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Your Credit Management & Collections Data Template

SAP S/4HANA

Credit Management & Collections Attributes (19) Credit Management & Collections Activities (12) Extraction Guides (4)

Your Credit Management & Collections Data Template

This template provides a clear guide for gathering the necessary data to analyze and optimize your Credit Management & Collections process. It outlines key attributes to collect, essential activities to track, and practical extraction guidance from your SAP S/4HANA system. Use this resource to ensure you capture all critical information for a robust process analysis.

Recommended attributes to collect for your event log
Key process activities to track across the invoice lifecycle
Specific extraction guidance for SAP S/4HANA

New to event logs? Learn how to create a process mining event log.

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Credit Management & Collections Attributes

These are the recommended data fields to include in your event log for a comprehensive analysis of your credit management and collections process.

3 Required 5 Recommended 11 Optional

	Name	Description
	Activity Name ActivityName	The name of the specific business event that occurred in the credit management and collections process.
Description This attribute describes a single step or task within the process, such as 'Invoice Generated', 'Dunning Procedure Initiated', or 'Payment Received'. The sequence of these activities forms the process flow for each case. Analyzing the Activity Name is fundamental to process mining, as it allows for the discovery of the process map, identification of variants, and measurement of transitions between different steps. It is the basis for nearly all process analysis, including KPI calculations like 'Dunning Success Rate' which tracks events following a dunning activity. Why it matters It defines the steps of the process, which is essential for visualizing the process map, analyzing variants, and understanding the process flow. Where to get Generated from a combination of transaction codes (TCODE), table change logs (CDHDR/CDPOS), or status fields within SAP documents. Examples Invoice Sent To CustomerDunning Procedure InitiatedPayment ReceivedDispute Case Registered
	Event Time EventTime	The timestamp indicating when the activity occurred.
Description This attribute provides the exact date and time for each recorded activity. It is critical for determining the sequence of events and calculating the duration between them. Event Time is used to order activities chronologically, build the process map, and compute performance metrics. For example, it is used to calculate 'Average Dispute Resolution Time' by measuring the duration between the 'Dispute Registered' timestamp and the 'Dispute Resolved' timestamp. Why it matters It provides the chronological order of events, which is necessary for calculating cycle times, analyzing process performance, and discovering bottlenecks. Where to get Sourced from various date and time fields in SAP tables, such as BUDAT (Posting Date) in BKPF, or CPUDT/CPUTM (Date/Time of Entry) in various tables. Change log tables CDHDR/CDPOS also contain timestamps. Examples 2023-04-15T10:00:00Z2023-05-01T14:30:00Z2023-05-10T09:15:00Z
	Invoice Number InvoiceNumber	The unique identifier for the customer invoice, which serves as the primary case ID for the credit management and collections process.
Description The Invoice Number, often represented as the Accounting Document Number, uniquely identifies each receivable transaction. It acts as the central thread connecting all process events, from invoice creation and dunning notices to dispute management and final payment or write-off. In process mining, analyzing the journey of each Invoice Number allows for a complete view of the end-to-end process. This helps identify common pathways, bottlenecks in payment processing, and variations in handling different invoices, which is crucial for dashboards like 'Invoice Process Variants Analysis' and 'Overdue Invoice Aging & Status'. Why it matters It is the essential case identifier that links all related credit and collection activities, enabling a full lifecycle analysis for each receivable. Where to get Typically found in tables like BKPF (Accounting Document Header) as field BELNR, or VBRK (Billing Document: Header Data) as field VBELN. Examples 190000012319000004561900000789
	Customer Number CustomerNumber	The unique identifier for the customer account.
Description The Customer Number is the identifier for the business partner to whom the invoice was issued. It links the financial transaction to the customer's master data record. This attribute is crucial for customer-centric analysis, such as identifying payment behaviors of specific customers or segments. It is used in dashboards like 'Customer Payment Behavior Trends' to track which customers consistently pay late and supports KPIs like 'High-Priority Account Coverage' by grouping invoices by customer. Why it matters It links transactions to specific customers, enabling analysis of payment behavior, segmentation, and relationship management. Where to get Found in accounting document line items in tables like BSEG (field KUNNR) or in the customer master data table KNA1. Examples CUST100234CUST200567CUST300890
	Days Overdue DaysOverdue	The number of days an invoice is past its payment due date.
Description This is a calculated metric that measures the time elapsed between the 'Payment Due Date' and the current date (for open invoices) or the 'Payment Date' (for cleared invoices). A positive value indicates a late payment. Days Overdue is a cornerstone KPI for collections. It is the primary metric in the 'Overdue Invoice Aging & Status' dashboard and is used to prioritize collection efforts. Analyzing the distribution of this metric helps to understand the overall health of the accounts receivable portfolio. Why it matters It is a critical performance indicator for collection effectiveness, used for aging analysis, prioritization of work, and measuring payment delays. Where to get Calculated during data transformation: (Current Date OR Clearing Date) - PaymentDueDate (BSEG-ZFBDT). Examples 1530920
	Invoice Amount InvoiceAmount	The total value of the invoice in the document currency.
Description This attribute represents the total monetary value of the invoiced goods or services. It is a critical measure of the financial impact of each case. Analyzing the Invoice Amount is fundamental for prioritizing collection efforts, as seen in the 'Collection Portfolio Prioritization' dashboard. It is also used to assess the financial impact of process inefficiencies, such as the total value of disputed invoices or the amount of funds tied up in overdue receivables. It directly supports KPIs like 'Invoice Write-Off Percentage' by providing the value basis. Why it matters It quantifies the financial value of each case, which is essential for prioritization, risk assessment, and performance measurement. Where to get Derived from accounting document line items in table BSEG (field DMBTR for local currency amount or WRBTR for document currency amount). Examples 1500.0025000.50750.75
	Payment Due Date PaymentDueDate	The date by which the invoice payment is due.
Description The Payment Due Date is calculated based on the invoice date and the payment terms agreed upon with the customer. It is the baseline against which timeliness of payment is measured. This date is fundamental for all overdue analysis. It is the starting point for calculating 'Days Overdue' and for triggering dunning procedures. Dashboards like 'Overdue Invoice Aging & Status' and KPIs such as 'Average Days Sales Outstanding (DSO)' rely heavily on this attribute. Why it matters It is the key date for determining if an invoice is overdue, triggering collections activities and calculating critical KPIs. Where to get Found in the customer line item of an accounting document, table BSEG (field ZFBDT). Examples 2023-05-302023-06-152023-07-01
	User Name UserName	The user ID of the person who executed the activity.
Description This attribute identifies the specific user responsible for a given process step, such as posting a payment or initiating a dunning run. In SAP, this is often stored as the 'User Name' (UNAME) or 'Entered by' field. Analyzing by user helps identify high-performing individuals or teams, areas where additional training may be needed, and patterns of workload distribution. It can also be used to investigate compliance issues or unauthorized activities. For example, it supports the 'Collection Call Effectiveness' dashboard by tracking outcomes per collector. Why it matters It links process activities to specific individuals, enabling analysis of resource performance, workload, and compliance. Where to get Commonly found in header tables like BKPF (field USNAM) or change log tables like CDHDR (field USERNAME). Examples JSMITHRROEBATCH_USER
	Company Code CompanyCode	The organizational unit representing a legally independent company for which financial statements are created.
Description The Company Code is a fundamental organizational entity in SAP Financials. All financial transactions, including invoices and payments, are posted to a specific company code. In process mining, filtering or dimensioning by Company Code is essential for comparing process performance across different legal entities within a corporation. This helps identify best practices in one entity that can be applied to others, or pinpoint systemic issues affecting a particular company. It is a key filter for nearly all financial process analyses. Why it matters It allows for the comparison of process performance and compliance across different legal entities within the organization. Where to get Found in most financial tables, such as BKPF and BSEG (field BUKRS). Examples 10002000US01DE01
