A matching method is a collection of matching rules and is used to match items between trading partners.
When creating a new matching method, select Stored as Master Data under the Attributes section to maintain the method in a production system.
If you select Stored as Configuration Data, the matching method is transported between systems. For example, you transport the method from the quality system to the production system, using a transport request. Any changes after the initial transport can then only be made in the source system where the matching method was originally defined.
Under the Data Sources section, select a data source that forms the matching context.
You can define filters for the data source to further refine the data set to be matched. In the following example, we are selecting accounts payable and accounts receivable values based on FS items. Because the 121100 and 211100 FS items include 3rd party as well as internal transactions, we are also selecting records where the Partner Unit is not empty.
You can also use other dimensions in the filter. For example:
- Use Document Type 0B to select non-SAP S/4HANA data loaded from the flat file to ACDOCU.
- Use Document Type 0F to select SAP S/4HANA accounting data from ACDOCA.
- Use Document Type 12 to select non-SAP S/4HANA data posted via group journal entry to ACDOCU.
In addition to document type, posting level and currency translation indicator are also used to filter the right transactions for reconciliation purposes when needed.
The Matching Rules are used to define the following components:
- Match Type
- Default Reason Code
- Data Slice
- Matching Expression
These are the available Match Types:
- Exact Match: Select this match type if data records assigned according to this rule do not require any follow-up activities.
- Auto-Assign: This match type results in a suggested assignment. Any data assigned based on such a rule will have the processing status Assigned. This means that you need to check the assigned data and complete any required follow-up activities. For example, trigger a workflow or an automatic adjustment posting, or confirm the item matching after communicating with others.
- Group as Matched: This match type groups the filtered data together and assigns the processing status Matched with no further actions needed. For example, you may want to apply this kind of matching rule to reversed journal entries that are not relevant for intercompany reconciliation.
- Group as Assigned: This match type groups the filtered data together and assigns the processing status Assigned. User action is required to achieve the final status Matched. For example, you may want to apply this kind of matching rule to journal entries that lack the trading partner information.
- Auto-Assign as Exception: Can be used for some common or known exceptional matches. For example, the documents have the same reference number and amount but different currency codes. Other than that, it has the same system behavior as Auto-Assign and helps filter assignments.Reason Code:
Select a Default Reason Code to control follow-up activities. For example, you may need to enter comments, go through an approval workflow, or make adjustment postings to achieve the final Matched status.
In a consolidation scenario, what is the use case for reason codes?
Transaction differences are posted against reason codes based on the Reference GC Amount in the Activate Reconciliation Close Process configuration.
In consolidation reports, reason code is used to make intercompany transaction variances more transparent.
Reason codes do not generate follow-up activities for consolidation.
Data slices represent user-defined data subsets of the data set defined on the matching method level. You can slice the data set into subsets by defining filters on the fields derived from the underlying data sources. Matching rules only process data included in the data slices.
Each matching rule partitions data into two data slices using filters:
- Leading Unit
- Partner Unit
In the subsequent matching run, the system first reads data based on the filters set for data slices and then applies the matching rule expressions to the filtered data. If the items from each slice agree with each other, they are grouped under a matching assignment number. The data not yet in agreement is subject to the next matching rule.
In the following example, FS items are selected based on the elimination attributes S-IUE-BS-AR and S-IUE-BS-AP.
In the Matching Expression subsection, define any necessary matching expressions for comparing values of both data slices. A matching expression is an equation that consists of left and right-hand matching fields and a comparison operator. Note that if any conversion function, is used for a data slice that has Aggregate selected, value conversion is executed after the aggregation.
In the following example, the Matching Expression applies to intercompany transactions posted to:
- The same invoice number (in field Assignment Ref).
- The same transactional currency (TC) key where the variance is less than 100 in the aggregated AR and AP FS items.
As a result of creating the matching method and running matching, all transactions are Assigned as you can see in the following Reconciliation Status Overview image.
In addition, in the Reconciliation Details, you can see that reason code Z98 is assigned to the difference (-5,395) for Germany and the United States.