Management have asked you to provide an overview of the reporting capabilities of LIS. You will need to study the basic functionality.
Reporting Functionality in LIS
• The various functions supported are – Standard Analysis in LIS – Early Warning System – Flexible Analysis in LIS – LIS Interface
Reporting using Standard Analysis
• Data Selection
– Single Value selection – Multiple Selection
– Selection using self defined heirarchies – Selection Options
– Variants
Figure 14: Navigation Options in Standard Analysis
After executing a standard analysis, an initial list is displayed on the screen.
Starting from this initial standard analysis list, the system offers three ways for navigation:
• Standard drilldown: By double-clicking on a characteristic you will access the next level of a predefined standard drilldown. In order to define this drilldown, you can use all characteristics and the period unit of the relevant info structure.
• Drill down by: You can drill down each characteristic according to a different characteristic. Starting from any list in a standard analysis, you can drill down any characteristic value in this list according to a different characteristic.
• Switch drilldown: The whole drilldown can by displayed for another characteristic. From any list in a standard analysis, the total values of all key figures can be drilled down by any possible characteristic of the standard analysis.
Figure 15: Functions in Standard Analysis
A wide range of functions can be used to individually examine the key figures and characteristic values on which the analysis is based from a business perspective.
All the functions for statistical analyses are graphically supported.
• ABC Analysis: The values of a characteristic (e.g. vendors) and a particular key figure (e.g. order value) are compared in order to make a classification in three segments. Various strategies can be used to set the class limits as characteristic or key figure-specific and as percentage or absolute values, respectively. The results are displayed in a cumulative frequency curve with an additional classification into three segments. The sizes of the segments correspond to the setting made when the strategy was selected.
• Classification: Classification provides you with an overview of the characteristic values for a key figure. You can define up to six classes here.
You can also organize the class limits to suit your requirements. Results can be displayed as both lists and presentation graphics. The sequence is preset.
• Dual Classification: You can classify the characteristic values of two key figures. The navigation and presentation options are identical to those in classification.
Figure 16: Functions in Standard Analysis (Contd)
Plan/actual comparison: At each drilldown level, there are three possibilities to carry out comparisons:
• The current data of a key figure can be compared to the data of a plan version.
• The values of the previous year can be compared to the current data.
• The values of any two key figures can be compared to each other.
Cumulative frequency curve: It graphically illustrates the distribution of a cumulated key figure value over the existing characteristic values. It can be scaled to represent either percentage or absolute values according to the selection made in the list upon which the curve is based.
Correlation: Correlation curves depict interrelationships between two or more key figures. When creating the correlation diagram, the system observes the sort sequence defined in the underlying list. The key figures in the correlation are always standardized to 1.
Time series: From the drilldown list of a characteristic, you can create a time series for any key figure. The period corresponds to the predefined period you determined when entering the standard analysis.
Figure 17: Early Warning System: An Overview
The Early Warning System enables you to search for exceptional situations and helps to detect and eliminate imminent problems at an early stage. This is done by the following process:
• The LIS provides the data that is analyzed by the EWS. Hence, the EWS can be used in any Logistics Information System.
• The Early Warning System is based on information structures. Information updated in these structures can be analyzed using the EWS. This also applies to data that is updated using your own programs, e.g. from an external system.
• The EWS can be used both to indicate defined alarm situations and to highlight specific data in an analysis.
Figure 18: Application of EWS
The Early Warning System is either used interactively in the standard analyses or run at regular intervals as a background job.
• If you use it interactively in the standard analyses, the exceptional situations are highlighted using color codes or filtered in the exception analysis. This allows you to detect exceptional situations at an early stage.
• In the periodic analysis, a list of the exceptional data is automatically sent to the designated recipient by fax, mail, or workflow.
Figure 19: Defining an Exception
To create an exception, the following steps have to be followed:
• Since an exception is always created with reference to an information structure, the characteristics are selected from this info structure. The sequence of characteristics defines the subsequent standard drilldown and the level at which the requirement is checked.
• The key figures are also selected from the info structure. Then the exception requirements for the selected key figures can be defined.
• In a third step, the follow-up processing of the exception is defined.
The characteristic in an information structure is used to define the characteristics of an exception.
• When you select the characteristics, you also define the aggregation level at which the check for the exception will take place. The key figure check always takes place at the lowest characteristic level.
• The sequence of the selected characteristics serves to define the standard drilldown sequence in a standard analysis, which is triggered by the exception.
Those key figures of an info structure that are required for the definition of an exception are chosen.
• Several requirements can be defined both for several key figures and for each individual key figure.
There are three types of requirements:
• Threshold values: identifies the key figure values that exceed or fall below a specific threshold value, e.g. incoming orders value > 200,000.
• Trend: identifies the key figure values that demonstrate a predefined trend, e.g. a negative trend.
• Planned/actual comparison: identifies the key figure values for which the actual data deviates from the planned data by more than a predefined percentage, e.g. 10%.
These individual requirements you defined can be linked by means of And or Or.
