PART III. AUTOMATED FORECASTING TECHNIQUES: THE STATE SPACE APPROACH

Chapter 8. Big Data: Baseline Forecasting with Exponential Smoothing Models

Chapter 9. Short-term Forecasting With ARIMA Models

PART IV: CREATING CAUSAL FORECASTING MODELS

Chapter 10. Demand Forecasting with Regression Models

Chapter 11. Gaining Credibility through Root Cause Analysis and Exception Handling

PART V: IMPROVING FORECASTING AGILITY: THE PEER PROCESS

Chapter 12. The Final Forecast Numbers: Reconciling Change and Chance

Chapter 13. Creating a Data Framework for Forecast Support and Decision Analysis

Chapter 14. Blending Agile Forecasting(R) with an Integrated Business Planning Process

Forecasting, Practice and Process for Demand Management

*(Class Exercises, Problem Sets, Cases, References, Glossary)*

by Hans Levenbach, PhD and James P. Cleary, MBA

© 2006 Duxbury Press/Cengage Learning

(ISBN 0-534-26286-6)

PART I. INTRODUCING THE FORECASTING PROCESS.

1. Forecasting as a Structured Process.

2. Classifying Forecasting Techniques.

PART II. EXPLORING TIME SERIES.

3. Data Exploration for Forecasting.

4. Characteristics of Time Series.

5. Assessing Accuracy of Forecasts.

PART III. FORECASTING THE AGGREGATE.

6. Dealing with Seasonal Fluctuations.

7. Forecasting the Business Environment.

PART IV: APPLYING BOTTOM-UP TECHNIQUES.

8. The Exponential Smoothing Method.

9. Disaggregate Product-Demand Forecasting.Forecasting for the Supply Chain.

PART V: FORECASTING WITH CAUSAL FORECASTING MODELS.

10. Creating and Analyzing Causal Forecasting Models.

11. Linear Regression Analysis.

12. Forecasting with Regression Models.

PART VI: FORECASTING WITH ARIMA MODELS.

13. Building ARIMA Models:

14. Forecasting with ARIMA Models.

PART VII: IMPROVING FORECASTING EFFECTIVENESS.

15. Selecting the Final Forecast Number.

16. Implementing the Forecasting Process.

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