Forecasting

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).

General statements about forecasting

• Assumes causal systempast ==> future

• Forecasts rarely perfect because of randomness (Be ready to react).

• Forecasts more accurate forgroups vs. individuals

• Forecast accuracy decreases as time horizon increases

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

The good forecasting system…

+ cost effectiveness

Timely

AccurateReliable

Mea

ningfu

l

Written

Easy

to u

se

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”

Forecast accuracy (measures of goodness)

Forecast error: et = At – Ft

Mean absolute deviation: MADn

FAMAD

n

ttt

1

Mean squared error: MSE

Mean absoulute percent error: MAPE

1

)(1

2

n

FAMSE

n

ttt

n

A

FA

MAD

n

t t

tt100

1

MAPE

Period Actual Forecast (A-F) |A-F| (A-F)^2 (|A-F|/Actual)*1001 217 215 2 2 4 0.922 213 216 -3 3 9 1.413 216 215 1 1 1 0.464 210 214 -4 4 16 1.905 213 211 2 2 4 0.946 219 214 5 5 25 2.287 216 217 -1 1 1 0.468 212 216 -4 4 16 1.89

-2 22 76 10.26

MAD= 2.75MSE= 10.86

MAPE= 1.28

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

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

Forecast Variations

Trend

Irregularvariation

Seasonal variations

908988

Cycles

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

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))

Techniques of averaging(smoothing variations in the data)

• (Simple) Avarage

• Moving average

• Weighted moving average

• Exponential smoothing

Average

Time

Variable

Moving average

Ft = (At-n+…+At-2+At-1)/n

Example (3 yrs):1. € 42

2. € 40

3. € 43

4. € 40

5. € 41

6. €?

New data:

6. € 38

7. ?

New data:

7. € 37

8. ?

What would be the forecast with 5 years moving average?

0

1

2

3

4

5

6

0 1 2 3 4 5 6

If the trend is permanent

Mottó: egy biztos – minden bizonytalan

Az előrejelzés a jövőbeni események megjósolásának tudománya és művészete

Miért tudomány?

Miért művészet?0

1

2

3

4

5

6

0 1 2 3 4 5 6

Changing trend

Weighted moving average

• Előrejelzés2.104

5

625

321

11031002951

• Súlyok:

• Data: Aug. 95

Sept. 100

Oct. 110

Nov. ?

Time Present -1 -2

Weight 3 2 1

Forecast:

Weights:

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)

)F - (A F 1-t1-t1-t tF

Linear trends

Ft = a + btwhere

And where n = number of periods, y = value of the time series

Nonlinear trends

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

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

Sources of Forecast errors

• Model may be inadequate

• Irregular variations

• Incorrect use of forecasting technique

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