1. Forecasting

2. Introduction What: Forecasting Techniques Where: Determine Trends
Why: Make better decisions

3. What is Forecasting?
The art and science of predicting future events

4. Time Horizon Short Range – 3 – 12 months
Medium Range – 3 months – 3 years
Long Range – 3+ years

5. Where do we Use Forecasts?
Economic Forecast
Inflation rate
Exchange Rate
Technological Forecast
Probabilities of new discoveries
Time to commercialize technologies
Demand Forecast

6. Impact of Forecasts
Human Resources: forecast gives warning of need to hire or lay off
Production Capacity: forecast gives warning of need for more or less capacity
Supply Chain: forecast gives warning of need for more or less inputs to production

7. How to Make a Forecast Determine use of forecast
Select variable to be forecasted
Determine time horizon
Select forecasting model
Gather data
Make forecast
Implement results and review model

8. Qualitative Methods Jury of Executive Opinion Sales Force Composite
Delphi
Consumer Marketing Survey

9. Quantitative Methods
Time Series
Associative

10. Time Series Methods
A sequence of evenly spaced data points (weekly, monthly, quarterly, etc)
Future values predicted only from past values
X axis is always time

11. Example of a Time Series
Year 1 2 3 4
Seasonal peaks
Trend component
Actual demand line
Average demand over four years
Demand for product or service
Random variation

12. Trend Upward or downward pattern
Due to changes in income, population, technology, etc
Several years duration
Time
Demand
© T/Maker Co.

13. Seasonality Repeating pattern over a period
Could be quarterly, monthly, weekly
Due to weather or customs
Time
Demand
Summer
© T/Maker Co.

14.  Cycles Pattern that occurs over several years
Affected by political events or international turmoil
Time
Demand
Cycle 

15. Random Variations Erratic, unsystematic
Caused by random chance and unusual situations
Short duration, non-repeating

16. Naïve Approach
Forecast for next period is the same as demand in most recent period

17. Moving Average ApproachMA n  
Demand in
Previous
Periods

18. Weighted Moving Average
Σ(Weight for period n) (Demand in period n)
WMA =
ΣWeights

19. Exponential Smoothing
Ft = Ft-1 + (At-1 - Ft-1)

20. MAD

21. MSE

22. Exponential Smoothing With Trend Adjustment
Ft = (At) + (1- )Ft-1 + Tt-1
Tt = (Ft - Ft-1) + (1- )Tt-1

23. Linear Trend Projection
Equation:
Slope:
Y-Intercept:

24. Seasonal Variations
Calculate average historical demand for each season
Compute average demand over all periods
Compute a seasonal index – historical demand / average demand
Estimate next year’s total demand
Divide estimate by number of seasons, multiply by seasonal index

25. Regression Analysis An associative method
Find the relationship between an independent variable and a dependant variable
Independent variable is a variable other than time

26. Regression Analysis
Equation:
Slope:
Y-Intercept:

27. Standard Error of Estimate

28. What Does Standard Error Mean?
Standard Deviation of data forming the regression line.
If error becomes large, regression data is widely dispersed and less reliable

29. Correlation Coefficient

30. What Does Correlation Coefficient Mean?
Strength of linear relationship between independent variable and dependant variable
A number between +1 and -1

31. What Does Correlation Coefficient Mean?
-1.0
+1.0
Perfect Positive Correlation
Increasing degree of negative correlation
-.5
+.5
Perfect Negative Correlation
No Correlation
Increasing degree of positive correlation

32. Coefficient of Determination
Percent of variation in dependant variable that is explained by the regression equation

33. Evaluating the Forecast
Monitor the forecast with a tracking signal
= RSFE / MAD
Small deviations are ok and should cancel each other out over time
A consistent tendency for the forecast to be higher or lower than actual values is called a bias error

34. Tracking Signal Limits
+/- 2, 3 or 4 MAD’s
Smaller range = less tollerance of error
But smaller range = higher costs

35. Other Ways to Forecast
Adaptive Smoothing – Exponential smoothing constants adapted when tracking signal outside limits
Focus Forecasting – Computer tries all forecast methods and selects best fit for next month’s forecast