Seasonal indexes are estimates of how much the demand during a particular season will be above or below the average demand for the item. The indexes are created and edited manually by users of the system. Then the profiles are automatically normalized before being used in any models. A season profile is normalized like this:
When N = number of periods per year and P = the seasonal index, a
normalized seasonal profile must have this property:
Whenever any of the seasonal indexes are changed or created, the profile is immediately
normalized. When 
is the seasonal index to
be normalized, the normalization method is:
When the automatic season profile is chosen, the part must first pass the season component detection algorithm (see detection under 2 years, and detection over 2 years). When the part has passed the season component detection algorithm then the part gets its own seasonal profile used in the forecasting of the part. (see making seasonal profile). The automatic season profile uses the same parameter values as the Automatic Generation of Season Indexes explained below. A good rule of thumb is to have the periods limit set at least 2 years of historical values. The only parameter that you then should consider is to change the partProfileO2Limit parameter that controls how strict the season profile detection algorithm should be. This value defaults to 85% certainty that when a part gets a seasonal profile that part has a seasonal profile, this value may be to strict in some cases.
This function is started only from the server GUI. It works like this:
The result of this function can be altered by changing some parameters in the registry. Before changing any of these parameters - especially PartProfileU2LimitIndex1, PartProfileU2LimitIndex2, partProfileO2Limit, and matchLimit - be sure that you understand how they work and what effects the changes will have. Note that the parameters MUST be changed/revised if you are using if you are planning on having a matchLimmit under 2 years.
This parameter states the minimum number of historical periods a part must have to be included in the automatic generation. The default value is set to 4/24/52 depending on what period version you are using. With this setting the parameters PartProfileU2LimitIndex1 and PartProfileU2LimitIndex2 have no effect because they do the detection under 2 years this is the default setting.
This parameter states the minimum number of parts a seasonal profile must have to be reported out as a result of the generation. This parameter is used to prevent the generation of profiles that only have one part that is using it. The default value is set to 3.
This is an indicator used for the algorithm that computes parts with under two years of demand. If the PeriodsLimit is set to 2 times the periods per year this parameter is never used and therefore has no meaning. This is used mainly for ruling out parts with extreme trends from consideration for seasonal profiles. The higher the number, the more strict the algorithm gets. The default value is 1.11.
This indicator is used for the algorithm that computes parts with under two years of demand. If the PeriodsLimit is set to 2 times the periods per year this parameter is never used and therefore has no meaning. This is the main parameter for the profile determination; the higher the number, the more strict the algorithm gets. The default value is 1.2.
Works together with the PartProfileLowerO2Limit to give a better more stabile automatic seasonal profile. It works like this: For a part to get a seasonal profile used in its forecat calculation it needs to get a seasonal indicator above the PartProfileUpperO2Limmit. Once the part has qualified above this limmit then it will have a seasonal profile used in its forecast calculation until it gets a value below the PartProfileLowerO2Limmit the part will then loose the seasonal profile in its forecast calculation. When a part has lost its profile it will need to get a profile indication above the PartProfileUpperO2Limmit to gain a seasonal profile in its forecast calculation.
This is the upper limmit that a part that did not have a seasonal component before has to be over before a seasonal profile will be aplied when using the automatic seasonal profile on a part.
This means that using the default numbers of PartProfileUpperO2Limit (1.05) and PartProfileLowerO2Limit (0.7) this will make the automatic season profile work like this. For a part to get a seasonal component the part needs to get a indicator above 1.05 to recive a seasonal profile in its forecast calculation, for a part that allready have been above 1.05 (has a seasonal profile applyed) it will need to get a indicator below 0.7 for that part to loose its seasonal komponent.
This indicator is used for the algorithm that computes parts with more than two years of demand. When setting this parameter, it can be useful to look at the normal distribution. A value of 1.96 gives 97,5% accuracy (see the normal distribution); this means that there is a 2,5% chance that the seasonal component is only noise. The default value is 1.05 (Gives 85% accuracy) . Reduce this value If there are many parts that have seasonal components but don't receive a profile when they are set to use the automatic seasonal profile. A value of 0.25 will give you 60% accuracy, but note that there is a 40% chance that the parts that received a seasonal profile simply have noise instead of a seasonal component. Note that this number MUST be lower than or equal to the PartProfileLowerO2Limit.
Works together with the PartProfileUpperO2Limit to give a better more stabile automatic seasonal profile. It works like this: For a part to get a seasonal profile used in its forecat calculation it needs to get a seasonal indicator above the PartProfileUpperO2Limmit. Once the part has qualified above this limmit then it will have a seasonal profile used in its forecast calculation until it gets a value below the PartProfileLowerO2Limmit the part will then loose the seasonal profile in its forecast calculation. When a part has lost its profile it will need to get a profile indication above the PartProfileUpperO2Limmit to gain a seasonal profile in its forecast calculation.
This is the lower limmit the limmt that a part have had a seasonal profile the last period has be lower than to loose it's seasonal component.
This means that using the default numbers of PartProfileUpperO2Limit (1.05) and PartProfileLowerO2Limit (0.7) this will make the automatic season profile work like this. For a part to get a seasonal component the part needs to get a indicator above 1.05 to recive a seasonal profile in its forecast calculation, for a part that allready have been above 1.05 (has a seasonal profile applyed) it will need to get a indicator below 0.7 for that part to loose its seasonal komponent.
