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Overview
Forecast Models
Model Selection
Manual Model Selection
Automatic Model Selection
Manual Model Selection with Additional System Check
Model Initialization
Ex-Post Forecast
Parameter Optimization
Maintaining Forecast Parameters
Forecast Parameters: Independent of the Forecast Model
Forecast Parameters: Dependent on the Forecast Model
Creating Historical Data
Creating Forecast Values
Carrying out the Forecast with Reference to Another Material
In order to carry out the forecast for a material, you must first of all maintain the forecast parameters for this material in the material master record.
In the following section, a description is given of,
When a series of consumption values is analysed, certain patterns can usually be detected. From these patterns it is then possible to differentiate between various forecast models: A constant consumption flow applies if consumption values vary very little from a stable mean value. With a trend model, consumption values fall or rise constantly over a long period of time with only occasional deviations. If periodically recurring peak or low values which differ significantly from a stable mean value are observed, it is a case of a seasonal consumption flow. A seasonal trend consumption model is characterized by a continual increase or decrease of the mean value. If none of the above patterns can be detected in a series of past consumption values, then we have an irregular consumption flow.
Before the first forecast can be carried out, you must specify which forecast model the system is to use to calculate the forecast values.
There are three basic possibilities:
If you want to select a model manually, then you must first of all analyse past consumption data to determine whether a distinct pattern or trend exists according to which you can manually select a model for the system.
Constant requirements pattern
If your past data represents a constant consumption flow, you can then select either the constant model or the constant model with adaption of the smoothing factors. In both cases, the forecast is carried out by first-order exponential smoothing. When adapting the smoothing parameters, the system calculates different parameter combinations and then selects the optimum parameter combination which is the one which results in the lowest mean absolute deviation.
You have another two possibilities if your past consumption pattern is constant; either the moving average model or the weighted moving average model.
In the weighted moving average model, you can weight individual past consumption values, which means that the system will not give equal value to past data when calculating the forecast values. In so doing, you can influence the calculation so that the most recent consumption values play a more important role in the forecast than the previous periods - as is also the case in exponential smoothing.
Trend requirements pattern
If your past consumption data represents a trend, it makes sense to select either the trend model or the second-order exponential smoothing model. In the trend model, the system calculates the forecast values by means of the first-order exponential smoothing procedure.
In the second-order exponential smoothing models, you can choose between a model with or without parameter optimization.
Seasonal requirements pattern
If your past consumption data represents a seasonal pattern, you can specify the seasonal model you want. The system calculates the forecast values for the seasonal model by means of first-order exponential smoothing.
Seasonal trend requirements pattern
If your past consumption data represents a seasonal trend pattern, you can select the seasonal trend model you want. The system calculates the forecast values by means of first-order exponential smoothing.
Irregular requirements pattern
None of the patterns or trends mentioned in the above examples can be recognized in an irregular consumption flow. If the system is to carry out a forecast for an irregular pattern, then it is usually advisable to select either the moving average model or the weighted moving average model.
Forecast Models for Different Requirements Patterns
Requirements pattern | Forecast model |
Constant | constant model |
| constant model with smoothing factor adaption | |
| moving average model | |
| weighted moving average model | |
Trend | trend model (first-order exponent smoothing) |
Irregular | no forecast |
| moving average model | |
| weighted moving average model | |
Extended forecasting component used: | |
Trend | second-order exponential smoothing model (with and without parameter optimization) |
Seasonal | seasonal model |
Seasonal trend | seasonal trend model |
If you do not want to specify a forecast model manually, you must instruct the system to make an automatic selection. The system then analyzes the historical data for different patterns and selects the appropriate model. The following models are possible:
If the system does not detect any regular pattern in the past consumption data, it automatically selects the constant model.
The system requires a different amount of historical values for the individual tests. For more information on the exact number that it requires, see Model Initialization.
Model Selection Procedures
If the system is to make an automatic model selection, you have the choice between two model selection procedures:
Procedure 1
The system carries out statistical tests and checks whether a trend or a seasonal requirements pattern applies.
In the trend test, the system subjects the historical values to a regression analysis and checks to see whether there is a significant trend pattern.
In the seasonal test, the system clears the historical values of any possible trends and carries out an autocorrelation test.
Procedure 2
The system calculates the models to be tested using various combinations for alpha, beta and gamma. The smoothing factors are also varied between 0.2 to 0.8 in intervals of 0.2.
