Profit from Your Forecasting Software : A Best Practice Guide for Sales Forecasters.
Goodwin, Paul.
Profit from Your Forecasting Software : A Best Practice Guide for Sales Forecasters. - 1st ed. - 1 online resource (243 pages) - Wiley and SAS Business Series . - Wiley and SAS Business Series .
Cover -- Title Page -- Copyright -- Contents -- Acknowledgments -- Prologue -- Chapter 1: Profit from Accurate Forecasting -- 1.1 The Importance of Demand Forecasting -- 1.2 When Is a Forecast Not a Forecast? -- 1.3 Ways of Presenting Forecasts -- 1.3.1 Forecasts as Probability Distributions -- 1.3.2 Point Forecasts -- 1.3.3 Prediction Intervals -- 1.4 The Advantages of Using Dedicated Demand Forecasting Software -- 1.5 Getting Your Data Ready for Forecasting -- 1.6 Trading-Day Adjustments -- 1.7 Overview of the Rest of the Book -- 1.8 Summary of Key Terms -- 1.9 References -- Chapter 2: How Your Software Finds Patterns in Past Demand Data -- 2.1 Introduction -- 2.2 Key Features of Sales Histories -- 2.2.1 An Underlying Trend -- 2.2.2 A Seasonal Pattern -- 2.2.3 Noise -- 2.3 Autocorrelation -- 2.4 Intermittent Demand -- 2.5 Outliers and Special Events -- 2.6 Correlation -- 2.7 Missing Values -- 2.8 Wrap-Up -- 2.9 Summary of Key Terms -- Chapter 3: Understanding Your Software's Bias and Accuracy Measures -- 3.1 Introduction -- 3.2 Fitting and Forecasting -- 3.2.1 Fixed-Origin Evaluations -- 3.2.2 Rolling-Origin Evaluations -- 3.3 Forecast Errors and Bias Measures -- 3.3.1 The Mean Error (ME) -- 3.3.2 The Mean Percentage Error (MPE) -- 3.4 Direct Accuracy Measures -- 3.4.1 The Mean Absolute Error (MAE) -- 3.4.2 The Mean Squared Error (MSE) -- 3.5 Percentage Accuracy Measures -- 3.5.1 The Mean Absolute Percentage Error (MAPE) -- 3.5.2 The Median Absolute Percentage Error (MDAPE) -- 3.5.3 The Symmetric Mean Absolute Percentage Error (SMAPE) -- 3.5.4 The MAD/MEAN Ratio -- 3.5.5 Percentage Error Measures When There Is a Trend or Seasonal Pattern -- 3.6 Relative Accuracy Measures -- 3.6.1 Geometric Mean Relative Absolute Error (GMRAE) -- 3.6.2 The Mean Absolute Scaled Error (MASE) -- 3.6.3 Bayesian Information Criterion (BIC). 3.7 Comparing the Different Accuracy Measures -- 3.8 Exception Reporting -- 3.9 Forecast Value-Added Analysis (FVA) -- 3.10 Wrap-Up -- 3.11 Summary of Key Terms -- 3.12 References -- Chapter 4: Curve Fitting and Exponential Smoothing -- 4.1 Introduction -- 4.2 Curve Fitting -- 4.2.1 Common Types of Curve -- 4.2.2 Assessing How Well the Curve Fits the Sales History -- 4.2.3 Strengths and Limitations of Forecasts Based on Curve Fitting -- 4.3 Exponential Smoothing Methods -- 4.3.1 Simple (or Single) Exponential Smoothing -- 4.3.2 Exponential Smoothing When There Is a Trend: Holt's Method -- 4.3.3 The Damped Holt's Method -- 4.3.4 Holt's Method with an Exponential Trend -- 4.3.5 Exponential Smoothing Where There Is a Trend and Seasonal Pattern: The Holt-Winters Method -- 4.3.6 Overview of Exponential Smoothing Methods -- 4.4 Forecasting Intermittent Demand -- 4.5 Wrap-Up -- 4.6 Summary of Key Terms -- Chapter 5: Box-Jenkins ARIMA Models -- 5.1 Introduction -- 5.2 Stationarity -- 5.3 Models of Stationary Time Series: Autoregressive Models -- 5.4 Models of Stationary Time Series: Moving Average Models -- 5.5 Models of Stationary Time Series: Mixed Models -- 5.6 Fitting a Model to a Stationary Time Series -- 5.7 Diagnostic Checks -- 5.7.1 Check 1: Are the Coefficients of the Model Statistically Significant? -- 5.7.2 Check 2: Overfitting-Should We Be Using a More Complex Model? -- 5.7.3 Check 3: Are the Residuals of the Model White Noise? -- 5.7.4 Check 4: Are the Residuals Normally