1. Introduction to Business Forecasting and Predictive Analytics
Chapter 1
Introduction to Business Forecasting and Predictive Analytics
© 2019 McGraw-Hill Education. All rights reserved. Authorized only for instructor use in the classroom. No reproduction or further distribution permitted without the prior written consent of McGraw-Hill Education. 1

2. Approach Forecasting Realistically
When we make a forecast one thing that we know is that we will almost surely be wrong. Our goal should be to provide better information than can be obtained otherwise. 2 2

3. From Data to Action 1
This course is primarily about turning “data” into “information.”
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4. An Effective Forecasting Process
If you can get the forecast “right,” you have the potential to get the rest of the supply chain right. Think of forecasting as a set of tools that helps decision makers make the best possible judgments about future events. Quantitative methods have been shown to out perform qualitative methods in making predictions about the future course of events for most situations. 4 4

5. Types Of Forecasting Methods
Judgmental Methods – an overview. Extrapolative Methods – Project the past pattern in the data into the future. Explanatory Methods – Take causality into consideration – based on regression analysis. 5 5

6. Using Judgments With Quantitative Methods
To captures forces that cannot be replicated in quantitative models. To improve quantitative methods. At the beginning and end of a forecasting process 6 6

7. DATA CLEANSING
Data cleansing: adjust historical data based on business knowledge and some statistical tests.
Data cleansing includes:
Replacing Outliers
Replacing Missing Values
Removing Leading/Trailing Zeros 7 7

8. DATA CLEANSING EXAMPLE - 1
An outlier is a data point that sits far away from the mean of the data. Statistical outliers are most easily found by plotting the data. Any data point that falls outside ± 2 standard deviations is usually considered an outlier.
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9. DATA CLEANSING EXAMPLE - 2
Sometimes we have a missing data point. The solution depends on whether the data are stationary, have a trend, and/or have seasonality.
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10. DATA CLEANSING EXAMPLE – 2b
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11. Qualitative versus Qualitative Predictions
Qualitative: (Subjective or Judgmental) versus Quantitative (Objective) Quantitative forecast methods have been repeatedly shown to do better than qualitative methods 11 11

12. TWO COMMON MEASURES of FIT and/or ACCURACY
Root Mean Squared Error
Mean Absolute Percentage Error
There is no need to remember the equations since forecasting software will calculate the results for you. However, it is good to have an understanding of the calculation so that you may interpret the results correctly. 12 12

13. Steps to improved forecasts
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