The model which is then chosen is the the model which displays the lowest mean absolute deviation (MAD).
Procedure 2 is more precise than procedure 1 but takes considerably longer.
In the third case, you can have a combination of manual and automatic model selection: you specify a model and set the system so that it additionally checks historical values for a seasonal pattern or trend.
Possible combinations that you can choose are as follows:
A reference is given to the forecast formulae used by the system for the individual models in Forecast Formulae.
Model initialization is the process of specifying the necessary model parameters, such as the basic value, the trend value and the seasonal indices for the selected forecast model. In each case, initialization occurs during the first forecast for a material. It must also be carried out in cases of structural interruption, that is, if the existing forecast becomes invalid. Which model parameters are necessary for which forecast model is shown in the following Table: Model Parameters
Model | Model parameters |
Constant model | basic value |
Trend model | basic value, trend value |
Seasonal model | basic value, seasonal indices |
Seasonal trend model | basic value, trend value, seasonal indices |
The forecast model is usually automatically initialized. In order to do this, the system requires a certain amount of historical values. The number required varies depending on the forecast model as shown in the table below.
Number of Historical Values Required for Model Initialization
Model | No. of historical values |
Constant model | 1 |
Trend model | 3 |
Seasonal model | 1 season |
Seasonal trend model | 1 season + 3 |
2nd-order exp. smoothing | 3 |
Moving average | 1 |
Weighted moving average | 1 |
The system calculates the basic value on the basis of an average value, and the trend using the results of the regression analysis. The seasonal indices result from the quotient of the actual past value and the basic value which has been adjusted for the trend value.
These calculation methods are used for the constant, trend, seasonal and seasonal trend models, depending on which parameters are to be determined.
For the second-order exponential smoothing model, a regression analysis is carried out.
For the moving average and the weighted moving average models, the system calculates an average value.
If more past values are available than the system requires or is to use for model initialization (refer to "Number of historical values" and "Initialization periods" in Forecast Parameters: Independent of the Forecast Model), then the system will carry out an ex-post forecast. In order to do this, the system divides the time series of past values into two groups. The first group with the older values is used for initialization. The system carries out an ex-post forecast using the second group as it is best to adapt the parameters to the most recent developments.
Furthermore, you can monitor the forecast model during the initialization phase by comparing the forecast values from the ex-post forecast with the actual consumption values.
The ex-post forecast does not only play an important role in the initial forecast. It is also significant for subsequent forecasts. It is, therefore, possible to omit periods during the forecast calculation. This means that you can determine, for example, daily forecast values for a material even if you only execute a weekly forecast run for this particular material. The system forecasts the missing periods in retrospect. The ex-post forecast is only possible for models with the exponential smoothing procedure.
For forecast models with exponential smoothing, you can instruct the system to optimize the smoothing factors (see Forecast Parameters: Dependent on the Forecast Model).
If the system is instructed to optimize the smoothing factors, it calculates several parameter combinations and then selects the combination with the lowest mean absolute deviation (MAD). The finer the degree of optimization, the more exact, but also the more time-consuming, the parameter optimization. You can vary the increment (also known as the degree of optimization) from 0.1 (low), 0.2 (medium) and 0.3 (high).
If you have set the system correctly, it will carry out parameter optimization during initialization for the initial forecast as well as for all other forecasts. The most effective combination of the smoothing factors is determined by means of the ex-post forecast.
Please refer to Appendix B in this manual for additional information on the calculation of the MAD as well as the significance of the smoothing factors.
Optimizing the smoothing factor is only possible for models with the exponential smoothing procedure.
You maintain forecast parameters in the material master record. In order to do this, proceed as is described below:
The pop-up window Select View(s) appears on your screen.
The Organizational Levels/Profiles pop-up window now appears.
The "Forecasting" data screen now appears.
A more detailed description is given of the individual forecast parameters in the follwoing sections:
Forecast Parameters: Independent of the Forecast Model
Forecast Parameters: Dependent on the Forecast Model
For more information on maintaining the material master record, please refer to MM Managing Material Master Data.
You can/must maintain the following parameters independent of the method you use for model selection and which model you choose.
Period indicator
The period indicator determines the time interval the system is to use to store consumption or forecast values. You can define the period indicator when maintaining the MRP parameters as well as when maintaining the forecast parameters.