	Credit Limit CreditLimit	The maximum amount of credit extended to a customer.
Description The Credit Limit is a value stored in the customer's credit master data, defining the total credit exposure the company is willing to have with that customer. Activities like 'Credit Limit Requested' and 'Credit Limit Approved' directly relate to this attribute. This attribute provides context for credit risk analysis. The 'Credit Approval Cycle Time Analysis' dashboard tracks the process of setting or changing this limit. Analyzing it in conjunction with overdue amounts helps to assess overall portfolio risk. Why it matters It defines the approved credit risk for a customer and is central to analyzing the credit approval process and overall credit exposure. Where to get Stored in the SAP Credit Management (FSCM) module, in tables such as UKM_BP_CMS_SGMT (field CREDIT_LIMIT). Examples 50000.00100000.00250000.00
	Customer Segment CustomerSegment	A classification of the customer, for example based on size, industry, or strategic importance.
Description Customer Segment is a marketing or sales classification used to group customers with similar characteristics. This could be based on factors like annual revenue, industry, geographical location, or relationship status (e.g., Gold, Silver, Bronze). In collections analysis, segmenting by this attribute helps uncover patterns specific to certain groups. For instance, the 'Invoice Write-Off Analysis' dashboard can reveal if a particular segment is responsible for a disproportionate amount of write-offs, guiding strategic decisions on credit policies for that segment. Why it matters It enables the analysis of process performance and customer behavior across different customer groups, revealing valuable strategic insights. Where to get Typically sourced from the Customer Master data (table KNA1), often in a classification or attribute field like KDKG1-KDKG5 (Customer group 1-5). Examples Large EnterpriseSmall & Medium BusinessGovernmentStrategic Account
	Dispute Reason DisputeReason	The reason code provided for why a customer is disputing an invoice.
Description When a customer disputes an invoice, a reason is typically assigned to categorize the nature of the issue, such as 'Incorrect Pricing', 'Damaged Goods', or 'Duplicate Invoice'. This is managed in the SAP Dispute Management module. Analyzing Dispute Reasons is key to identifying root causes of customer dissatisfaction and payment delays. The 'Invoice Dispute Volume & Resolution' dashboard can be dimensioned by this attribute to pinpoint recurring problems in the upstream order-to-cash process that need to be fixed. Why it matters It provides critical insight into the root causes of invoice disputes, helping to identify and resolve underlying operational issues. Where to get Sourced from the SAP Dispute Management module, likely from tables like UDM_SCASE_ATTR or similar, based on the dispute case attributes. Examples PRC_ERR - Pricing ErrorQTY_DIF - Quantity DifferenceSHIP_DMG - Damaged Shipment
	Dunning Level DunningLevel	Indicates the current stage of the dunning process for an overdue invoice.
Description The Dunning Level represents the intensity of the collection effort, typically escalating from a simple reminder to more formal legal notices. Each level corresponds to a specific dunning activity defined in the dunning procedure. Tracking this attribute is essential for monitoring the effectiveness and compliance of the dunning process. The 'Dunning Process Effectiveness' dashboard uses this to analyze payment rates at different levels, while the 'Dunning Process Compliance' dashboard checks if the sequence of levels is followed correctly. Why it matters It tracks the progression of collection efforts, allowing for analysis of dunning effectiveness and process compliance. Where to get This information is stored in dunning data tables like MHND (Dunning data). The last dunning level for an item can be found in BSEG (field MANST). Examples 123 - Final NoticeLegal Action
	Invoice Currency InvoiceCurrency	The currency in which the invoice was issued.
Description This attribute specifies the currency code (e.g., USD, EUR) for the invoice amount. It provides the necessary context for interpreting monetary values, especially in multinational organizations. While most analysis is done in a standardized local currency, the document currency is important for understanding the original transaction and for any analysis related to foreign exchange effects. It provides context to the InvoiceAmount attribute. Why it matters It provides essential context for the Invoice Amount, enabling accurate financial analysis in multi-currency environments. Where to get Typically found in header tables like BKPF (field WAERS) or line item tables like BSEG (field PSWSL). Examples USDEURGBP
	Is Automated IsAutomated	A flag indicating whether the activity was performed by a system user or a human user.
Description This boolean attribute distinguishes between activities performed automatically by the system (e.g., a scheduled dunning run, an automatic payment posting) and those performed manually by a user (e.g., a collection call). Analyzing this attribute helps to measure the level of automation in the process. It allows for comparing the efficiency and consistency of automated versus manual steps, identifying opportunities for further automation, and understanding the true manual effort involved in the collections process. Why it matters It helps quantify the level of automation in the process, enabling analysis of the impact of automation on efficiency and cost. Where to get This is a derived attribute, typically based on the 'UserName'. If the user ID matches a predefined list of system or batch users (e.g., 'BATCH_USER'), the flag is set to true. Examples truefalse
	Last Data Update LastDataUpdate	The timestamp of the most recent data refresh or extraction from the source system.
Description This attribute indicates when the dataset was last updated. It provides users with context on the freshness of the data they are analyzing. This is a critical piece of metadata for any dashboard or analysis. It informs the user about the time period covered by the analysis and prevents misinterpretations based on outdated information. For example, an analysis of overdue invoices is only meaningful if the user knows when the data was last pulled. Why it matters It ensures users understand the timeliness of the data, which is critical for making accurate, data-driven decisions. Where to get This value is generated and stored during the data extraction, transformation, and loading (ETL) process. Examples 2023-10-27T02:00:00Z2023-10-26T02:00:00Z
	Processing Time ProcessingTime	The duration of a specific activity.
Description This attribute measures the time it takes to complete an activity, calculated as the difference between the activity's end time and start time. This is most relevant for activities that are not instantaneous. For many financial transactions in SAP, the start and end times are the same. However, for processes like dispute resolution, this metric can measure the time a case spends in a particular status. It directly supports KPIs like 'Payment Posting Cycle Time' by measuring the duration of the posting activity. Why it matters It helps identify time-consuming activities that are potential bottlenecks in the process. Where to get Calculated from event data as (EndTime - StartTime). Requires both a start and end timestamp for each activity. Examples P1DPT2H30MP3DT5H
	Source System SourceSystem	Identifies the system from which the data originated.
Description This attribute specifies the source information system, which in this case is SAP S/4HANA. It is particularly important in environments where data might be consolidated from multiple systems, such as different ERP instances or a separate CRM. In process analysis, this helps differentiate processes that may span multiple systems or allows for filtering the analysis to a specific system instance. It ensures data lineage and provides context, especially during data validation and troubleshooting. Why it matters It provides crucial context about the data's origin, ensuring clarity in multi-system environments and aiding in data governance. Where to get This is typically a static value added during the data extraction process, identifying the specific SAP S/4HANA instance (e.g., by System ID, or SID). Examples S4H_PROD_100S4HANA_FINANCE_EUSAP_ECC_US
	Write-Off Reason WriteOffReason	The reason code explaining why an invoice was written off as uncollectible.
Description When a receivable is deemed uncollectible, it is written off the books, and a reason code is assigned to classify the cause, such as 'Bankruptcy', 'Small Balance Write-Off', or 'Unresolved Dispute'. This attribute is essential for the 'Invoice Write-Off Analysis' dashboard. By analyzing the frequency and value of write-offs by reason, a company can identify weaknesses in its credit policy or collection strategy and take corrective action to minimize future losses. Why it matters It explains the root causes of financial losses from uncollectible debt, informing improvements to credit and collection policies. Where to get This is often captured via reason code fields (BSEG-RSTGR) during the specific financial posting transaction (e.g., F-30) used for writing off receivables. Examples BANKRUPTCYSMALL_BALANCEDISPUTE_LOSS

