Demand Forecasting and Planning
Unit Outlines

Demand Forecasting And Planning

AI Generated Intermediate 30 hours 10 topics

Learning Objectives

5 objectives
  • Understand the fundamental concepts and significance of demand forecasting in supply chain management.
  • Explore and differentiate between qualitative and quantitative demand forecasting methods and techniques.
  • Analyze factors influencing demand and assess their impact on forecasting accuracy.
  • Apply forecasting accuracy metrics and evaluate forecast errors to improve prediction reliability.
  • Examine the role of collaborative approaches, technology, and inventory management in enhancing demand planning.

Content Outline

Preview

Unit 981: Demand Forecasting and Supply Chain Planning

1. Introduction to Demand Forecasting

  • Importance of demand forecasting in supply chain management
  • Benefits of accurate demand forecasting
  • Role of forecasting in efficient planning and decision-making

2. Methods of Demand Forecasting

2.1 Qualitative Methods

  • Market research techniques
  • Expert opinion and Delphi method
  • Customer surveys and focus groups

2.2 Quantitative Methods

  • Time series analysis overview
  • Regression analysis basics
  • Overview of causal models

3. Factors Influencing Demand

  • Internal factors: pricing, product portfolio, promotions
  • External factors:
    • Economic conditions (GDP, inflation, unemployment)
    • Consumer preferences and behavior changes
    • Market trends and innovation
    • Seasonality and cyclical demand patterns
    • Competitive landscape and market share shifts

4. Forecasting Accuracy and Error Measurement

  • Importance of measuring forecasting accuracy
  • Key metrics:
    • Mean Absolute Percentage Error (MAPE)
    • Tracking signal
    • Forecast bias
  • Implications of forecast errors on supply chain performance

5. Demand Forecasting Techniques

5.1 Moving Averages

  • Simple moving average calculation and use cases
  • Weighted moving averages

5.2 Exponential Smoothing

  • Single, double, and triple exponential smoothing
  • Trend and seasonality adjustments

5.3 Trend Projection

  • Identifying and extrapolating trends

5.4 Causal Models

  • Using regression and other statistical models to link demand drivers

6. Collaborative Planning, Forecasting, and Replenishment (CPFR)

  • Concept and objectives of CPFR
  • Benefits of collaboration between trading partners
  • Steps and process flow in CPFR
  • Case examples illustrating CPFR implementation

7. Inventory Management and Demand Forecasting

  • Relationship between demand forecasting and inventory control
  • Role of safety stock and buffer inventory
  • Reorder point calculation and its dependence on forecast accuracy
  • Techniques to optimize inventory levels using forecast data

8. Demand Planning and Sales & Operations Planning (S&OP) Process

  • Overview of S&OP framework
  • Integrating demand forecasting within S&OP
  • Aligning production, inventory, and demand plans
  • Cross-functional collaboration for effective S&OP

9. Technology and Tools for Demand Forecasting

  • Introduction to forecasting software solutions
  • Role of Artificial Intelligence (AI) and Machine Learning (ML) in forecasting
  • Automation of forecasting processes
  • Enhancing decision-making with predictive analytics

10. Case Studies and Best Practices in Demand Forecasting

  • Analysis of real-world industry case studies
  • Best practices for effective forecasting and planning
  • Lessons learned from successful and unsuccessful implementations
  • Strategies for continuous improvement in demand forecasting
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Quick Information

Unit Demand Forecasting And Planning
Difficulty Intermediate
Duration30 hours
Topics10
CreatedJul 20, 2026
GeneratedJul 20, 2026 06:05

Prerequisites

  • Basic knowledge of supply chain management concepts
  • Fundamental understanding of statistics and data analysis
  • Familiarity with business operations and inventory management

Recommended Resources

  • Chopra, S. & Meindl, P. (2021). Supply Chain Management: Strategy, Planning, and Operation. Pearson.
  • Makridakis, S., Wheelwright, S.C., & Hyndman, R.J. (1998). Forecasting: Methods and Applications. Wiley.
  • Mentzer, J.T. (2004). Fundamentals of Supply Chain Management: Twelve Drivers of Competitive Advantage. Sage Publications.
  • Council of Supply Chain Management Professionals (CSCMP) articles on Demand Forecasting and CPFR.
  • Software tools: SAP Integrated Business Planning, Oracle Demantra, Forecast Pro, Microsoft Excel for forecasting models.
  • Research papers on AI and Machine Learning applications in demand forecasting (available via academic databases).

Unit Topics

10
Introduction to Demand Forecasting
Understanding the importance of demand forecasting in supply chain management, its benefits, and how...
Methods of Demand Forecasting
Exploring qualitative and quantitative methods used in demand forecasting such as time series analys...
Factors Influencing Demand
Identifying and analyzing various internal and external factors that impact demand including economi...
Forecasting Accuracy and Error Measurement
Discussing key metrics and techniques to measure the accuracy of demand forecasts, including Mean Ab...
Demand Forecasting Techniques
Delving into different forecasting techniques like moving averages, exponential smoothing, trend pro...
Collaborative Planning, Forecasting, and Replenishment (CPFR)
Understanding the concept of CPFR as a collaborative approach between trading partners to enhance de...
Inventory Management and Demand Forecasting
Exploring the relationship between demand forecasting and inventory management, the role of safety s...
Demand Planning and S&OP Process
Examining the demand planning process within the Sales and Operations Planning (S&OP) framework, ali...
Technology and Tools for Demand Forecasting
Reviewing the role of advanced technologies like Artificial Intelligence (AI), Machine Learning (ML)...
Case Studies and Best Practices in Demand Forecasting
Analyzing real-world case studies, industry examples, and best practices in demand forecasting and p...