Fiscal year variant
You must maintain the fiscal year variant if you want a flexible period length for the material equal to that of the accounting period.
Historical values
You specify how many historical values the system should take into account for the forecast by filling in this field. The default value in this case is the maximum number possible (60). If fewer values are available, then they are used for initialization.
Initialization periods
You specify the number of periods the system is to use for model initialization here. If the number of historical values is greater than the number of initialization periods, then the system will carry out an ex-post forecast for those periods which were not used for initialization. The number of periods that the system requires for initialization depends on the forecast model.
Forecast periods
Via the number of forecast periods, you specify for how many periods the system determines forecast values.
Fixed periods
Via the number of fixed periods, you determine how many periods are fixed in the future and are no longer automatically changed by the next forecast run.
Initialization
This field activates re-initialization during the forecast. This is necessary for the first forecast and also when structural changes (that is, if the forecast model which has been used until now has lost its validity) appear in the time series.
Model initialization can either be carried out automatically (X) or manually (M). In the case of manual initialization (which should be the exception), you must determine the model parameters manually. To do this, select Details ® Forecast details ® Model parameters. Depending on the forecast model, you can manually maintain such parameters as the basic value and the trend in the pop-up window. If you do not set these parameters here, you will be requested to set them during the forecast. No historical data has to be stored in the system in order to carry out manual initialization.
Tracking limit
During the forecast calculation, the system compares the tracking limit with the quotient of the error total and the mean absolute deviation (MAD). This quotient is known as the tracking signal. If the value calculated by the system lies below that of the tracking limit, then the forecast remains valid. If not, the system generates an exception message which indicates that a structural interruption has taken place. This means that the forecast model should be revised and possibly re-initialized.
Autom. reset
Causes the system to reset the forecast model automatically if the tracking limit is exceeded. Furthermore, the system carries out a new model selection during the next forecast.
Correction factors
If you fill in this field, historical values and forecast values are weighted with the appropriate period factors which you determined via customizing.
Reference material for consumption
If you define a reference material, the system accesses the historical data of the reference material up to the specified validity date. This makes sense, if no historical data exists yet for the specified material.
Reference plant for consumption
If you enter a reference material for consumption, you must also define a reference plant where the historical data to be used comes from.
Validity date
During the forecast, the system accesses the historical data of the reference material up to the specified validity date. From this date, it uses the material’s own historical data.
Multiplier
By entering a multiplier, you can define that only a certain percentage of the consumption quantity of the reference material is used in the forecast.
You can/must maintain the following parameters depending on how you carry out model selection and which model you choose. The relationship between parameters and model selection is shown in the following two tables. Parameters Dependent on Model Selection
Model selection | Parameters |
Manual model | forecast model selection |
Automatic | model selection, model selection procedure model selection |
Parameters Dependent on the Forecast Model
Specified model/ model to be tested | Possible/required parameters |
Constant model | parameter optimization |
| optimization level | |
| alpha and delta factors | |
Constant model with optimization of the smoothing factors | - |
Trend model | parameter optimization |
| optimization level | |
| alpha, beta and delta factors | |
Moving average model | initialization periods |
Weighted moving average | weighting group model |
Extended forecast component used: | |
Seasonal model | parameter optimization |
| optimization level | |
| alpha, beta and delta factors | |
Seasonal trend model | parameter optimization |
| optimization level | |
| alpha, beta, gamma and | |
| delta factors | |
2nd-order exponential smoothing model | parameter optimization |
| alpha and delta factors | |
2nd-order exponential smoothing model with smoothing factor optimization | alpha and delta factors |
Forecast model
You determine via the forecast model which model the system uses as a basis when calculating the forecast values. If you do not know the forecast model, you can have it determined by the system via automatic model selection.
Model selection
This indicator specifies for which model the system is to examine the historical values. You can specify whether the system searches the historical values
Please note that depending on the model test, a minimum number of historical values must be available. This field is significant if you do not know the model and you want the system to determine it automatically. Furthermore, you also have the possibility of pre-selecting a trend model, but at the same time instruct the system to search for a seasonal pattern and vice versa.
Selection procedure
This indicator specifies how the system is to carry out the model selection. Here, you can choose between two procedures:
Parameter optimization
Via this indicator, you can specify that the system is to optimize the necessary smoothing factors for the appropriate model. The system calculates several parameter combinations and selects the one that displays the lowest MAD. Parameter optimization is carried for the initial forecast as well as for the subsequent forecasts.