Required Recommended Optional

Credit Management & Collections Activities

These are the key process steps and milestones to capture in your event log for accurate discovery and optimization of your collections workflow.

6 Recommended 6 Optional

	Activity	Description
	Dispute Case Registered	This activity signifies that a customer has formally disputed an invoice, and a case has been created in SAP Dispute Management. This is captured when a new dispute case is created and linked to the invoice document.
Why it matters Registering disputes is a critical step for understanding process exceptions. Analyzing the volume, reasons, and resolution time of disputes helps identify root causes like pricing or shipping errors. Where to get This is an explicit event recorded in the SAP Dispute Management tables. The creation date of the case in tables like UKM_CASE or FDM_DCOBJ linked to the invoice serves as the timestamp. Capture Use the creation timestamp of the dispute case from the UKM_CASE table, filtering by the case type for disputes. Event type explicit
	Invoice Generated	This activity marks the creation of a customer invoice, which is the starting point for the collections process. This event is captured when a new accounting document with an invoice document type is posted in the SAP S/4HANA system.
Why it matters This is the primary start event for the invoice-to-cash process. Analyzing the time from this point to payment is crucial for measuring Days Sales Outstanding (DSO) and overall process efficiency. Where to get This event is explicitly recorded. The creation date and time can be found in the accounting document header table, BKPF, for the relevant document number (BELNR). Document types like 'RV' typically denote customer invoices. Capture Use the document creation timestamp (CPUDT, CPUTM) from the BKPF table for the invoice document. Event type explicit
	Invoice Settled	This is the final, successful outcome for an invoice, indicating it has been fully paid and cleared from open items. This state is recognized when an invoice has a corresponding clearing document and date.
Why it matters As the primary success endpoint of the process, this activity concludes the invoice lifecycle. Analyzing the paths and time taken to reach this state is fundamental to process optimization. Where to get This is not a discrete event but a state inferred from financial tables. An invoice is considered settled when it appears in the cleared customer items table, BSAD, and has a valid clearing date (AUGDT). Capture Use the clearing date (AUGDT) from the BSAD table as the timestamp for this concluding activity. Event type inferred
	Invoice Written Off	This activity represents the decision to absorb an unpaid invoice as a loss, classifying it as bad debt. This is captured when a specific type of clearing document is posted, often with a unique reason code.
Why it matters Write-offs represent a direct financial loss. Analyzing their frequency, value, and preceding activities helps identify at-risk customers and failures in the collections process. Where to get This can be inferred by analyzing the clearing document that settles the invoice. A specific general ledger account or reason code (BSEG-RSTGR) used in the clearing document indicates a write-off. Capture Identify the clearing document and check for a specific reason code or posting to a bad debt account. Event type inferred
	Payment Due Date Passed	A calculated event that occurs when the current date surpasses the invoice's net due date without a clearing payment being posted. It signifies the transition of an invoice from 'current' to 'overdue' status.
Why it matters This activity is the trigger for all collections and dunning activities. It is essential for analyzing payment behavior, overdue reasons, and the effectiveness of proactive reminders. Where to get This is not an explicit log. It is calculated by comparing the net due date (from tables like BSID field ZFBDT) with the timestamp of the analysis or a subsequent activity. Capture Derive by comparing the invoice net due date (BSID-ZFBDT) to the current timestamp or a subsequent event's timestamp. Event type calculated
	Payment Received	This activity represents the receipt of funds from the customer, which is then applied to the open invoice. It is captured by the creation of a clearing document that settles the invoice amount.
Why it matters This is a critical milestone that directly impacts cash flow and DSO. Analyzing the time to payment and the activities that precede it reveals the effectiveness of the entire collections process. Where to get This is inferred from the clearing document posted against the invoice. The clearing date (AUGDT) from the cleared items table, BSAD, for the specific invoice provides the timestamp. Capture Identify the clearing date (AUGDT) from the BSAD table for the corresponding invoice document number (BELNR). Event type inferred
	Collection Call Made	Represents a manual contact made by a collections agent to the customer regarding an overdue invoice. This is often not a standard SAP event and relies on custom logging or integration with a CRM system.
Why it matters Manual interventions are a costly part of the collections process. Tracking these calls helps measure collector effectiveness and understand which accounts require manual effort. Where to get This information is typically not available in a standard S/4HANA setup. It might be stored in custom tables, as a note in the collections worklist, or in an external CRM system that needs to be integrated. Capture Requires analysis of system enhancements, notes, or data from an external system where collection activities are logged. Event type explicit
	Dispute Resolved	This activity marks the closure of a dispute case, either because the customer's claim was validated or rejected. This is captured when the status of the dispute case is updated to a 'closed' or 'resolved' state.
Why it matters The time taken to resolve disputes is a key performance indicator for customer satisfaction and operational efficiency. Long resolution times can delay payments and strain customer relationships. Where to get This is inferred from a status change on the dispute case. A timestamp associated with the status change to 'Closed' or 'Confirmed' in the UKM_CASE table indicates when the resolution occurred. Capture Identify the timestamp of the status change to a final, resolved status within the UKM_CASE table or related status change log tables. Event type inferred
	Dunning Procedure Initiated	This activity represents the formal initiation of the automated dunning process for an overdue invoice. It is captured when a dunning run processes the invoice and assigns a dunning level.
Why it matters This marks the start of automated collections. Tracking dunning activities is key to assessing the effectiveness of the dunning strategy and ensuring compliance with internal policies. Where to get This is an explicit event logged in the dunning data tables. The run date (LAUFD) from the dunning header table, MHNK, associated with the invoice indicates when the procedure was executed. Capture Extract the dunning run date (MHNK-LAUFD) and dunning level (MHNK-MAHNS) for the specific invoice. Event type explicit
	Invoice Sent To Customer	Represents the moment the invoice was transmitted to the customer, for example, via print, email, or EDI. This is typically captured through SAP's output determination logs which record when an output message was processed.
Why it matters Delays between invoice generation and sending can extend the entire payment cycle. Tracking this helps identify bottlenecks in customer communication and document distribution. Where to get This can be inferred from the processing date and time in the message status table, NAST, where the output type corresponds to a customer invoice. This requires proper configuration of output determination. Capture Look for a successfully processed record in the NAST table linked to the invoice's billing document number. Event type inferred
	Payment Posted	Represents the accounting entry of the payment being recorded in the general ledger. In many cases, this happens concurrently with payment receipt but can be a separate step with its own delays.
Why it matters Delays between receiving cash and posting it can distort financial reporting. Measuring this cycle time helps ensure accounting processes are efficient and accurate. Where to get This event can be inferred from the posting date (BUDAT) or creation date (CPUDT) of the clearing document found in the BKPF table. The clearing document number is found in BSAD-AUGBL. Capture Use the posting date (BUDAT) from the BKPF table for the clearing document that settled the invoice. Event type inferred
	Promise To Pay Created	This event occurs when a collections agent records a customer's promise to pay an overdue item on a specific date. This is an explicit action within the SAP Collections Management module.
Why it matters Promises to pay are a key outcome of collection activities. Analyzing their frequency, fulfillment rate, and impact on payment times helps to refine collection strategies. Where to get This is an explicit event recorded in SAP Collections Management. The creation date of the promise to pay can be sourced from tables like UDM_P2P_ATTR. Capture Use the creation date from promise to pay tables, such as UDM_P2P_ATTR, linked to the invoice. Event type explicit

