Profit From Your Forecasting Software A Best Practice Guide for Sales Forecasters 1st edition by Paul Goodwin ISBN 1119416005 9781119416005

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Product details:

ISBN 10: 1119416005
ISBN 13:  9781119416005
Author: Paul Goodwin

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.

Profit From Your Forecasting Software A Best Practice Guide for Sales Forecasters 1st Table of contents:

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

1.4 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

2.3 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

3.3 FORECAST ERRORS AND BIAS MEASURES

3.4 DIRECT ACCURACY MEASURES

3.5 PERCENTAGE ACCURACY MEASURES

3.6 RELATIVE ACCURACY MEASURES

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

4.3 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

5.8 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

6.3 MULTIPLE REGRESSION

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

7.4 WHAT IF THE PROBABILITY DISTRIBUTION OF DEMAND IS NOT NORMAL?

7.5 TEMPORAL AGGREGATION

7.6 DEALING WITH PRODUCT HIERARCHIES AND RECONCILING FORECASTS

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

9.3 RESTRICT YOUR INTERVENTIONS

9.4 MAKING EFFECTIVE INTERVENTIONS

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

10.4 THE BASS DIFFUSION 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

11.3 A BLUEPRINT FOR BEST PRACTICE

11.4 REFERENCES

Index

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