Periods per seasonal cycle
You must enter the number of periods that constitute a season here if you have selected a seasonal model or if the system is to carry out a seasonal test.
Optimization level
By determining the optimization level, you are specifying the increment with which the system is to carry out parameter optimization. The lower the increment, the more exact but also the more time consuming the optimization process will be.
Weighting group
You only have to maintain this field if you selected the forecast model, "weighted moving average". This key specifies how many historical values are taken into account for the forecast and how these values are weighted in the forecast calculation.
The following factors are used by the system, depending on the model, for exponential smoothing. Thus, for example, only the alpha and the delta factors are required for the constant model whereas all of the smoothing factors are required for the seasonal trend model.
Alpha factor
The system uses the alpha factor for smoothing the basic value. If you do not specify an alpha factor, the system will automatically use the alpha factor 0.2.
Beta factor
The system uses the beta factor for smoothing the trend value. If you do not specify a beta factor, the system will autimatically use the beta factor 0.1.
Gamma factor
The system uses the gamma factor for smoothing the seasonal index. If you do not specify a gamma factor, the system will automatically use the gamma factor 0.3.
Delta factor
The system uses the delta factor for smoothing the mean absolute deviation and the error total. If you do not specify a delta factor, the system will automatically use the delta factor 0.3.
If you set parameter optimization, the system will overwrite the originally set smoothing factors with those which have been newly calculated by the optimization process.
Before you can carry out the forecast for an article, the following prerequisites must be fulfilled:
For a more detailed description of these prerequisites, see Overview
Before carrying out the forecast, you also have the possibility of revising an article's historical values which are normally automatically updated by the system during withdrawal postings. On the one hand, you can achieve this by doing one of the following;
On the other hand, you can reduce the number of periods to be taken into consideration by limiting the number of historical values in the article master. This means that not all of the historical values are used for the forecast, and instead, only the number of values that you enter.
If a structural interruption appears in the time series of past values stored in the article master record, and if for the next forecast you only want the system to take those values into account which lie after the interruption, then you do this by entering the appropriate number of past time periods in the Historical values field.
Consumption values not determined by the SAP System can be maintained directly with the following exception.
Consumption values for the period in which the article was created as well as in the previous period. These periods can only be influenced by consumption postings for the respective period.
There are several possibilities available to you for carrying out the article forecast:
Carrying Out the Individual Forecast
Carrying Out the Total Forecast
Carrying Out the Total Forecast in Background Mode
You can also carry out the article forecast from the forecast screen of the customized article master. Depending on the forecast parameters maintained here, you can carry out the forecast in simulative mode in several online steps. The functionality used for this is that of the single-item forecast for an article.
The forecast values are posted when you save the article master record. This also automatically adjusts the two RP parameters, the reorder level and the safety stock level.
Carrying out the forecast for a particular article in a particular site is known as the individual forecast. You carry out an individual forecast as is described below:
The initial Forecast screen appears.
The Parameter Overview screen now appears. This screen displays the following information:
– the last forecast date
– data, copied from the article master record
– parameter values, which were calculated as a result of the previous forecast
If no forecast has yet been carried out for this article, the Last forecast field is empty and the message Until now no forecast values were saved appears.
From this screen you can display the historical values by selecting Goto ® Historical values and if a forecast has already been carried out, you can display the forecast values by selecting Goto ® Forecast values.
A pop-up window now appears where the date of the first day of the period is suggested.
If you choose to overwrite the forecast date, please note that the date you enter must lie within the current or the following period.
The forecast is carried out. The Forecast Values screen then appears and the following message appears:
Forecast calculation was carried out.
You now have the option of manually changing the forecast values calculated by the system by entering new values in the Corrected value column.
The system then uses these new values as a basis for the future planning run.
A pop-up window appears with the additional parameters.
If you manually change a forecast value, then you should check the actual consumption value that results for this period later and correct it, if necessary.
If you want to carry out the forecast for all the articles in one certain site or in all sites, then you must carry out a total forecast. A description of how to do this is given in the following paragraphs:
The initial Forecast screen appears for carrying out the total forecast.
Site
Enter the site for which the total forecast is to be started. If you do not enter a site here, the forecast is carried out for all sites.