Recommended Optional

Extraction Guides

How to get your data from SAP S/4HANA

Steps

Verify CDS View Accessibility: Log in to your SAP S/4HANA system and use the 'View Browser' Fiori app. Search for key CDS views mentioned in the query, such as I_OperationalAcctgDocItemCube and I_DisputeCase, to ensure they are active and contain data. This confirms the necessary components are available.
Expose OData Services: Use transaction /IWFND/MAINT_SERVICE to verify that the relevant CDS views are published as OData services. If they are not, you will need to publish them to make them accessible to external tools.
Establish Client Connectivity: Use your data extraction tool (such as Microsoft Power BI, SAP Analytics Cloud, Python, or others) to create a new connection to your SAP S/4HANA system's OData endpoint.
Authenticate: Provide the connection details and authenticate with a user ID that has sufficient authorizations to read financial accounting, collections management, and dispute management data.
Prepare the Query: Copy the complete SQL query provided in the 'query' section of this document.
Customize Parameters: In the query text, replace all placeholder values with ones that are specific to your system and analysis scope. This includes [Your Company Code], [Start Date], [End Date], [Your Invoice Doc Type], [Your Write-off Reason Code], and [Your Dispute Case Closed Status].
Adapt for Custom Logging: The 'Collection Call Made' activity relies on a placeholder view [Your Custom Contact Log View]. You must replace this with the actual table or view where your organization logs collection agent contacts. If this data is not available, remove this entire UNION ALL block from the query.
Execute the Query: Run the customized query from your connected client tool. Depending on the tool, you may need to adapt the SQL syntax to its specific OData query language, for instance, by converting WHERE clauses to $filter parameters.
Review and Validate: Once the query finishes, perform a preliminary review of the returned data. Check for a reasonable number of rows and ensure that various activities are present.
Export to CSV: Export the complete result set to a CSV file. Ensure the file is UTF-8 encoded to prevent character issues.
Finalize Column Headers: Open the CSV file and confirm the column headers match the required and recommended attributes exactly: InvoiceNumber, ActivityName, EventTime, UserName, CustomerNumber, InvoiceAmount, PaymentDueDate, and DaysOverdue.
Upload to Process Mining Tool: The finalized CSV file is now ready to be uploaded to ProcessMind or your chosen process mining platform.

Configuration

Date Range: We recommend extracting a data slice of 3 to 6 months to balance dataset representativeness with system performance. The placeholders [Start Date] and [End Date] in the query must be set accordingly.
Company Code Filter: The query is designed to be filtered by CompanyCode. Limiting the extraction to one or a few company codes at a time is crucial for managing performance and creating a focused analysis.
Document Type Filter: It is essential to filter for specific invoice document types (e.g., 'RV') using the AccountingDocumentType field. This prevents non-relevant documents like vendor invoices or credit memos from being included in the event log.
Customizable Logic: Several activities rely on configuration-specific codes. You must identify and provide the correct values for [Your Write-off Reason Code] and [Your Dispute Case Closed Status] based on your system's setup.
System Prerequisites: This method requires that your S/4HANA system actively uses the Financial Supply Chain Management (FSCM) modules for Dispute and Collections Management. Without these modules, related activities like 'Dispute Case Registered' and 'Promise To Pay Created' cannot be extracted.
Authorization: The SAP user executing the extraction needs read permissions for all underlying CDS views. This typically involves authorization objects for general ledger accounting (e.g., F_BKPF_BUK), customer master data, and specific FSCM components.

a Sample Query sql

SELECT
    INV.AccountingDocument AS "InvoiceNumber",
    'Invoice Generated' AS "ActivityName",
    INV.CreationDateTime AS "EventTime",
    INV.CreatedByUser AS "UserName",
    INV.Customer AS "CustomerNumber",
    INV.AmountInTransactionCurrency AS "InvoiceAmount",
    INV.NetDueDate AS "PaymentDueDate",
    0 AS "DaysOverdue"
FROM I_OperationalAcctgDocItemCube AS INV
WHERE INV.CompanyCode IN ('[Your Company Code]') AND INV.AccountingDocumentType = '[Your Invoice Doc Type]' AND INV.CreationDate BETWEEN '[Start Date]' AND '[End Date]'

UNION ALL

SELECT
    INV.AccountingDocument AS "InvoiceNumber",
    'Invoice Sent To Customer' AS "ActivityName",
    OUTP.OutputRequestDateTime AS "EventTime",
    OUTP.LastChangedByUser AS "UserName",
    INV.Customer AS "CustomerNumber",
    INV.AmountInTransactionCurrency AS "InvoiceAmount",
    INV.NetDueDate AS "PaymentDueDate",
    CASE WHEN INV.NetDueDate < CAST(OUTP.OutputRequestDateTime AS DATE) THEN DATEDIFF(day, INV.NetDueDate, CAST(OUTP.OutputRequestDateTime AS DATE)) ELSE 0 END AS "DaysOverdue"
FROM I_BillingDocumentOutputRequest AS OUTP
JOIN I_OperationalAcctgDocItemCube AS INV ON OUTP.BillingDocument = INV.AccountingDocument AND INV.CompanyCode IN ('[Your Company Code]') AND INV.AccountingDocumentType = '[Your Invoice Doc Type]'
WHERE CAST(OUTP.OutputRequestDateTime AS DATE) BETWEEN '[Start Date]' AND '[End Date]'

