Demand-Driven Forecasting: A Structured Approach to Forecasting [NOOK Book]


Praise for Demand-Driven Forecasting

"Charlie Chase is one of the top thought leaders in the business forecasting community, making him eminently qualified to write what I consider to be the definitive book on demand-driven forecasting. This Second Edition takes the reader from a review of the most basic forecasting methods, to the most advanced time-series methods, and then on to the most innovative techniques in use today."
Lawrence "Larry" ...

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Demand-Driven Forecasting: A Structured Approach to Forecasting

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Praise for Demand-Driven Forecasting

"Charlie Chase is one of the top thought leaders in the business forecasting community, making him eminently qualified to write what I consider to be the definitive book on demand-driven forecasting. This Second Edition takes the reader from a review of the most basic forecasting methods, to the most advanced time-series methods, and then on to the most innovative techniques in use today."
Lawrence "Larry" Lapide, PhD, Research Affiliate, MIT Center for Transportation & Logistics Lecturer, University of Massachusetts, Recipient of the Institute of Business Forecasting & Planning's inaugural Lifetime Achievement in Business Forecasting & Planning Award

"It is with profound pleasure that I am endorsing one of my favorite books, Demand-Driven Forecasting, by one of the sharpest minds in the field of business forecasting. Charlie Chase has created a truly holistic book, which allows a rare focus on key methodologies and real business challenges that is highly relevant for CPG companies. This Second Edition has expanded into the field of new product forecasting, a key discipline that makes or breaks forecasting success in a world of increasingly shorter product life cycles. In short: If you are looking for a book on forecasting that has it all, then you have found it. Demand-Driven Forecasting is a must-read."
—Lauge Valentin, Director of Forecasting, The LEGO Group

"Charlie Chase has delivered a text that provides forecasters at all levels with practical knowledge and the quantitative tools that will allow them to make a significant leap in modeling and predicting demand. Using plain and concise language, he integrates real-world examples with discussions of advanced statistical methods to demonstrate how to develop forecasting solutions by applying the power of analytics. Those in forecasting will benefit from the knowledge found in this book and by keeping it handy as a valuable reference."
—Glenn Keltner, PhD, Manager, Demand Planning Development, Nestle Purina PetCare Company

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Product Details

  • ISBN-13: 9781118735572
  • Publisher: Wiley
  • Publication date: 7/16/2013
  • Series: Wiley and SAS Business Series
  • Sold by: Barnes & Noble
  • Format: eBook
  • Edition number: 2
  • Pages: 384
  • File size: 4 MB

Meet the Author

CHARLES W. CHASE Jr. is the Chief Industry Consultant in SAS's Manufacturing & 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.

