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Forecast model K (Constant with smoothing factor adjustment)

Former Member
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Hi All,

i am using forecast model K in materail master. I would like to know the system calculation on forecast value?

For Example:

Consumption qty maintained in material master for last 3 months.

Period Total consumption

04.2011 100

03.2011 150

02.2011 200

When i am using Forecast model K, system is calculating as follows,

Period Orig. HV Corr. HV Ex-post FV Orig. FV

M 02.2011 200 200

M 03.2011 150 150 200

M 04.2011 100 100 155

M 05.2011 106

05.2011 Forecast value is 106, Basic value is 105.500, Error total is -105 and MAD is 27.

I would like to know the system calculation of these value. Particularly Basic value and MAD.

And also the difference between Forcast Model D and K if possible.

When i am using Forecast model D, system is calculating as follows,

Period Orig. HV Corr. HV Ex-post FV Orig. FV

M 02.2011 200 200

M 03.2011 150 150 200

M 04.2011 100 100 190

M 05.2011 172

Basic value 172.000, MAD 38, Error total -140

Thanks in advance,

Babu

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Answers (1)

Answers (1)

madlercm
Active Contributor
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MODEL K:

Constant Model with First-Order Exponential Smoothing

The constant model with first-order exponential smoothing is derived as in formula (5). A simple transformation gives the basic formula for exponential smoothing as shown in (6).

To determine the basic value, you only require the basic value from the preceding period, the last past consumption value and the alpha smoothing factor. The smoothing factor weights the most recent consumption values more than the less recent ones, so that they have a stronger influence on the forecast.

The forecast value is the basic value for the last period for which historical data is avaialble, that is the last ex-post period.

where k> n

How quickly the forecast reacts to a change in consumption pattern depends on what value you give the smoothing factor. If you set alpha to be 0, the new average is equal to the old one and the basic value calculated previously remains; that is, the forecast does not react to current consumption data. If you give alpha the value 1, the new average equals the last consumption value.

The most common values for alpha lie between 0.1 and 0.5. An alpha value of 0.5 weights past consumption values as follows:

1st historical value : 50%

2nd historical value : 25%

3rd historical value : 12.5%

4th historical value : 6.25%

and so on.

The weightings of past consumption data can be changed by one single parameter. Therefore, it is relatively easy to respond to changes in the time series.

The constant model of first-order exponential smoothing derived above is applicable to time series that do not have trend-like patterns or seasonal-like variations.

MODEL 😧

I think that simple constant model is just a normal average.

Former Member
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Can anyone explain the calcuation in detail?

Thanks,

Babu

Former Member
0 Kudos

Can anyone explain the calcuation of basic value for "1st order exp.smoothing w.constant alpha optimization"?

Thanks,

Babu

Edited by: babs on May 27, 2011 10:01 AM

Edited by: babs on May 27, 2011 10:04 AM