UNION ALL

SELECT
    INV.AccountingDocument AS "InvoiceNumber",
    'Payment Due Date Passed' AS "ActivityName",
    INV.NetDueDate AS "EventTime",
    'SYSTEM' AS "UserName",
    INV.Customer AS "CustomerNumber",
    INV.AmountInTransactionCurrency AS "InvoiceAmount",
    INV.NetDueDate AS "PaymentDueDate",
    0 AS "DaysOverdue"
FROM I_OperationalAcctgDocItemCube AS INV
WHERE INV.CompanyCode IN ('[Your Company Code]') AND INV.AccountingDocumentType = '[Your Invoice Doc Type]' AND INV.IsCleared = '' AND INV.NetDueDate BETWEEN '[Start Date]' AND '[End Date]' AND INV.NetDueDate < CURRENT_DATE

UNION ALL

SELECT
    REL.RelatedAccountingDocument AS "InvoiceNumber",
    'Dispute Case Registered' AS "ActivityName",
    DCASE.CreationDateTime AS "EventTime",
    DCASE.CreatedByUser AS "UserName",
    INV.Customer AS "CustomerNumber",
    INV.AmountInTransactionCurrency AS "InvoiceAmount",
    INV.NetDueDate AS "PaymentDueDate",
    CASE WHEN INV.NetDueDate < CAST(DCASE.CreationDateTime AS DATE) THEN DATEDIFF(day, INV.NetDueDate, CAST(DCASE.CreationDateTime AS DATE)) ELSE 0 END AS "DaysOverdue"
FROM I_DisputeCase AS DCASE
JOIN I_DisputeCaseRelatedObject AS REL ON DCASE.DisputeCase = REL.DisputeCase
JOIN I_OperationalAcctgDocItemCube AS INV ON REL.RelatedAccountingDocument = INV.AccountingDocument AND REL.RelatedCompanyCode = INV.CompanyCode AND INV.AccountingDocumentType = '[Your Invoice Doc Type]'
WHERE INV.CompanyCode IN ('[Your Company Code]') AND CAST(DCASE.CreationDateTime AS DATE) BETWEEN '[Start Date]' AND '[End Date]'

UNION ALL

SELECT
    DUN.AccountingDocument AS "InvoiceNumber",
    'Dunning Procedure Initiated' AS "ActivityName",
    DUN.DunningDate AS "EventTime",
    DUN.DunningRunUser AS "UserName",
    INV.Customer AS "CustomerNumber",
    INV.AmountInTransactionCurrency AS "InvoiceAmount",
    INV.NetDueDate AS "PaymentDueDate",
    CASE WHEN INV.NetDueDate < DUN.DunningDate THEN DATEDIFF(day, INV.NetDueDate, DUN.DunningDate) ELSE 0 END AS "DaysOverdue"
FROM I_DunningHistoryItem AS DUN
JOIN I_OperationalAcctgDocItemCube AS INV ON DUN.AccountingDocument = INV.AccountingDocument AND DUN.CompanyCode = INV.CompanyCode AND INV.AccountingDocumentType = '[Your Invoice Doc Type]'
WHERE DUN.CompanyCode IN ('[Your Company Code]') AND DUN.DunningDate BETWEEN '[Start Date]' AND '[End Date]'

UNION ALL

-- Note: This activity requires a custom view. Replace '[Your Custom Contact Log View]' with your specific implementation.
SELECT
    LOG.InvoiceReference AS "InvoiceNumber",
    'Collection Call Made' AS "ActivityName",
    LOG.CreationDateTime AS "EventTime",
    LOG.CreatedByUser AS "UserName",
    INV.Customer AS "CustomerNumber",
    INV.AmountInTransactionCurrency AS "InvoiceAmount",
    INV.NetDueDate AS "PaymentDueDate",
    CASE WHEN INV.NetDueDate < CAST(LOG.CreationDateTime AS DATE) THEN DATEDIFF(day, INV.NetDueDate, CAST(LOG.CreationDateTime AS DATE)) ELSE 0 END AS "DaysOverdue"
FROM [Your Custom Contact Log View] AS LOG
JOIN I_OperationalAcctgDocItemCube AS INV ON LOG.InvoiceReference = INV.AccountingDocument AND INV.CompanyCode IN ('[Your Company Code]') AND INV.AccountingDocumentType = '[Your Invoice Doc Type]'
WHERE CAST(LOG.CreationDateTime AS DATE) BETWEEN '[Start Date]' AND '[End Date]'

UNION ALL

SELECT
    P2P.OriginalReferenceDocument AS "InvoiceNumber",
    'Promise To Pay Created' AS "ActivityName",
    P2P.CreationDateTime AS "EventTime",
    P2P.CreatedByUser AS "UserName",
    INV.Customer AS "CustomerNumber",
    INV.AmountInTransactionCurrency AS "InvoiceAmount",
    INV.NetDueDate AS "PaymentDueDate",
    CASE WHEN INV.NetDueDate < CAST(P2P.CreationDateTime AS DATE) THEN DATEDIFF(day, INV.NetDueDate, CAST(P2P.CreationDateTime AS DATE)) ELSE 0 END AS "DaysOverdue"
FROM I_PromiseToPay AS P2P
JOIN I_OperationalAcctgDocItemCube AS INV ON P2P.OriginalReferenceDocument = INV.AccountingDocument AND P2P.CompanyCode = INV.CompanyCode AND INV.AccountingDocumentType = '[Your Invoice Doc Type]'
WHERE P2P.CompanyCode IN ('[Your Company Code]') AND CAST(P2P.CreationDateTime AS DATE) BETWEEN '[Start Date]' AND '[End Date]'

UNION ALL

SELECT
    INV.AccountingDocument AS "InvoiceNumber",
    'Payment Received' AS "ActivityName",
    INV.ClearingDate AS "EventTime",
    INV.ClearingJournalEntryUser AS "UserName",
    INV.Customer AS "CustomerNumber",
    INV.AmountInTransactionCurrency AS "InvoiceAmount",
    INV.NetDueDate AS "PaymentDueDate",
    CASE WHEN INV.NetDueDate < INV.ClearingDate THEN DATEDIFF(day, INV.NetDueDate, INV.ClearingDate) ELSE 0 END AS "DaysOverdue"
FROM I_OperationalAcctgDocItemCube AS INV
WHERE INV.CompanyCode IN ('[Your Company Code]') AND INV.AccountingDocumentType = '[Your Invoice Doc Type]' AND INV.IsCleared = 'X' AND INV.ClearingDate BETWEEN '[Start Date]' AND '[End Date]'

