Business forecasting : with ForecastX TM

Description

The Sixth Edition of Business Forecasting is the most practical forecasting book on the market with the most powerful software―Forecast X.This edition presents a broad-based survey of business forecasting methods including subjective and objective approaches. As always, the author team of Wilson and Keating deliver practical how-to forecasting techniques, along with dozens of real world data sets while theory and math are held to a minimum. This Sixth Edition includes Forecast X software updated for Excel 2007 and Vista. Forecast X is the most comprehensive software tool available in this market and the new version is also backwards compatible for XP Excel 2003 systems. This Excel-based tool effectively uses wizards and many tools to make forecasting easy and understandable. Title Contents 1 Introduction to Business Forecasting Introduction Comments from the Field Quantitative Forecasting Has Become Widely Accepted Forecasting in Business Today Krispy Kreme Bell Atlantic Columbia Gas Segix Italia Pharmaceuticals in Singapore Fiat Auto Brake Parts, Inc. Some Global Forecasting Issues: Examples from Ocean Spray Cranberries Forecasting in the Public and Not-for-Profit Sectors Forecasting and Supply Chain Management Collaborative Forecasting Computer Use and Quantitative Forecasting Qualitative or Subjective Forecasting Methods Sales Force Composites Surveys of Customers and the General Population Jury of Executive Opinion The Delphi Method Some Advantages and Disadvantages of Subjective Methods New-Product Forecasting Using Marketing Research to Aid New-Product Forecasting The Product Life Cycle Concept Aids in New-Product Forecasting Analog Forecasts New Product and Penetration Curves for VCR Sales Test Marketing Product Clinics Type of Product Affects New-Product Forecasting The Bass Model for New-Product Forecasting Forecasting Sales for New Products That Have Short Product Life Cycles Two Simple Naive Models Evaluating Forecasts Using Multiple Forecasts Sources of Data Forecasting Total Houses Sold Overview of the Text Comments from the Field Integrative Case: Forecasting Sales of The Gap Comments from the Field John Galt Partial Customer List An Introduction to ForecastX 7.0 Forecasting with the ForecastX Wizard™ Using the Five Main Tabs on the Opening ForecastX Screen Suggested Readings and Web Sites Exercises 2 The Forecast Process, Data Considerations, and Model Selection Introduction The Forecast Process Trend, Seasonal, and Cyclical Data Patterns Data Patterns and Model Selection A Statistical Review Descriptive Statistics The Normal Distribution The Student’s t-Distribution From Sample to Population: Statistical Inference Hypothesis Testing Correlation Correlograms: Another Method of Data Exploration Total Houses Sold: Exploratory Data Analysis and Model Selection Business Forecasting: A Process, Not an Application Integrative Case: The Gap Comments from the Field Using ForecastX™ to Find Autocorrelation Functions Suggested Readings Exercises 3 Moving Averages and Exponential Smoothing Moving Averages Simple Exponential Smoothing Holt’s Exponential Smoothing Winters’ Exponential Smoothing The Seasonal Indices Adaptive–Response-Rate Single Exponential Smoothing Using Single, Holt’s, or ADRES Smoothing to Forecast a Seasonal Data Series New-Product Forecasting (Growth Curve Fitting) Gompertz Curve Logistics Curve Bass Model The Bass Model in Action Event Modeling Forecasting Jewelry Sales and Houses Sold with Exponential Smoothing Jewelry Sales Houses Sold Summary Integrative Case: The Gap Using ForecastX™ to Make Exponential Smoothing Forecasts Suggested Readings Exercises 4 Introduction to Forecasting with Regression Methods The Bivariate Regression Model Visualization of Data: An Important Step in Regression Analysis A Process for Regression Forecasting Forecasting with a Simple Linear Trend Using a Causal Regression Model to Forecast A Jewelry Sales Forecast Based on Disposable Personal Income Statistical Evaluation of Regression Models Basic Diagnostic Checks for Evaluating Regression Results Using the Standard Error of the Estimate Serial Correlation Heteroscedasticity Cross-Sectional Forecasting Forecasting Total Houses Sold with Two Bivariate Regression Models Comments from the Field Integrative Case: The Gap Comments from the Field Using ForecastX™ to Make Regression Forecasts