently allocate resources, improve customer satisfaction, and increase profitability. This 5-day professional course is designed to equip participants with the skills and knowledge necessary to implement and manage demand forecasting processes, leveraging both traditional and modern forecasting techniques.

Objectives

• Understand the importance and benefits of demand forecasting and planning.
• Learn about various forecasting methodologies and tools.
• Develop skills to analyze demand patterns and trends.
• Explore best practices for integrating demand forecasting into business strategy.
• Apply demand forecasting techniques through practical exercises and case studies.

Course Outlines

Day 1: Introduction to Demand Forecasting

• Definition and importance of demand forecasting.
• Overview of demand forecasting and planning process.
• Types of demand forecasting methods.
• Identifying key data sources for accurate forecasting.
• Introduction to forecasting software and tools.

Day 2: Quantitative Forecasting Techniques

• Understanding time series analysis.
• Simple and weighted moving averages.
• Exponential smoothing techniques.
• Regression analysis for forecasting demand.
• Hands-on exercises with quantitative tools.

Day 3: Qualitative Forecasting Methods and Data Analysis

• Exploratory data analysis techniques.
• Delphi method and its applications.
• Market research and survey methodologies.
• Judgmental forecasting methods.
• Combining qualitative and quantitative forecasts.

Day 4: Demand Planning and Strategy Integration

• Aligning demand planning with business strategy.
• Inventory management and demand forecasting.
• Collaborative planning, forecasting, and replenishment (CPFR).
• Risk management in demand forecasting.
• Case studies on successful demand planning strategies.

Day 5: Advanced Topics and Practical Applications

• Big data and machine learning in demand forecasting.
• Implementing and managing forecasting systems.
• Measuring and improving forecast accuracy.
• Practical exercises and simulation projects.
• Final assessment and course wrap-up.