Charles W. Chase

Forecasting 2E (SAS)

• Gebundenes Buch

Produktbeschreibung

An updated new edition of the comprehensive guide to better business forecasting

Many companies still look at quantitative forecasting methods with suspicion, but a new awareness is emerging across many industries as more businesses and professionals recognize the value of integrating demand data (point-of-sale and syndicated scanner data) into the forecasting process. Demand-Driven Forecasting equips you with solutions that can sense, shape, and predict future demand using highly sophisticated methods and tools. From a review of the most basic forecasting methods to the most advanced and innovative techniques in use today, this guide explains demand-driven forecasting, offering a fundamental understanding of the quantitative methods used to sense, shape, and predict future demand within a structured process. Offering a complete overview of the latest business forecasting concepts and applications, this revised Second Edition of Demand-Driven Forecasting is the perfect guide for professionals who need to improve the accuracy of their sales forecasts.
Completely updated to include the very latest concepts and methods in forecasting
Includes real case studies and examples, actual data, and graphical displays and tables to illustrate how effective implementation works
Ideal for CEOs, CFOs, CMOs, vice presidents of supply chain, vice presidents of demand forecasting and planning, directors of demand forecasting and planning, supply chain managers, demand planning managers, marketing analysts, forecasting analysts, financial managers, and any other professional who produces or contributes to forecasts

Accurate forecasting is vital to success in today's challenging business climate. Demand-Driven Forecasting offers proven and effective insight on making sure your forecasts are right on the money.

Produktdetails

• SAS Institute Inc
• Verlag: Wiley & Sons
• 2. Aufl.
• Seitenzahl: 384
• Erscheinungstermin: 19. August 2013
• Englisch
• Abmessung: 235mm x 157mm x 25mm
• Gewicht: 710g
• ISBN-13: 9781118669396
• ISBN-10: 1118669398
• Artikelnr.: 37779854

Autorenporträt

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.

Inhaltsangabe

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