Demand-Driven Forecasting: A Structured Approach to Forecasting / Edition 1

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Overview

Praise for Demand-Driven Forecasting

A Structured Approach to Forecasting

Many companies still view quantitative forecasting methods as a"black box" or unknown approach that adds little value to improvingoverall demand forecast accuracy. Fortunately, there is a newawareness emerging across many industries of the value ofintegrating demand data into the demand forecasting process.

Equipping you with solutions that can sense, shape, and predictdemand using highly sophisticated methods and tools,internationally renowned author and thought leader Charles Chaseprovides you with a basic understanding of the methods andprocesses required to implement a demand-driven forecasting processin Demand-Driven Forecasting: A Structured Approach toForecasting.

From a review of the most basic forecasting methods, to the mostadvanced time-series methods, and innovative techniques in usetoday, this guide defines demand-driven forecasting, uniquelyoffering a fundamental understanding of the quantitative methodsused to sense, shape, and predict demand within a structuredprocess.

A must-read for CEOs, CMOs, CFOs, supply chain managers, salesmanagers, marketing brand managers, and demand forecastinganalysts, Demand-Driven Forecasting: A Structured Approach toForecasting covers:

  • Myths versus realities of forecasting
  • Causes of forecast error
  • Purposes for measuring forecasting performance
  • Forecasting methods using causal data
  • Sensing, shaping, and linking demand to supply
  • Combining analytics and domain knowledge in a structuredframework

As a forecast practitioner, it's up to you to demonstrate thevalue of analytics to senior-level managers. Filled with real-lifeexamples to build a business case for the justification ofdemand-driven forecasting, Demand-Driven Forecasting: AStructured Approach to Forecasting is your detailed blueprintto better understand this new structured approach and addsignificant improvement to demand forecast accuracy.

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Product Details

  • ISBN-13: 9780470415023
  • Publisher: Wiley
  • Publication date: 8/10/2009
  • Series: Wiley and SAS Business Series , #19
  • Edition description: Older Edition
  • Edition number: 1
  • Pages: 288
  • Sales rank: 1,370,516
  • Product dimensions: 6.30 (w) x 9.10 (h) x 1.10 (d)

Meet the Author

Charles W. Chase, Jr., is Chief Industry Consultant andSubject Matter Expert, SAS Institute Inc., where he is theprincipal architect and strategist for delivering demand planningand forecasting solutions to improve SAS customers' supply chainefficiencies. He has more than twenty-six years of experience inthe consumer packaged goods industry, and is an expert in salesforecasting, market response modeling, econometrics, and supplychain management.

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Table of Contents

Foreword.

Preface.

Acknowledgments.

Chapter 1 Demystifying Forecasting: Myths versusReality.

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, andPlans.

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 ConsensusForecasting 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 ForecastingMethod.

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 SeriesData

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 CausalData.

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: ACase 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 AdvertisingGRP/TRPs.

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

Strategic Value Assessment Framework.

Strategic Value Assessment Process.

A SVA Case Study: XYZ Company.

Summary.

Suggested Reading.

Index.

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