1. Chapter 12 - Forecasting
Forecasting is important in the business decision-making process in which a current choice or decision has future implications:
Routine decisions
very near in the future
small gains or losses
assume future is like the past

2. Introduction to Forecasting
Business is the main user of Forecasting Methods, but other areas such as State and Federal governments and non-profit organizations (university, hospital, services) use forecasting.
Marketing and operations management is the most obvious function in business to use forecasting. A valid strategy depends on demand expectations.

3. Introduction to Forecasting
As business majors, you operate and make decisions within the framework of a complex, interrelated, social, economic, and competitive structure.
The success of a firm depends on its ability to compete with firms producing similar products or services from the same market.
Firms must secure information concerning potential market sales to plan effectively.

4. Introduction to Forecasting
Sales forecasts become the primary information input depicting the state of the environment.
The better and more complete the data, the better the decision will be.

5. Introduction to Forecasting
Forecasting alleviates uncertainty:
Long and short term forecasts
Forecasts relating to industry trends
Market research relating to consumer surveys
Advertising & Sales promotions
Demand Anticipation
Inventory levels

6. Introduction to Forecasting
Horizontal Pattern
No trend, stationary
Equally likely chance that the next value will be above the mean or below it
Stable sales, # of defects in production process

7. Introduction to Forecasting
Seasonal
Fluctuations occur in certain months/quarter during the year. Examples: weather, holidays

8. Introduction to Forecasting
Cyclical
Similar to seasonal, but the length of cycle is longer than one year: Housing starts, GNP
Difficult to predict because it does not repeat itself at constant intervals

9. Introduction to Forecasting
Trend
General increase or decrease in value over time
Examples: sales, stock

10. Accuracy of Techniques & Measurement Error
There will always be some deviation between actual and forecasted values. Our objective is to minimize the deviations with sound analysis
Errors are squared to eliminate signs and emphasize the extreme errors

11. Types of Models Time Series
Identify historical patterns and forecast into the future. If we know that sales are 20% above average each January, the forecast for next January should be upward 20%.
This is an inappropriate method for weekly sales fluctuations that are the result of price and advertising changes.

12. Types of Models
Causal
Assumes that the value of a certain variable is a function of several other variables.
Time Series could be considered causal since actual values are assumed to be a function of the time period. Usually, variables other than time are used. For example, sales as a function of price and advertising.
This is a more complex method than time series.

13. Types of Models Statistical Models
Statistical analysis can be used to identify patterns in the variables and in making statements about the reliability of the forecast. Confidence Intervals, R2, Test of Significance
Non-Statistical Models
These models do not follow rules of statistical analysis and probability theory. Usually, they are easy to understand and apply. They are limited because they lack guidelines.
Qualitative models

14. Regression Analysis
Once a linear relationship is defined, the independent variable can be used to forecast the dependent variable. Y ^
= bo + bX
bo is called the Y intercept - represents the value of Y when X = 0. But be cautious - this interpretation may be incorrect and difficult to estimate - many times our data does not include 0. Think of this value as representing the influences of the many other independent variables that are not included in the equation.
bX is called the slope - represents the amount of change in Y when X increases by one unit.

15. Regression Analysis
Tools, Data Analysis, Regression - Hint: Include labels in the input ranges to help with the interpretation! Can also include plots (not shown here)

16. Averages
Naive models
3 and 5 month moving averages

17. Exponential Smoothing Methods
Continually revising a forecast in light of more recent experiences. Averaging (smoothing) past values of a series in a decreasing (exponential) manner. The observations are weighted with more weight being given to the more recent observations

18. Forecast Errors
MAD measures forecast accuracy by averaging the absolute value of the forecast errors (n = number of errors and not sample size). Magnitude of errors.
MSE (Mean Squared Error) - each error is squared, then summed and divided by the number of observations (errors). Identifies large forecasting errors because of the squares
MAPE (Mean absolute percentage error) - percentages. Find the MAD for EACH period then divide by actual of that period and dividing the sum by the number of errors. How large the forecast errors are in comparison to the actual values.

19. Forecast Errors

20. Tracking Signals
Indicate whether a forecast method is accurately predicting.
Tracking Signal = CFE / MAD
It is actually a control chart (see table 12.2 for limits)
Hold-out sample – used to test forecast before applying to practice

21. OMS Forecasting
Averages, Regression, Multiple Regression, Smoothing
Seasonality
Measuring Forecast Errors
Combining Forecasts
Great course! Lots of computer experience in Excel and Mini-tab. 2nd Six Weeks – MW 6-9 pm