UNION ALL

SELECT
    INV.AccountingDocument AS "InvoiceNumber",
    'Payment Posted' AS "ActivityName",
    INV.ClearingDate AS "EventTime", -- Often same as Payment Received timestamp
    INV.ClearingJournalEntryUser AS "UserName",
    INV.Customer AS "CustomerNumber",
    INV.AmountInTransactionCurrency AS "InvoiceAmount",
    INV.NetDueDate AS "PaymentDueDate",
    CASE WHEN INV.NetDueDate < INV.ClearingDate THEN DATEDIFF(day, INV.NetDueDate, INV.ClearingDate) ELSE 0 END AS "DaysOverdue"
FROM I_OperationalAcctgDocItemCube AS INV
WHERE INV.CompanyCode IN ('[Your Company Code]') AND INV.AccountingDocumentType = '[Your Invoice Doc Type]' AND INV.IsCleared = 'X' AND INV.ClearingDate BETWEEN '[Start Date]' AND '[End Date]'

UNION ALL

SELECT
    REL.RelatedAccountingDocument AS "InvoiceNumber",
    'Dispute Resolved' AS "ActivityName",
    DCASE.LastChangeDateTime AS "EventTime",
    DCASE.LastChangedByUser AS "UserName",
    INV.Customer AS "CustomerNumber",
    INV.AmountInTransactionCurrency AS "InvoiceAmount",
    INV.NetDueDate AS "PaymentDueDate",
    CASE WHEN INV.NetDueDate < CAST(DCASE.LastChangeDateTime AS DATE) THEN DATEDIFF(day, INV.NetDueDate, CAST(DCASE.LastChangeDateTime AS DATE)) ELSE 0 END AS "DaysOverdue"
FROM I_DisputeCase AS DCASE
JOIN I_DisputeCaseRelatedObject AS REL ON DCASE.DisputeCase = REL.DisputeCase
JOIN I_OperationalAcctgDocItemCube AS INV ON REL.RelatedAccountingDocument = INV.AccountingDocument AND REL.RelatedCompanyCode = INV.CompanyCode AND INV.AccountingDocumentType = '[Your Invoice Doc Type]'
WHERE INV.CompanyCode IN ('[Your Company Code]') AND DCASE.DisputeCaseProcessingStatus = '[Your Dispute Case Closed Status]' AND CAST(DCASE.LastChangeDateTime AS DATE) BETWEEN '[Start Date]' AND '[End Date]'

UNION ALL

SELECT
    INV.AccountingDocument AS "InvoiceNumber",
    'Invoice Settled' AS "ActivityName",
    INV.ClearingDate AS "EventTime",
    INV.ClearingJournalEntryUser AS "UserName",
    INV.Customer AS "CustomerNumber",
    INV.AmountInTransactionCurrency AS "InvoiceAmount",
    INV.NetDueDate AS "PaymentDueDate",
    CASE WHEN INV.NetDueDate < INV.ClearingDate THEN DATEDIFF(day, INV.NetDueDate, INV.ClearingDate) ELSE 0 END AS "DaysOverdue"
FROM I_OperationalAcctgDocItemCube AS INV
WHERE INV.CompanyCode IN ('[Your Company Code]') AND INV.AccountingDocumentType = '[Your Invoice Doc Type]' AND INV.IsCleared = 'X' AND INV.ClearingDate BETWEEN '[Start Date]' AND '[End Date]'

UNION ALL

SELECT
    INV.AccountingDocument AS "InvoiceNumber",
    'Invoice Written Off' AS "ActivityName",
    INV.ClearingDate AS "EventTime",
    INV.ClearingJournalEntryUser AS "UserName",
    INV.Customer AS "CustomerNumber",
    INV.AmountInTransactionCurrency AS "InvoiceAmount",
    INV.NetDueDate AS "PaymentDueDate",
    CASE WHEN INV.NetDueDate < INV.ClearingDate THEN DATEDIFF(day, INV.NetDueDate, INV.ClearingDate) ELSE 0 END AS "DaysOverdue"
FROM I_OperationalAcctgDocItemCube AS INV
WHERE INV.CompanyCode IN ('[Your Company Code]') AND INV.AccountingDocumentType = '[Your Invoice Doc Type]' AND INV.IsCleared = 'X' AND INV.ClearingReason = '[Your Write-off Reason Code]' AND INV.ClearingDate BETWEEN '[Start Date]' AND '[End Date]'

Steps

Verify CDS View Accessibility: Log in to your SAP S/4HANA system and use the 'View Browser' Fiori app. Search for key CDS views mentioned in the query, such as I_OperationalAcctgDocItemCube and I_DisputeCase, to ensure they are active and contain data. This confirms the necessary components are available.
Expose OData Services: Use transaction /IWFND/MAINT_SERVICE to verify that the relevant CDS views are published as OData services. If they are not, you will need to publish them to make them accessible to external tools.
Establish Client Connectivity: Use your data extraction tool (such as Microsoft Power BI, SAP Analytics Cloud, Python, or others) to create a new connection to your SAP S/4HANA system's OData endpoint.
Authenticate: Provide the connection details and authenticate with a user ID that has sufficient authorizations to read financial accounting, collections management, and dispute management data.
Prepare the Query: Copy the complete SQL query provided in the 'query' section of this document.
Customize Parameters: In the query text, replace all placeholder values with ones that are specific to your system and analysis scope. This includes [Your Company Code], [Start Date], [End Date], [Your Invoice Doc Type], [Your Write-off Reason Code], and [Your Dispute Case Closed Status].
Adapt for Custom Logging: The 'Collection Call Made' activity relies on a placeholder view [Your Custom Contact Log View]. You must replace this with the actual table or view where your organization logs collection agent contacts. If this data is not available, remove this entire UNION ALL block from the query.
Execute the Query: Run the customized query from your connected client tool. Depending on the tool, you may need to adapt the SQL syntax to its specific OData query language, for instance, by converting WHERE clauses to $filter parameters.
Review and Validate: Once the query finishes, perform a preliminary review of the returned data. Check for a reasonable number of rows and ensure that various activities are present.
Export to CSV: Export the complete result set to a CSV file. Ensure the file is UTF-8 encoded to prevent character issues.
Finalize Column Headers: Open the CSV file and confirm the column headers match the required and recommended attributes exactly: InvoiceNumber, ActivityName, EventTime, UserName, CustomerNumber, InvoiceAmount, PaymentDueDate, and DaysOverdue.
Upload to Process Mining Tool: The finalized CSV file is now ready to be uploaded to ProcessMind or your chosen process mining platform.

