Profit From Your Forecasting Software: A Best Practice Guide for Sales Forecasters

✍ Scribed by Paul Goodwin

• Publisher: John Wiley & Sons
• Year: 2018
• Tongue: English
• Leaves: 243
• Series: Wiley and SAS Business Series
• Edition: 1
• Category: Library

✦ Synopsis

Go beyond technique to master the difficult judgement calls of forecasting

A variety of software can be used effectively to achieve accurate forecasting, but no software can replace the essential human component. You may be new to forecasting, or you may have mastered the statistical theory behind the software’s predictions, and even more advanced “power user” techniques for the software itself—but your forecasts will never reach peak accuracy unless you master the complex judgement calls that the software cannot make. Profit From Your Forecasting Software addresses the issues that arise regularly, and shows you how to make the correct decisions to get the most out of your software.

Taking a non-mathematical approach to the various forecasting models, the discussion covers common everyday decisions such as model choice, forecast adjustment, product hierarchies, safety stock levels, model fit, testing, and much more. Clear explanations help you better understand seasonal indices, smoothing coefficients, mean absolute percentage error, and r-squared, and an exploration of psychological biases provides insight into the decision to override the software’s forecast. With a focus on choice, interpretation, and judgement, this book goes beyond the technical manuals to help you truly grasp the more intangible skills that lead to better accuracy.

• Explore the advantages and disadvantages of alternative forecasting methods in different situations
• Master the interpretation and evaluation of your software’s output
• Learn the subconscious biases that could affect your judgement toward intervention

• Find expert guidance on testing, planning, and configuration to help you get the most out of your software

Relevant to sales forecasters, demand planners, and analysts across industries, Profit From Your Forecasting Software is the much sought-after “missing piece” in forecasting reference.

✦ Table of Contents

Content: 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 Data2.1 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 Method4.3.3 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

✦ Subjects

Sales forecasting.;Sales management -- Data processing.;BUSINESS & ECONOMICS -- Industrial Management.;BUSINESS & ECONOMICS -- Management.;BUSINESS & ECONOMICS -- Management Science.;BUSINESS & ECONOMICS -- Organizational Behavior.