Early Warning System: Basics

The Early Warning System is based on the key figures of the Logistics Information System (LIS) and can be used for all of the applications in Logistics.

You can find the Early Warning System in all of the LIS information system menus.

The Early Warning System allows you to make decision-oriented selections and to check weak areas in Logistics, by enabling you to search for exceptional situations. This means that you can detect and rectify potential problems at an early stage.

You can define both Exceptions and the conditions for follow-up processing.

An exception consists of specified characteristics or characteristic values (for example customer, material), and requirements. The following requirements can be defined:

For example, materials/customers whose sales are greater than 10,000 DM)

For example, materials/customers with a negative trend in sales or lead times)

For example, which customers have a plan realization for incoming orders of less than 80%)

In the planned/actual comparison, you specify the plan realization in percent and specify an operator (>, <, = etc.). This type of requirement helps you to check the plan realization and to pinpoint the weak areas between planned and actual data.

You can also carry out a forecast in the threshold value analysis and planned/actual comparison. The number of periods to be forecasted are determined by you. The forecast is carried out based on historical data.

When using the threshold value analysis you can determine, with the help of the forecast, whether or not a preset threshold value is also possible in the future based on the present actual data thereby enabling you to react in time to undesirable situations.

With the help of the forecast in the planned/actual comparison you can compare planned figures with forecasted data, which result due to past developments. This allows you then to react quickly to threatening situations in the future.

Another advantage to checking for trends is that developments can be recognized early, leaving enough time for a reaction.

An exception can have a variety of forms, for example, you can store requirements for various characteristic values and different key figures or you can combine a trend and threshold value analysis.

By combining exceptions into exception groups you can make a check on complex data.

There are three possible ways to check for exceptional situations:

Displaying Exceptional Situations in the Standard Analyses

You can specify an exception when carrying out a standard analysis. In standard analyses the exceptional situations are highlighted using color codes. Differentiation is possible in that different colors can denote different conditions. For example, you can choose red for an inventory turnover less than three, yellow for an inventory turnover greater than two and green for an inventory turnover greater than five.

These color codes allow you to navigate selectively within the standard analyses. If exceptions occur, on the material level, for example, this will be displayed on a higher aggregation level (for example, on the purchasing organization or customer level). With the help of the drill-down function, you can then go to the line with the corresponding color code for the exceptional situation.

Exception Analysis

You can carry out what is known as an exception analysis. The difference between this and the display of exceptions in standard analyses is that only data to which the exception applies is displayed here. The exception analysis is like a filter; only exceptional situations are displayed. Exceptional situations here are also highlighted in different colors.

Periodic Analysis

Periodic analyses release you from the burden of searching for exceptional situations. The existing dataset is searched systematically for exceptional situations,. The frequency of the search is freely definable (hourly, daily, weekly, monthly). If an exceptional situation arises, you can be informed by mail or via workflow and initialize follow-up processing.

A periodic analysis can be carried out in two ways:

The search for exceptional situations is initialized when a change in data occurs due to a logistics event (for example, sales order, purchase order, stock movement).

The dataset chosen is checked for exceptional situations.

We recommend that you monitor particularly important key figures or characteristics using event-driven periodic analysis. For all other cases, a weekly check, for example, can be carried out with the system-driven periodic analysis.

The following representation shows you an overview of the capabilities of the Early Warning System.