Configuration

Date Range: We recommend extracting a data slice of 3 to 6 months to balance dataset representativeness with system performance. The placeholders [Start Date] and [End Date] in the query must be set accordingly.
Company Code Filter: The query is designed to be filtered by CompanyCode. Limiting the extraction to one or a few company codes at a time is crucial for managing performance and creating a focused analysis.
Document Type Filter: It is essential to filter for specific invoice document types (e.g., 'RV') using the AccountingDocumentType field. This prevents non-relevant documents like vendor invoices or credit memos from being included in the event log.
Customizable Logic: Several activities rely on configuration-specific codes. You must identify and provide the correct values for [Your Write-off Reason Code] and [Your Dispute Case Closed Status] based on your system's setup.
System Prerequisites: This method requires that your S/4HANA system actively uses the Financial Supply Chain Management (FSCM) modules for Dispute and Collections Management. Without these modules, related activities like 'Dispute Case Registered' and 'Promise To Pay Created' cannot be extracted.
Authorization: The SAP user executing the extraction needs read permissions for all underlying CDS views. This typically involves authorization objects for general ledger accounting (e.g., F_BKPF_BUK), customer master data, and specific FSCM components.

a Sample Query sql

SELECT
    INV.AccountingDocument AS "InvoiceNumber",
    'Invoice Generated' AS "ActivityName",
    INV.CreationDateTime AS "EventTime",
    INV.CreatedByUser AS "UserName",
    INV.Customer AS "CustomerNumber",
    INV.AmountInTransactionCurrency AS "InvoiceAmount",
    INV.NetDueDate AS "PaymentDueDate",
    0 AS "DaysOverdue"
FROM I_OperationalAcctgDocItemCube AS INV
WHERE INV.CompanyCode IN ('[Your Company Code]') AND INV.AccountingDocumentType = '[Your Invoice Doc Type]' AND INV.CreationDate BETWEEN '[Start Date]' AND '[End Date]'

UNION ALL

SELECT
    INV.AccountingDocument AS "InvoiceNumber",
    'Invoice Sent To Customer' AS "ActivityName",
    OUTP.OutputRequestDateTime AS "EventTime",
    OUTP.LastChangedByUser AS "UserName",
    INV.Customer AS "CustomerNumber",
    INV.AmountInTransactionCurrency AS "InvoiceAmount",
    INV.NetDueDate AS "PaymentDueDate",
    CASE WHEN INV.NetDueDate < CAST(OUTP.OutputRequestDateTime AS DATE) THEN DATEDIFF(day, INV.NetDueDate, CAST(OUTP.OutputRequestDateTime AS DATE)) ELSE 0 END AS "DaysOverdue"
FROM I_BillingDocumentOutputRequest AS OUTP
JOIN I_OperationalAcctgDocItemCube AS INV ON OUTP.BillingDocument = INV.AccountingDocument AND INV.CompanyCode IN ('[Your Company Code]') AND INV.AccountingDocumentType = '[Your Invoice Doc Type]'
WHERE CAST(OUTP.OutputRequestDateTime AS DATE) BETWEEN '[Start Date]' AND '[End Date]'

UNION ALL

SELECT
    INV.AccountingDocument AS "InvoiceNumber",
    'Payment Due Date Passed' AS "ActivityName",
    INV.NetDueDate AS "EventTime",
    'SYSTEM' AS "UserName",
    INV.Customer AS "CustomerNumber",
    INV.AmountInTransactionCurrency AS "InvoiceAmount",
    INV.NetDueDate AS "PaymentDueDate",
    0 AS "DaysOverdue"
FROM I_OperationalAcctgDocItemCube AS INV
WHERE INV.CompanyCode IN ('[Your Company Code]') AND INV.AccountingDocumentType = '[Your Invoice Doc Type]' AND INV.IsCleared = '' AND INV.NetDueDate BETWEEN '[Start Date]' AND '[End Date]' AND INV.NetDueDate < CURRENT_DATE

UNION ALL

SELECT
    REL.RelatedAccountingDocument AS "InvoiceNumber",
    'Dispute Case Registered' AS "ActivityName",
    DCASE.CreationDateTime AS "EventTime",
    DCASE.CreatedByUser AS "UserName",
    INV.Customer AS "CustomerNumber",
    INV.AmountInTransactionCurrency AS "InvoiceAmount",
    INV.NetDueDate AS "PaymentDueDate",
    CASE WHEN INV.NetDueDate < CAST(DCASE.CreationDateTime AS DATE) THEN DATEDIFF(day, INV.NetDueDate, CAST(DCASE.CreationDateTime AS DATE)) ELSE 0 END AS "DaysOverdue"
FROM I_DisputeCase AS DCASE
JOIN I_DisputeCaseRelatedObject AS REL ON DCASE.DisputeCase = REL.DisputeCase
JOIN I_OperationalAcctgDocItemCube AS INV ON REL.RelatedAccountingDocument = INV.AccountingDocument AND REL.RelatedCompanyCode = INV.CompanyCode AND INV.AccountingDocumentType = '[Your Invoice Doc Type]'
WHERE INV.CompanyCode IN ('[Your Company Code]') AND CAST(DCASE.CreationDateTime AS DATE) BETWEEN '[Start Date]' AND '[End Date]'

UNION ALL

SELECT
    DUN.AccountingDocument AS "InvoiceNumber",
    'Dunning Procedure Initiated' AS "ActivityName",
    DUN.DunningDate AS "EventTime",
    DUN.DunningRunUser AS "UserName",
    INV.Customer AS "CustomerNumber",
    INV.AmountInTransactionCurrency AS "InvoiceAmount",
    INV.NetDueDate AS "PaymentDueDate",
    CASE WHEN INV.NetDueDate < DUN.DunningDate THEN DATEDIFF(day, INV.NetDueDate, DUN.DunningDate) ELSE 0 END AS "DaysOverdue"
FROM I_DunningHistoryItem AS DUN
JOIN I_OperationalAcctgDocItemCube AS INV ON DUN.AccountingDocument = INV.AccountingDocument AND DUN.CompanyCode = INV.CompanyCode AND INV.AccountingDocumentType = '[Your Invoice Doc Type]'
WHERE DUN.CompanyCode IN ('[Your Company Code]') AND DUN.DunningDate BETWEEN '[Start Date]' AND '[End Date]'

UNION ALL

-- Note: This activity requires a custom view. Replace '[Your Custom Contact Log View]' with your specific implementation.
SELECT
    LOG.InvoiceReference AS "InvoiceNumber",
    'Collection Call Made' AS "ActivityName",
    LOG.CreationDateTime AS "EventTime",
    LOG.CreatedByUser AS "UserName",
    INV.Customer AS "CustomerNumber",
    INV.AmountInTransactionCurrency AS "InvoiceAmount",
    INV.NetDueDate AS "PaymentDueDate",
    CASE WHEN INV.NetDueDate < CAST(LOG.CreationDateTime AS DATE) THEN DATEDIFF(day, INV.NetDueDate, CAST(LOG.CreationDateTime AS DATE)) ELSE 0 END AS "DaysOverdue"
FROM [Your Custom Contact Log View] AS LOG
JOIN I_OperationalAcctgDocItemCube AS INV ON LOG.InvoiceReference = INV.AccountingDocument AND INV.CompanyCode IN ('[Your Company Code]') AND INV.AccountingDocumentType = '[Your Invoice Doc Type]'
WHERE CAST(LOG.CreationDateTime AS DATE) BETWEEN '[Start Date]' AND '[End Date]'