Distributed? -- 5.8 Models of Nonstationary Time Series: Differencing -- 5.9 Should You Include a Constant in Your Model of a Nonstationary Time Series? -- 5.10 What If a Series Is Nonstationary in the Variance? -- 5.11 ARIMA Notation -- 5.12 Seasonal ARIMA Models -- 5.13 Example of Fitting a Seasonal ARIMA Model -- 5.14 Wrap-Up -- 5.15 Summary of Key Terms. Chapter 6: Regression Models -- 6.1 Introduction -- 6.2 Bivariate Regression -- 6.2.1 Should You Drop the Constant? -- 6.2.2 Spurious Regression -- 6.3 Multiple Regression -- 6.3.1 Interpreting Computer Output for Multiple Regression -- 6.3.2 Refitting the Model -- 6.3.3 Multicollinearity -- 6.3.4 Using Dummy Predictor Variables in Your Regression Model -- 6.3.5 Outliers and Influential Observations -- 6.4 Regression Versus Univariate Methods -- 6.5 Dynamic Regression -- 6.6 Wrap-Up -- 6.7 Summary of Key Terms -- 6.8 Appendix: Assumptions of Regression Analysis -- 6.9 Reference -- Chapter 7: Inventory Control, Aggregation, and Hierarchies -- 7.1 Introduction -- 7.2 Identifying Reorder Levels and Safety Stocks -- 7.3 Estimating the Probability Distribution of Demand -- 7.3.1 Using Prediction Intervals to Determine Safety Stocks -- 7.4 What If the Probability Distribution of Demand Is Not Normal? -- 7.4.1 The Log-Normal Distribution -- 7.4.2 Using the Poisson and Negative Binomial Distributions -- 7.5 Temporal Aggregation -- 7.6 Dealing with Product Hierarchies and Reconciling Forecasts -- 7.6.1 Bottom-Up Forecasting -- 7.6.2 Top-Down Forecasting -- 7.6.3 Middle-Out Forecasting -- 7.6.4 Hybrid Methods -- 7.6.5 Issues and Future Developments -- 7.7 Wrap-Up -- 7.8 Summary of Key Terms -- 7.9 References -- Chapter 8: Automation and Choice -- 8.1 Introduction -- 8.2 How Much Past Data Do You Need to Apply Different Forecasting Methods? -- 8.3 Are More Complex Forecasting Methods Likely to Be More Accurate? -- 8.4 When It's Best to Automate Forecasts -- 8.5 The Downside of Automation -- 8.6 Wrap-Up -- 8.7 References -- Chapter 9: Judgmental Interventions: When Are They Appropriate? -- 9.1 Introduction -- 9.2 Psychological Biases That Might Catch You Out -- 9.2.1 Seeing Patterns in Randomness -- 9.2.2 Recency Bias -- 9.2.3 Hindsight Bias. 9.2.4 Optimism Bias -- 9.3 Restrict Your Interventions -- 9.3.1 Large Adjustments Perform Better -- 9.3.2 Focus Your Efforts Where They'll Count -- 9.4 Making Effective Interventions -- 9.4.1 Divide and Conquer -- 9.4.2 Using Analogies -- 9.4.3 Counteracting Optimism Bias -- 9.4.4 Harnessing the Power of Groups of Managers -- 9.4.5 Record Your Rationale -- 9.5 Combining Judgment and Statistical Forecasts -- 9.6 Wrap-Up -- 9.7 Reference -- Chapter 10: New Product Forecasting -- 10.1 Introduction -- 10.2 Dangers of Using Unstructured Judgment in New Product Forecasting -- 10.3 Forecasting by Analogy -- 10.3.1 Structured Analogies -- 10.3.2 Applying Structured Analogies -- 10.4 The Bass Diffusion Model -- 10.4.1 Innovators and Imitators -- 10.4.2 Estimating a Bass Model -- 10.4.3 Limitations of the Basic Bass Model -- 10.5 Wrap-Up -- 10.6 Summary of Key Terms -- 10.7 References -- Chapter 11: Summary: A Best Practice Blueprint for Using Your Software -- 11.1 Introduction -- 11.2 Desirable Characteristics of Forecasting Software -- 11.2.1 Data Preparation -- 11.2.2 Graphical Displays -- 11.2.3 Method Selection -- 11.2.4 Implementing Methods -- 11.2.5 Hierarchies -- 11.2.6 Forecasting with Probabilities -- 11.2.7 Support for Judgment -- 11.2.8 Presentation of Forecasts -- 11.3 A Blueprint for Best Practice -- 11.4 References -- Index -- EULA.
9781119415985
Sales management-Data processing.
Electronic books.
