Demand-Driven Forecasting A Structured Approach to Forecasting

Foreword.

Preface.

Acknowledgments.

Chapter 1 Demystifying Forecasting: Myths versus Reality.

Data Collection, Storage, and Processing Reality.

“Art of Forecasting” Myth.

End-Cap Display Dilemma.

Reality of Judgmental Overrides.

Oven Cleaner Connection.

More Is Not Necessarily Better.

Reality of Unconstrained Forecasts, Constrained Forecasts, and Plans.

Northeast Regional Sales Equation.

“Hold and Roll” Myth.

The Plan That Wasn't Good Enough.

Summary.

Notes.

Chapter 2 What Is Demand-Driven Forecasting?

“Do You Want Fries with That?”

Definition of Demand-Driven Forecasting.

What Is Demand Sensing?

Data Requirements.

Role of Sales and Marketing.

What Is Demand Shaping?

Integrating Demand-Driven Forecasting into the Consensus Forecasting Process.

Importance of Business Intelligence Portals/Dashboards.

Role of the Finance Department.

Demand-Driven Forecasting Process Flow Model.

Key Process Participants.

Benefits of Demand-Driven Forecasting.

Summary.

Chapter 3 Overview of Forecasting Methods.

Underlying Methodology.

Different Categories of Methods.

How Predictable Is the Future?

Some Causes of Forecast Error.

Segmenting Your Products to Choose the Appropriate Forecasting Method.

Summary.

Chapter 4 Measuring Forecast Performance.

We Overachieved Our Forecast, So Let’s Party!

Purposes for Measuring Forecasting Performance

Standard Statistical Error Terms.

Specific Measures of Forecast Error.

Out-of-Sample Measurement.

Forecast Value Added.

Summary.

Chapter 5 Quantitative Forecasting Methods Using Time Series Data

Understanding the Model-Fitting Process.

Introduction to Quantitative Time Series Methods.

Quantitative Time Series Methods.

Moving Averaging.

Exponential Smoothing.

Single Exponential Smoothing.

Holt's Two-Parameter Method.

Holt's-Winters' Method.

Winters' Additive Seasonality.

Summary.

Chapter 6 Quantitative Forecasting Methods Using Causal Data.

Regression Methods.

Simple Regression.

Multiple Regression.

Box-Jenkins Approach to ARIMA Models.

Box-Jenkins Overview.

Extending ARIMA Models to Include Explanatory Variables.

Unobserved Component Models.

Summary.

Chapter 7 Weighted Combined Forecasting Methods.

What Is Weighted Combined Forecasting?

Developing a Variance Weighted Combined Forecast.

Summary.

Chapter 8 Sensing, Shaping, and Linking Demand to Supply: A Case Study Using MTCA.

Linking Demand to Supply Using Multi-tiered Causal Analysis.

Case Study: The Carbonated Soft Drink Story.

Summary.

Appendix 8A Consumer Packaged Goods Terminology.

Appendix 8B. Adstock Transformations for Advertising GRP/TRPs.

Chapter 9 Strategic Value Assessment: Assessing the Readiness of Your Demand Forecasting Process.

Strategic Value Assessment Framework.

Strategic Value Assessment Process.

A SVA Case Study: XYZ Company.

Summary.

Suggested Reading.

Index.

Charles W. Chase, Jr., is the Business Enablement Manager at SAS Manufacturing and Supply Chain Global Practice, where he is the principal architect and strategist for delivering demand planning and forecasting solutions to improve SAS customers' supply chain efficiencies. He has more than twenty-six years of experience in the consumer packaged goods industry, and is an expert in sales forecasting, market response modeling, econometrics, and supply chain management.