In the follow-up processing, you define whether the exception is active for the standard analysis and/or a periodic analysis.
• The color used to highlight the exceptional values determined via the requirements in the standard analysis.
• In addition, definition of how the result of a periodic analysis is to be further
Figure 20: Requirements
For Threshold Value Analysis, you enter a threshold value for a key figure and an operator for the threshold value (e.g. incoming orders value > 200,000).
• The threshold value in the example above is defined so that the exception is satisfied when the total of the incoming orders values of the last three months, including the current month, exceeds 200,000. The number of periods defines the number of periods to be analyzed. If the option Separate periods is chosen, each individual period is checked instead of the key figure value total of these three periods.
• Any other currencies are converted into the specified analysis currency, before the check for exceptions takes place.
• You may also run a threshold value test to analyze future developments.
You need to choose the number of forecast periods that you wish to test. A forecast then takes place for the next few periods (2 in this case) which is based on the number of periods you have selected (7 in this case). A threshold value analysis is then carried out for the forecasted values.
The trend analysis determines whether there is a positive or negative trend in the dataset with regard to the selected key figure.
• Based on the dataset and the number of periods to analyze (6 in this case), the system checks whether or not a trend exists. It is not possible to determine a trend if only one period to analyze is selected. If a positive trend exists and no statistical test has been performed, an exceptional situation will occur if each value exceeds the value in the previous period.
• If the period to analyze extends over 3 or more periods, you can also carry out a statistical trend test for each individual requirement by means of statistical resources. This is recommended if you want to know whether a general trend exists, even if the dataset contains outliers. If there is a positive trend and a statistical test has been performed, an exception will occur when the system detects a trend with a probability of 95%.
• If there are 3 or 4 past periods available to carry out a statistical test for a trend, a regression line is drawn upon which the trend is based. If there are 5 or more past periods, a reliable statistical test for a trend can be performed. If you select only two past periods, the system only determines whether the second period value is greater or less than the first value.
The planned/actual comparison compares planned data of a specific planning version with actual data. You can check the realization of the plan and determine the weak points with respect to planned/actual values. To do this, you enter the plan realization percentage and an operator.
• In the first example, the planned values for a key figure are compared with the actual data. An exceptional situation occurs when the realization of the plan is less than 90%, i.e. when the actual data falls short of the planned values by more than 10%.
Note: The exception is based on the total number of specified periods because the flag “Separate periods” is not set.
• In the second example, the system creates a forecast for the next two periods, based on the values of the last six periods. The total of both forecasted values is compared with the total planned values assigned to these periods.
• This kind of analysis enables you to detect potential problems which are in the future.
Figure 21: Flexible Analysis in LIS
The flexible analyses in LIS are used in the same way as a report generator: You use a menu to describe the content and format of the list you require and, at the touch of a button, the respective program is generated in the background.
In comparison to standard analyses, flexible analyses have the following advantages:
• This technique enables you to combine characteristics and key figures from different information structures or DDIC tables in one list.
• You can choose between a variety of layouts.
• You can use your own formulas to calculate new key figures for existing ones.
You can use the Evaluation function to describe the reporting function in LIS. The
“Evaluation” concept contains a program object that controls the collection and formatting of data for evaluation purposes.
In LIS, the evaluation structures control the way in which the evaluations collect data. They describe the possible data sources of your evaluations. These data sources are usually information structures.
• An evaluation structure mainly consists of a list of characteristics and key figures. An evaluation structure can also contain characteristics and key figures from different physical database tables.
• The name of an evaluation structure must begin with “ZF” (example:
ZFMARA).
• Evaluations are created with reference to evaluation structures. The characteristics and key figures in an evaluation structure can form the rows and columns of your evaluation list.
• When evaluation structures and evaluations are generated, Report Writer objects are created in the background. They can also include libraries for evaluation structures and reports for evaluations.
• When you run an evaluation, a list of data is displayed at the data presentation level, which you can change and interpret using a variety of functions.
Figure 22: LIS Interface with Excel: Transfer Process
You can transfer data to Excel using all the reporting functions already discussed within the Logistics Information System. Standard analyses, early warning systems and flexible analyses provide easy-to-use tools for transferring data.
• One method is to save the current content of a list locally as an ASCII file using the Save to PC file function with the spreadsheet option. You can then open this file in Excel.
• The other option is implemented through the function Transfer to XXL.
Data is transferred to the XXL interface in the form of a list object. XXL (Extended Excel) comprises the tools for displaying and manipulating list objects from SAP R/3 applications. You first have to define the characteristics to be transferred. The figures transferred are the key figures in the current standard analysis list. Depending on the software installed on your computer, you can use XXL to transfer data to the following media:
– SAPoffice: You can save the list object to the SAPoffice inbox. From there you can send the list object or save it in a folder.
– PC file: You can save the list object to the local file system for subsequent editing using another display media at your disposal.
– Excel display: Table display with all characteristics combinations.
– Excel SAP macros: You can display and edit the list using SAP Macros (SAP-XXL List Viewer).
– Excel Pivot table: You can display and edit the list using Excel.