HF5438.4 .G66 2018
658.4030285
Profit from Your Forecasting Software : A Best Practice Guide for Sales Forecasters. - 1st ed. - 1 online resource (243 pages) - Wiley and SAS Business Series . - Wiley and SAS Business Series .
Cover -- Title Page -- Copyright -- Contents -- Acknowledgments -- Prologue -- Chapter 1: Profit from Accurate Forecasting -- 1.1 The Importance of Demand Forecasting -- 1.2 When Is a Forecast Not a Forecast? -- 1.3 Ways of Presenting Forecasts -- 1.3.1 Forecasts as Probability Distributions -- 1.3.2 Point Forecasts -- 1.3.3 Prediction Intervals -- 1.4 The Advantages of Using Dedicated Demand Forecasting Software -- 1.5 Getting Your Data Ready for Forecasting -- 1.6 Trading-Day Adjustments -- 1.7 Overview of the Rest of the Book -- 1.8 Summary of Key Terms -- 1.9 References -- Chapter 2: How Your Software Finds Patterns in Past Demand Data -- 2.1 Introduction -- 2.2 Key Features of Sales Histories -- 2.2.1 An Underlying Trend -- 2.2.2 A Seasonal Pattern -- 2.2.3 Noise -- 2.3 Autocorrelation -- 2.4 Intermittent Demand -- 2.5 Outliers and Special Events -- 2.6 Correlation -- 2.7 Missing Values -- 2.8 Wrap-Up -- 2.9 Summary of Key Terms -- Chapter 3: Understanding Your Software's Bias and Accuracy Measures -- 3.1 Introduction -- 3.2 Fitting and Forecasting -- 3.2.1 Fixed-Origin Evaluations -- 3.2.2 Rolling-Origin Evaluations -- 3.3 Forecast Errors and Bias Measures -- 3.3.1 The Mean Error (ME) -- 3.3.2 The Mean Percentage Error (MPE) -- 3.4 Direct Accuracy Measures -- 3.4.1 The Mean Absolute Error (MAE) -- 3.4.2 The Mean Squared Error (MSE) -- 3.5 Percentage Accuracy Measures -- 3.5.1 The Mean Absolute Percentage Error (MAPE) -- 3.5.2 The Median Absolute Percentage Error (MDAPE) -- 3.5.3 The Symmetric Mean Absolute Percentage Error (SMAPE) -- 3.5.4 The MAD/MEAN Ratio -- 3.5.5 Percentage Error Measures When There Is a Trend or Seasonal Pattern -- 3.6 Relative Accuracy Measures -- 3.6.1 Geometric Mean Relative Absolute Error (GMRAE) -- 3.6.2 The Mean Absolute Scaled Error (MASE) -- 3.6.3 Bayesian Information Criterion (BIC). 3.7 Comparing the Different Accuracy Measures -- 3.8 Exception Reporting -- 3.9 Forecast Value-Added Analysis (FVA) -- 3.10 Wrap-Up -- 3.11 Summary of Key Terms -- 3.12 References -- Chapter 4: Curve Fitting and Exponential Smoothing -- 4.1 Introduction -- 4.2 Curve Fitting -- 4.2.1 Common Types of Curve -- 4.2.2 Assessing How Well the Curve Fits the Sales History -- 4.2.3 Strengths and Limitations of Forecasts Based on Curve Fitting -- 4.3 Exponential Smoothing Methods -- 4.3.1 Simple (or Single) Exponential Smoothing -- 4.3.2 Exponential Smoothing When There Is a Trend: Holt's Method -- 4.3.3 The Damped Holt's Method -- 4.3.4 Holt's Method with an Exponential Trend -- 4.3.5 Exponential Smoothing Where There Is a Trend and Seasonal Pattern: The Holt-Winters Method -- 4.3.6 Overview of Exponential Smoothing Methods -- 4.4 Forecasting Intermittent Demand -- 4.5 Wrap-Up -- 4.6 Summary of Key Terms -- Chapter 5: Box-Jenkins ARIMA Models -- 5.1 Introduction -- 5.2 Stationarity -- 5.3 Models of Stationary Time Series: Autoregressive Models -- 5.4 Models of Stationary Time Series: Moving Average Models -- 5.5 Models of Stationary Time Series: Mixed Models -- 5.6 Fitting a Model to a Stationary Time Series -- 5.7 Diagnostic Checks -- 5.7.1 Check 1: Are the Coefficients of the Model Statistically Significant? -- 5.7.2 Check 2: Overfitting-Should We Be Using a More Complex Model? -- 5.7.3 Check 3: Are the Residuals of the Model White Noise? -- 5.7.4 Check 4: Are the Residuals Normally Distributed? -- 5.8 Models of Nonstationary Time