This indicator is used for the algorithm that computes parts with more than two years of demand. When setting this parameter, it can be useful to look at the normal distribution. A value of 1.96 gives 97,5% accuracy (see the normal distribution); this means that there is a 2,5% chance that the seasonal component is only noise. The default value is 1.05 (Gives 85% accuracy) . Increase this value If there are many parts that gets seasonal profile but does not seem to have a seasonal component when they are set to use the automatic seasonal profile. A value of 0.25 will give you 60% accuracy, but note that there is a 40% chance that the parts that received a seasonal profile simply have noise instead of a seasonal component. Default valu is 0.7 (gives 75% accuracy). Note that this number MUST be lower than or equal to the PartProfileUpperO2Limit.
This parameter is used to decide how strict the matching of two profiles is. The lower the number, the more unequal the profiles are allowed to become. The higher the number, the more similar the profiles must be to be added together. The default value is 1.05. See detailed explanation above (.../SeasonProfiles/partProfileO2Limit). A lower number will result in more general season profiles will be generated (fewer profiles), the higher number the more specialized profiles will be generated (higher number of profiles). Note the number of profiles generated is also dependent on the NumberLimit se Above.
A detailed explanation about how to create seasonal profiles follows.
A centered moving average with the number of periods per year is executed. If months are used, then the moving average weights over a year look like this (the number in boldface is the center): 1/12[0.5, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.5]
Below are the formulas used; the table shows an example.
Example table:
| Period | Demand | 12-month centered | 12 season ratio |
199001 |
112 |
NA |
NA |
199002 |
118 |
NA |
NA |
199003 |
132 |
NA |
NA |
199004 |
129 |
NA |
NA |
199005 |
121 |
NA |
NA |
199006 |
135 |
NA |
NA |
199007 |
148 |
126.7916667 |
1.167269142 |
199008 |
148 |
127.25 |
1.163064833 |
199009 |
136 |
127.9583333 |
1.062845979 |
199010 |
119 |
128.5833333 |
0.925469864 |
199011 |
104 |
129 |
0.80620155 |
199012 |
118 |
NA |
NA |
199101 |
115 |
NA |
NA |
199102 |
126 |
NA |
NA |
This algorithm is preformed only if the part has at least one year plus one period of historical demand to considered. The algorithm below is used for parts with demand from 1 year (+ one period) up to 2 years.
n Number of demand periods
D Demand
Filtered demand Df where R is the regression line for the demand:
Run a centered 3 moving average on the filtered demand (see formula).
If (indicator1 > Registry limit1 and indicator2 > Registry limit2) then
Part has season
Else
Part has no season
The setting of indicator 1 and indicator 2 will affect how strict the algorithm is (how many parts that have seasonal component) The higher Registry limit 1 and 2 become, the more strict the algorithm gets. Indicator 2 is the main parameter for the profile, and is the parameter that should be changed first to get a more strict algorithm. Indicator 1 is mainly for removing parts with extreme trends from consideration for seasonal profiles. Registry Limit 1 and Registry Limit 2 are adjustable in registry key HKEY_LOCAL_MACHINE/SOFTWARE/IFS/ForecastServer/SeasonProfiles/PartProfileU2LimitIndex1 and HKEY_LOCAL_MACHINE/SOFTWARE/IFS/ForecastServer/SeasonProfiles/PartProfileU2LimitIndex2.
For parts that have over 2 years with demand, we compute the autocorrelation factor of the demand. The autocorrelation factor of lag equal to the number of periods per year,12 (if a monthly period is used). This laged autocorrelation factor is compared to a limit, to get 85% accurancy the formula 1.05/sqrt(number of demand periods). The number 1.05 (see a normal curve distribution table) gives 85 % accurancy, to change the accurancy look at a normal distribution table and find the number for the wanted accurancy and use that instead of 1.05. Below are the formulas.
The part has a seasonal component if r(k) > Limit (k = no of periods per year). The partProfileO2Limit can be changed from registry key=HKEY_LOCAL_MACHINE/SOFTWARE/IFS/ForecastServer/SeasonProfiles/partProfileO2Limit. When setting this parameter it can be useful to look at the normal distribution. A value of 1.05 gives 85% accurancy.
Testing whether two seasonal profiles can be matched proceeds like this: P1 and P2 are the seasonal profiles. The two profiles are combined for a two-year-long demand array, then the autocorrelation factor is taken at lag N (N is the number of periods per year). Then this factor is compared to the MLimit. If the factor is higher then the two profiles match; if it is lower then there is no match. The matchLimit can be changed from registry. Key=HKEY_LOCAL_MACHINE/SOFTWARE/IFS/ForecastServer/SeasonProfiles/matchLimit.
It is also possible to set the minimum number of demand periods a part must have to be considered by the algorithms. This is also done from registry. Key=HKEY_LOCAL_MACHINE/SOFTWARE/IFS/ForecastServer/SeasonProfiles/PeriodsLimit. The last parameter that can be set in registry is the minmum number of parts a resulting profile must contain. This is to prevent an automatic profile generation run from creating manny profiles that contain only one part. Key=HKEY_LOCAL_MACHINE/SOFTWARE/IFS/ForecastServer/SeasonProfiles/NumberLimit.
When performing best fit seasonal profile search, the profile with the highest Mlimit wins the contents and becomes the best matching profile for the current part. If Mlimit is negative for all profiles then no profile is set as best fit for that part.
The Gauss filter smoothes the peaks of a season profile. The graph below shows a typical shape of a gauss filter. This function can be ran several times the season profile will then become more and more smooth.
The above formula is the formula for the Gauss filter used.
a is a alpha value that will vary with the period version used.
If the period version is under 10 periods per year a=3
All other period version lengths
a= (-0.002537136 * number of periods per year) + 0.931143734
If this gets below 0.005 then a is set to be 0.005.
X is the period number of the seasonal profile.