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Table of Contents

Foreword xi

Preface xv

Acknowledgments xix

About the Author xx

Chapter 1 Demystifying Forecasting: Myths versus Reality 1

Data Collection, Storage, and Processing Reality 5

Art-of-Forecasting Myth 8

End-Cap Display Dilemma 10

Reality of Judgmental Overrides 11

Oven Cleaner Connection 13

More Is Not Necessarily Better 16

Reality of Unconstrained Forecasts, Constrained Forecasts, and Plans 17

Northeast Regional Sales Composite Forecast 21

Hold-and-Roll Myth 22

The Plan that Was Not Good Enough 23

Package to Order versus Make to Order 25

“Do You Want Fries with That?” 26

Summary 28

Notes 28

Chapter 2 What Is Demand-Driven Forecasting? 31

Transitioning from Traditional Demand Forecasting 33

What’s Wrong with The Demand-Generation Picture? 34

Fundamental Flaw with Traditional Demand Generation 37

Relying Solely on a Supply-Driven Strategy Is Not the Solution 39

What Is Demand-Driven Forecasting? 40

What Is Demand Sensing and Shaping? 41

Changing the Demand Management Process Is Essential 57

Communication Is Key 65

Measuring Demand Management Success 67

Benefits of a Demand-Driven Forecasting Process 68

Key Steps to Improve the Demand

Management Process 70

Why Haven’t Companies Embraced the Concept of Demand-Driven? 71

Summary 74

Notes 75

Chapter 3 Overview of Forecasting Methods 77

Underlying Methodology 79

Different Categories of Methods 83

How Predictable Is the Future? 88

Some Causes of Forecast Error 91

Segmenting Your Products to Choose the Appropriate Forecasting Method 94

Summary 101

Note 101

Chapter 4 Measuring Forecast Performance 103

“We Overachieved Our Forecast, So Let’s Party!” 105

Purposes for Measuring Forecasting Performance 106

Standard Statistical Error Terms 107

Specific Measures of Forecast Error 111

Out-of-Sample Measurement 115

Forecast Value Added 118

Summary 122

Notes 123

Chapter 5 Quantitative Forecasting Methods Using Time Series Data 125

Understanding the Model-Fitting Process 127

Introduction to Quantitative Time Series Methods 130

Quantitative Time Series Methods 135

Moving Averaging 136

Exponential Smoothing 142

Single Exponential Smoothing 143

Holt’s Two-Parameter Method 147

Holt’s-Winters’ Method 149

Winters’ Additive Seasonality 151

Summary 156

Notes 158

Chapter 6 Regression Analysis 159

Regression Methods 160

Simple Regression 160

Correlation Coefficient 163

Coefficient of Determination 165

Multiple Regression 166

Data Visualization Using Scatter Plots and Line Graphs 170

Correlation Matrix 173

Multicollinearity 175

Analysis of Variance 178

F-test 178

Adjusted R2 180

Parameter Coefficients 181

t-test 184

P-values 185

Variance Inflation Factor 186

Durbin-Watson Statistic 187

Intervention Variables (or Dummy Variables) 191

Regression Model Results 197

Key Activities in Building a Multiple Regression Model 199

Cautions about Regression Models 201

Summary 201

Notes 202

Chapter 7 ARIMA Models 203

Phase 1: Identifying the Tentative Model 204

Phase 2: Estimating and Diagnosing the Model Parameter Coefficients 213

Phase 3: Creating a Forecast 216

Seasonal ARIMA Models 216

Box-Jenkins Overview 225

Extending ARIMA Models to Include Explanatory Variables 226

Transfer Functions 229

Numerators and Denominators 229

Rational Transfer Functions 230

ARIMA Model Results 234

Summary 235

Notes 237

Chapter 8 Weighted Combined Forecasting Methods 239

What Is Weighted Combined Forecasting? 242

Developing a Variance Weighted Combined Forecast 245

Guidelines for the Use of Weighted Combined Forecasts 248

Summary 250

Notes 251

Chapter 9 Sensing, Shaping, and Linking Demand to Supply: A Case Study Using MTCA 253

Linking Demand to Supply Using Multi-Tiered Causal Analysis 256

Case Study: The Carbonated Soft Drink Story 259

Summary 276

Appendix 9A Consumer Packaged Goods Terminology 277

Appendix 9B Adstock Transformations for Advertising GRP/TRPs 279

Notes 282

Chapter 10 New Product Forecasting: Using Structured Judgment 283

Differences between Evolutionary and Revolutionary New Products 284

General Feeling about New Product Forecasting 286

New Product Forecasting Overview 288

What Is a Candidate Product? 292

New Product Forecasting Process 293

Structured Judgment Analysis 294

Structured Process Steps 296

Statistical Filter Step 303

Model Step 305

Forecast Step 308

Summary 313

Notes 316

Chapter 11 Strategic Value Assessment: Assessing the Readiness of Your Demand Forecasting Process 317

Strategic Value Assessment Framework 319

Strategic Value Assessment Process 321

SVA Case Study: XYZ Company 323

Summary 351

Suggested Reading 352

Notes 352

Index 355

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