Series: Differencing -- 5.9 Should You Include a Constant in Your Model of a Nonstationary Time Series? -- 5.10 What If a Series Is Nonstationary in the Variance? -- 5.11 ARIMA Notation -- 5.12 Seasonal ARIMA Models -- 5.13 Example of Fitting a Seasonal ARIMA Model -- 5.14 Wrap-Up -- 5.15 Summary of Key Terms. Chapter 6: Regression Models -- 6.1 Introduction -- 6.2 Bivariate Regression -- 6.2.1 Should You Drop the Constant? -- 6.2.2 Spurious Regression -- 6.3 Multiple Regression -- 6.3.1 Interpreting Computer Output for Multiple Regression -- 6.3.2 Refitting the Model -- 6.3.3 Multicollinearity -- 6.3.4 Using Dummy Predictor Variables in Your Regression Model -- 6.3.5 Outliers and Influential Observations -- 6.4 Regression Versus Univariate Methods -- 6.5 Dynamic Regression -- 6.6 Wrap-Up -- 6.7 Summary of Key Terms -- 6.8 Appendix: Assumptions of Regression Analysis -- 6.9 Reference -- Chapter 7: Inventory Control, Aggregation, and Hierarchies -- 7.1 Introduction -- 7.2 Identifying Reorder Levels and Safety Stocks -- 7.3 Estimating the Probability Distribution of Demand -- 7.3.1 Using Prediction Intervals to Determine Safety Stocks -- 7.4 What If the Probability Distribution of Demand Is Not Normal? -- 7.4.1 The Log-Normal Distribution -- 7.4.2 Using the Poisson and Negative Binomial Distributions -- 7.5 Temporal Aggregation -- 7.6 Dealing with Product Hierarchies and Reconciling Forecasts -- 7.6.1 Bottom-Up Forecasting -- 7.6.2 Top-Down Forecasting -- 7.6.3 Middle-Out Forecasting -- 7.6.4 Hybrid Methods -- 7.6.5 Issues and Future Developments -- 7.7 Wrap-Up -- 7.8 Summary of Key Terms -- 7.9 References -- Chapter 8: Automation and Choice -- 8.1 Introduction -- 8.2 How Much Past Data Do You Need to Apply Different Forecasting Methods? -- 8.3 Are More Complex Forecasting Methods Likely to Be More Accurate? -- 8.4 When It's Best to Automate Forecasts -- 8.5 The Downside of Automation -- 8.6 Wrap-Up -- 8.7 References -- Chapter 9: Judgmental Interventions: When Are They Appropriate? -- 9.1 Introduction -- 9.2 Psychological Biases That Might Catch You Out -- 9.2.1 Seeing Patterns in Randomness -- 9.2.2 Recency Bias -- 9.2.3 Hindsight Bias. 9.2.4 Optimism Bias -- 9.3 Restrict Your Interventions -- 9.3.1 Large Adjustments Perform Better -- 9.3.2 Focus Your Efforts Where They'll Count -- 9.4 Making Effective Interventions -- 9.4.1 Divide and Conquer -- 9.4.2 Using Analogies -- 9.4.3 Counteracting Optimism Bias -- 9.4.4 Harnessing the Power of Groups of Managers -- 9.4.5 Record Your Rationale -- 9.5 Combining Judgment and Statistical Forecasts -- 9.6 Wrap-Up -- 9.7 Reference -- Chapter 10: New Product Forecasting -- 10.1 Introduction -- 10.2 Dangers of Using Unstructured Judgment in New Product Forecasting -- 10.3 Forecasting by Analogy -- 10.3.1 Structured Analogies -- 10.3.2 Applying Structured Analogies -- 10.4 The Bass Diffusion Model -- 10.4.1 Innovators and Imitators -- 10.4.2 Estimating a Bass Model -- 10.4.3 Limitations of the Basic Bass Model -- 10.5 Wrap-Up -- 10.6 Summary of Key Terms -- 10.7 References -- Chapter 11: Summary: A Best Practice Blueprint for Using Your Software -- 11.1 Introduction -- 11.2 Desirable Characteristics of Forecasting Software -- 11.2.1 Data Preparation -- 11.2.2 Graphical Displays -- 11.2.3 Method Selection -- 11.2.4 Implementing Methods -- 11.2.5 Hierarchies -- 11.2.6 Forecasting with Probabilities -- 11.2.7 Support for Judgment -- 11.2.8 Presentation of Forecasts -- 11.3 A Blueprint for Best Practice -- 11.4 References -- Index -- EULA.
9781119415985
Sales management-Data processing.
Electronic books.
HF5438.4 .G66 2018
658.4030285