Period indicator
This indicator specifies the time period for which the forecast values are managed in the system. The options given mean the following:
M - Monthly
W - Weekly
T - Daily
P - Period according to fiscal year variant
All those articles which have the same period indicator in the article master record as the indicator you enter here are included in the forecast.
Forecast execution
Indicator A means that the forecast will be carried out for the current period and indicator F means that the forecast will be carried out for the following period.
Save
Select this field if you want the forecast result to be stored in the system.
Log record
If you select this field, a list is displayed after the forecast run of all the articles for which forecast values were created. The list also contains the process and exception messages.
The corresponding texts for the entries now appear and you are requested to check your entries.
The following message will appear:
Total forecast to be started. Please press ENTER .
When the forecast run is complete, you receive a list of all the articles which were included in the forecast if you filled in the Log record field. If not, then no list will appear.
The total forecast must be carried out periodically. The time intervals available are; monthly, weekly, daily, or per accounting period. If you carry out the forecast on a daily basis, then, for articles that are planned using time-phased planning, the system only includes the articles whose turn it is according to the RP date.
If past consumption values are known at the end of a period, the system will use them as a basis for carrying out the forecast for the next period.
Subsequently, the forecast results can be printed out. The print program prints the forecast results which have automatically been sorted according to company code, site, RP controller and highest error number. For more information on this subject, see Overview.
The forecast calculates the forecast values by using various mathematical formulae which evaluate historical data. The accuracy of the forecast basically depends on the extent and quality of the data that is available.
You only enter historical values when you create a new article master record or when the system creates a new article master record via batch input. The system then generates these values using withdrawal postings.
The system recalculates the basic value and the trend value for each forecast by using the smoothing factors and the most recent consumption values. The smoothing factors are the weighting factors used for historical data, with the values from the most recent period playing a more important role in the forecast than the values from previous periods. The greater the smoothing factors you select in the article master, the faster the forecast values are adapted to the actual past consumption values.
If your system is set so that the forecast values are to be taken into account for a planning run, then a so-called planning file entry is automatically made by creating a forecast requirement independent of the forecast result. In turn, this planning file entry initiates a new planning run for the articles affected.
For articles planned by reorder point planning, however, a requirements planning run is only initiated if the new reorder level, recalculated during the forecast, differs from the old level.
If a warehouse that stores article has to be ready for delivery at all times, then there has to be a high level of safety stock in this warehouse, as forecasting error cannot always be ruled out. This would also entail a high level of warehousing costs. The level of safety stock depends on the service level you select, on the replenishment lead time and on the accuracy of the forecast.
In order to keep the safety stock and therefore the storage costs as low as possible, the RP controller usually specifies a service level for each article. From a mathematical point of view, the service level represents an organization's ability to avoid a shortfall occurring during replenishment lead time.
If you opt for a relatively high service level, then the safety stock level calculated by the system will also be high. If you opt for a low service level, then the safety stock level will also be low.
The replenishment lead time refers to the in-house production time for in-house production and the planned delivery time for external procurement. It represents the time between the initiation of a procurement procedure and transferring the article into the warehouse. The replenishment lead time is stored at site level in the article master record by the RP controller. Since the probability of a shortfall is higher in a longer period, the safety stock level must also be set higher for long replenishment lead times.
Finally, the safety stock level also depends on the accuracy of the forecast . If the forecast deviates greatly from the actual consumption values, then the safety stock level will also be unusually high.
In addition to the safety stock level, the system recalculates the reorder level for articles which are subject to automatic reorder level adjustment. The reorder level is the sum of the safety stock and the forecast requirement within the replenishment lead time or the overall lead time for articles produced in-house.
The system compares "static" availability during article withdrawal; that is current availability, without taking future requirements into consideration, with the reorder level. In other words, it compares the remaining stock with the reorder level. If remaining stock falls below the reorder level, then the system marks the article for the planning run by making an entry in the planning file. During the next planning run, the system automatically generates a purchase requisition or a planned order for the article.
You can find a list of the various formulae for calculating the safety stock level and the reorder level in Overview
The system carries out an ex-post forecast for the initial forecast if more past values are available than are required/are to be used for initialization. It also carries out the ex-post forecast during parameter optimization, model initialization and for evaluating the accuracy of the forecast.