Further Comments on Regression Models Suggested Readings Exercises 5 Forecasting with Multiple Regression The Multiple-Regression Model Selecting Independent Variables Forecasting with a Multiple-Regression Model The Regression Plane Statistical Evaluation of Multiple-Regression Models Three Quick Checks in Evaluating Multiple- Regression Models Multicollinearity The Demand for Nurses Serial Correlation: A Second Look Serial Correlation and the Omitted-Variable Problem Alternative-Variable Selection Criteria Accounting for Seasonality in a Multiple-Regression Model Extensions of the Multiple-Regression Model Advice on Using Multiple Regression in Forecasting Forecasting Jewelry Sales with Multiple Regression Forecasting Consumer Products Integrative Case: The Gap Using ForecastX™ to Make Multiple-Regression Forecasts Suggested Readings Exercises 6 Time-Series Decomposition The Basic Time-Series Decomposition Model Deseasonalizing the Data and Finding Seasonal Indices Finding the Long-Term Trend Measuring the Cyclical Component Overview of Business Cycles Business Cycle Indicators The Cycle Factor for Private Housing Starts The Time-Series Decomposition Forecast Forecasting Shoe Store Sales by Using Time-Series Decomposition Forecasting Total Houses Sold by Using Time-Series Decomposition Forecasting Winter Daily Natural Gas Demand at Vermont Gas Systems Integrative Case: The Gap Using ForecastX™ to Make Time-Series Decomposition Forecasts Suggested Readings Exercises Appendix: Components of the Composite Indices 7 ARIMA (Box-Jenkins)–Type Forecasting Models Introduction The Philosophy of Box-Jenkins Moving-Average Models Autoregressive Models Mixed Autoregressive and Moving-Average Models Stationarity The Box-Jenkins Identification Process Comments from the Field: An Overview of INTELSAT Forecasting ARIMA: A Set of Numerical Examples Example 1 Example 2 Example 3 Example 4 Forecasting Seasonal Time Series Total Houses Sold Intelligent Transportation Systems Integrative Case: Forecasting Sales of The Gap Using ForecastX™ to Make ARIMA (Box-Jenkins) Forecasts Suggested Readings Exercises Appendix: Critical Values of Chi-Square 8 Combining Forecast Results Introduction Bias An Example What Kinds of Forecasts Can Be Combined? Considerations in Choosing the Weights for Combined Forecasts Three Techniques for Selecting Weights When Combining Forecasts Justice Is Forecast An Application of the Regression Method for Combining Forecasts Forecasting Total Houses Sold with a Combined Forecast Comments from the Field: Combining Forecasts Can Improve Results Integrative Case: Forecasting The Gap Sales Data with a Combination Model Using ForecastX™ to Combine Forecasts Suggested Readings Exercises 9 Data Mining Introduction Data Mining Comments from the Field The Tools of Data Mining Business Forecasting and Data Mining A Data Mining Example: k-Nearest-Neighbor Comments from the Field: Cognos A Business Data Mining Example: k-Nearest-Neighbor Classification Trees: A Second Classification Technique A Business Data Mining Example: Classification Trees Naive Bayes: A Third Classification Technique Comments from the Field Regression: A Fourth Classification Technique Comments from the Field: Fool’s Gold Summary Suggested Readings Exercises 10 Forecast Implementation Keys to Obtaining Better Forecasts The Forecast Process Step 1. Specify Objectives Step 2. Determine What to Forecast Step 3. Identify Time Dimensions Step 4. Data Considerations How to Evaluate and Improve a Forecasting Process Step 5. Model Selection Step 6. Model Evaluation Step 7. Forecast Preparation Step 8. Forecast Presentation Step 9. Tracking Results Choosing the Right Forecasting Techniques Sales Force Composite (SFC) Customer Surveys (CS) Jury of Executive Opinion (JEO) Delphi Method Naive Moving Averages Simple Exponential Smoothing (SES) Adaptive–Response-Rate Single Exponential Smoothing (ADRES) Holt’s Exponential Smoothing (HES) Winters’ Exponential Smoothing (WES) Regression-Based Trend Models Regression-Based Trend Models with Seasonality Comments from the Field Regression Models with Causality Comments from the Field Time-Series Decomposition (TSD) ARIMA Special Forecasting Considerations Event Modeling Combining Forecasts New-Product Forecasting (NPF) Data Mining Comments from the Field Summary Using ProCast™ in ForecastX™ to Make Forecasts Suggested Readings Exercises Index