1. Forecasting

2. Forecast…
is a statement about the future value of a variable of interest (such as demand).
affects decisions and activities throughout an organization.
reduces uncertainty (replaces data).

3. General statements about forecasting
Assumes causal system past ==> future
Forecasts rarely perfect because of randomness (Be ready to react).
Forecasts more accurate for groups vs. individuals
Forecast accuracy decreases as time horizon increases

4. Possible targets of forecasting
Factors outside the organization, that are hard to change
eg. weather, inflation, unemployment
Factors within the organization with a greater possibility to change.
eg. age structure, labor turnover, scrap ratio

5. The good forecasting system…
Timely
Accurate
Reliable
Meaningful
Written
Easy to use
+ cost effectiveness

6. Steps in the forecasting process
Step 1 Determine purpose of forecast
Step 2 Establish a time horizon
Step 3 Select a forecasting technique
Step 4 Gather and analyze data
Step 5 Prepare the forecast
Step 6 Monitor the forecast
“The forecast”

7. Forecast accuracy (measures of goodness)
Forecast error: et = At – Ft
Mean absolute deviation: MAD
Mean squared error: MSE
Mean absoulute percent error: MAPE
MAPE 7

9. Types of forecast Judgmental - uses subjective inputs
Executive opinions, salesforce opinions, consumer surveys etc.
Time series (time ordered sequence of observations) - uses historical data assuming the future will be like the past
Associative models - uses explanatory variables to predict the future

10. Time series forecasts Trend - long-term movement in data
Seasonality - short-term regular variations in data
Cycle – wavelike variations of more than one year’s duration
Irregular variations - caused by unusual circumstances
Random variations - caused by chance

11. Forecast Variations Trend Cycles Irregular variation 90 89 88
Seasonal variations

12. Naive forecasts
The forecast for any period equals the previous period’s actual value.
Advantages:
Simple to use
Virtually no cost
Quick and easy to prepare
Data analysis is nonexistent
Easily understandable
Cannot provide high accuracy
Can be a standard for accuracy

13. 3 uses of naive forecasts
Stable time series data
F(t) = A(t-1)
Seasonal variations
F(t) = A(t-n)
Data with trends
F(t) = A(t-1) + (A(t-1) – A(t-2))

14. Techniques of averaging (smoothing variations in the data)
(Simple) Avarage
Moving average
Weighted moving average
Exponential smoothing

15. Average
Variable
Time 15

16. Ft = (At-n+…+At-2+At-1)/n
Moving average
Ft = (At-n+…+At-2+At-1)/n
Example (3 yrs):
€ 42
€ 40
€ 43
€ 41 €?
New data:
6. € 38
7. ?
New data:
7. € 37
8. ?
What would be the forecast with 5 years moving average?

17. If the trend is permanent17

18. Mottó: egy biztos – minden bizonytalan
Changing trend
Mottó: egy biztos – minden bizonytalan
Az előrejelzés a jövőbeni események megjósolásának tudománya és művészete
Miért tudomány?
Miért művészet? 18

20. Weighted moving average
Data:
Aug. 95
Sept.
100
Oct.
110
Nov. ?
Előrejelzés
Súlyok:
Weights:
Time
Present -1 -2
Weight 3 2 1
Forecast: 20

21. Exponential smoothing
New forecast = forecast for the previous period + α*error
Where the error is = actual data for the last period – forecasted data for the last period
α: smoothing constant (usually 0.05<α<0.5) 21

23. Linear trends Ft = a + bt where
And where n = number of periods, y = value of the time series

25. Nonlinear trends

26. Associative Forecasting
Predictor variables - used to predict values of variable interest
Regression - technique for fitting a line to a set of points
Least squares line - minimizes sum of squared deviations around the line

27. Controlling the Forecast
Control chart
A visual tool for monitoring forecast errors
Used to detect non-randomness in errors
Forecasting errors are in control if
All errors are within the control limits
No patterns, such as trends or cycles, are present

28. Sources of Forecast errors
Model may be inadequate
Irregular variations
Incorrect use of forecasting technique

29. Choosing a Forecasting Technique
No single technique works in every situation
Two most important factors
Cost
Accuracy
Other factors include the availability of:
Historical data
Computers
Time needed to gather and analyze the data
Forecast horizon