The system only carries out the ex-post forecast for subsequent forecasts when there is a gap of more than one forecast period between the last forecast date and the current forecast date. For technical reasons, the series of forecast values must be recalculated for every period. Since this requires a lot of time and effort for articles which are forecast daily or weekly, you can now make up for periods which you have "omitted" from the forecast. This means that it is no longer necessary to carry out the forecast for each period as the missing values can be provided by a later forecast. This is then achieved with the help of the ex-post forecast.
In the course of time, the characteristics of a consumption series can change. In order to be able to react to this in time, the system calculates a so-called tracking signal during every forecast. The tracking signal links the error total (FS) and the mean absolute devialtion (MAD) in the following manner:
Tracking signal = │ FS/MAD │
The forecast error is the difference between actual consumption values and the forecast values from the same period whereas the error total is the sum of all the forecast errors in a consumption series.
The error total is used to check the validity of the forecast model in operation.
If a model is still valid, that is, if the consumption series has not changed, then you can assume that the error total is distributed normally and has an average of zero.
If the consumption pattern has changed, however, the error total will no longer be equal to zero. You must standardize the calculated forecast error in order to set boundaries. Therefore, in addition to the error total, the system also calculates the mean absolute deviation (MAD) as a second value. The system then adds the errors (irrespective of the plus or minus sign) and divides them by the number of consumption values. The first-order exponential smoothing procedure is used to calculate the MAD. The smoothing factor used for this is the delta factor.
Using the quotient from the error total and the MAD, the system can now define a warning limit, the tracking signal which it then compares with the tracking limit specified in the article master. When the tracking signal is greater than the tracking limit, the RP controller receives a message stating that the forecast model should be checked.
After a model change or a forecast model initialization, the error total is automatically reset to zero and the MAD to its initial value.
The system automatically sets the tracking limit (the default value is 4.00). You can change it, however, when maintaining the article master record.
The process of the ex-post forecast described above is used for parameter optimization in order to optimize the smoothing factors. In the article master, you can store whether parameter optimization is to take place during the forecast and if it is to take place, the degree of optimization you want to select.
When a forecast is carried out with parameter optimization, the system will start from an initial value and then gradually increase the increment of the smoothing factors in every simulation run. (This constitutes the first stage of the ex-post forecast.) The increment in this case is specified in the article master record by the optimization level. The system then further analyzes the situation for the parameter combination with the smallest MAD (second stage). The optimum parameter combination is the combination which has the lowest possible MAD.
In order to print out a list of materials from a certain plant which were included in a forecast run and which you want to use for checking the forecast results, follow the procedures described below:
The initial screen for Material Forecast Printing appears.
If you also want a printout of the process and exception messages which were created during the forecast, select the Log field.
The Print Parameters screen now appears.
For more detailed information on printing procedures, see Getting Started With the R/3 System.
After the system has created the list of all the material forecasts, the screen with the list of results appears and you can see exactly how many material forecasts the printed list contains.
Printing the list in background mode
If the forecast was carried out in background mode, it is also possible to print out the list in background mode. You do this as is described below:
The Background Request screen appears.
You can read more about background request procedures in ABAP/4: Generating and Printing Reports.
Errors or exceptional situations which appear during the total forecast are recorded in the exception messages and are allocated to a certain error class. If an error occurs in a material forecast, then the system marks the material for which the error occurs for reprocessing.
If you want to check the forecast result in online mode and reprocess the marked materials, proceed as follows:
The initial screen for Forecast Reprocessing appears.
If you want to limit the selection to a certain interval, you must enter the appropriate period indicator.
Furthermore, you can limit the selection of exception messages to one particular error class by selecting various error classes. The following Table shows which types of exception messages exist and to which error class they are allocated.
Description of error classes
Exception message | Error class |
Reorder level and safety stock | 1 |
Model selection and test | 2 |
Initialization | 3 |
Parameter optimization | 4 |
Difficulties during the forecast | 5 |
Forecast not calculated | 6 |
Missing table entries | 7 |
Terminations | 8 |
A list of materials now appears displaying those materials for which one or more forecast errors occurred and which correspond to your selection.
The Parameter Overview for the selected material now appears.
A screen with the corresponding exception message appears. You can now see what caused the error during the forecast.
Example:
The MRP controller did not enter any historical values when creating the material master record. You must now respond to this situation with manual initialization.
The system carries out the forecast and displays the forecast value with the message:
Forecast calculation was carried out.
The forecast values were saved.
By saving the new forecast result, the system then records that the material in question has been reprocessed and this material will automatically be deleted from the list of materials to be reprocessed.