UNION ALL

SELECT
    P2P.OriginalReferenceDocument AS "InvoiceNumber",
    'Promise To Pay Created' AS "ActivityName",
    P2P.CreationDateTime AS "EventTime",
    P2P.CreatedByUser AS "UserName",
    INV.Customer AS "CustomerNumber",
    INV.AmountInTransactionCurrency AS "InvoiceAmount",
    INV.NetDueDate AS "PaymentDueDate",
    CASE WHEN INV.NetDueDate < CAST(P2P.CreationDateTime AS DATE) THEN DATEDIFF(day, INV.NetDueDate, CAST(P2P.CreationDateTime AS DATE)) ELSE 0 END AS "DaysOverdue"
FROM I_PromiseToPay AS P2P
JOIN I_OperationalAcctgDocItemCube AS INV ON P2P.OriginalReferenceDocument = INV.AccountingDocument AND P2P.CompanyCode = INV.CompanyCode AND INV.AccountingDocumentType = '[Your Invoice Doc Type]'
WHERE P2P.CompanyCode IN ('[Your Company Code]') AND CAST(P2P.CreationDateTime AS DATE) BETWEEN '[Start Date]' AND '[End Date]'

UNION ALL

SELECT
    INV.AccountingDocument AS "InvoiceNumber",
    'Payment Received' AS "ActivityName",
    INV.ClearingDate AS "EventTime",
    INV.ClearingJournalEntryUser AS "UserName",
    INV.Customer AS "CustomerNumber",
    INV.AmountInTransactionCurrency AS "InvoiceAmount",
    INV.NetDueDate AS "PaymentDueDate",
    CASE WHEN INV.NetDueDate < INV.ClearingDate THEN DATEDIFF(day, INV.NetDueDate, INV.ClearingDate) ELSE 0 END AS "DaysOverdue"
FROM I_OperationalAcctgDocItemCube AS INV
WHERE INV.CompanyCode IN ('[Your Company Code]') AND INV.AccountingDocumentType = '[Your Invoice Doc Type]' AND INV.IsCleared = 'X' AND INV.ClearingDate BETWEEN '[Start Date]' AND '[End Date]'

UNION ALL

SELECT
    INV.AccountingDocument AS "InvoiceNumber",
    'Payment Posted' AS "ActivityName",
    INV.ClearingDate AS "EventTime", -- Often same as Payment Received timestamp
    INV.ClearingJournalEntryUser AS "UserName",
    INV.Customer AS "CustomerNumber",
    INV.AmountInTransactionCurrency AS "InvoiceAmount",
    INV.NetDueDate AS "PaymentDueDate",
    CASE WHEN INV.NetDueDate < INV.ClearingDate THEN DATEDIFF(day, INV.NetDueDate, INV.ClearingDate) ELSE 0 END AS "DaysOverdue"
FROM I_OperationalAcctgDocItemCube AS INV
WHERE INV.CompanyCode IN ('[Your Company Code]') AND INV.AccountingDocumentType = '[Your Invoice Doc Type]' AND INV.IsCleared = 'X' AND INV.ClearingDate BETWEEN '[Start Date]' AND '[End Date]'

UNION ALL

SELECT
    REL.RelatedAccountingDocument AS "InvoiceNumber",
    'Dispute Resolved' AS "ActivityName",
    DCASE.LastChangeDateTime AS "EventTime",
    DCASE.LastChangedByUser AS "UserName",
    INV.Customer AS "CustomerNumber",
    INV.AmountInTransactionCurrency AS "InvoiceAmount",
    INV.NetDueDate AS "PaymentDueDate",
    CASE WHEN INV.NetDueDate < CAST(DCASE.LastChangeDateTime AS DATE) THEN DATEDIFF(day, INV.NetDueDate, CAST(DCASE.LastChangeDateTime AS DATE)) ELSE 0 END AS "DaysOverdue"
FROM I_DisputeCase AS DCASE
JOIN I_DisputeCaseRelatedObject AS REL ON DCASE.DisputeCase = REL.DisputeCase
JOIN I_OperationalAcctgDocItemCube AS INV ON REL.RelatedAccountingDocument = INV.AccountingDocument AND REL.RelatedCompanyCode = INV.CompanyCode AND INV.AccountingDocumentType = '[Your Invoice Doc Type]'
WHERE INV.CompanyCode IN ('[Your Company Code]') AND DCASE.DisputeCaseProcessingStatus = '[Your Dispute Case Closed Status]' AND CAST(DCASE.LastChangeDateTime AS DATE) BETWEEN '[Start Date]' AND '[End Date]'

UNION ALL

SELECT
    INV.AccountingDocument AS "InvoiceNumber",
    'Invoice Settled' AS "ActivityName",
    INV.ClearingDate AS "EventTime",
    INV.ClearingJournalEntryUser AS "UserName",
    INV.Customer AS "CustomerNumber",
    INV.AmountInTransactionCurrency AS "InvoiceAmount",
    INV.NetDueDate AS "PaymentDueDate",
    CASE WHEN INV.NetDueDate < INV.ClearingDate THEN DATEDIFF(day, INV.NetDueDate, INV.ClearingDate) ELSE 0 END AS "DaysOverdue"
FROM I_OperationalAcctgDocItemCube AS INV
WHERE INV.CompanyCode IN ('[Your Company Code]') AND INV.AccountingDocumentType = '[Your Invoice Doc Type]' AND INV.IsCleared = 'X' AND INV.ClearingDate BETWEEN '[Start Date]' AND '[End Date]'

UNION ALL

SELECT
    INV.AccountingDocument AS "InvoiceNumber",
    'Invoice Written Off' AS "ActivityName",
    INV.ClearingDate AS "EventTime",
    INV.ClearingJournalEntryUser AS "UserName",
    INV.Customer AS "CustomerNumber",
    INV.AmountInTransactionCurrency AS "InvoiceAmount",
    INV.NetDueDate AS "PaymentDueDate",
    CASE WHEN INV.NetDueDate < INV.ClearingDate THEN DATEDIFF(day, INV.NetDueDate, INV.ClearingDate) ELSE 0 END AS "DaysOverdue"
FROM I_OperationalAcctgDocItemCube AS INV
WHERE INV.CompanyCode IN ('[Your Company Code]') AND INV.AccountingDocumentType = '[Your Invoice Doc Type]' AND INV.IsCleared = 'X' AND INV.ClearingReason = '[Your Write-off Reason Code]' AND INV.ClearingDate BETWEEN '[Start Date]' AND '[End Date]'

Steps

Prerequisites Installation: Before running the script, ensure you have Python 3.6 or newer installed. Install the necessary Python library, PyRFC, using pip: pip install pyrfc. Additionally, download the SAP NWRFC SDK from the SAP Support Portal and place its lib folder in a location accessible by your system's path.
SAP User and Authorization: A dedicated SAP user account is required to run the extraction. This user must have authorizations for remote function calls (RFC) via the S_RFC authorization object. The user also needs specific permissions to execute the RFC_READ_TABLE function module and read access to the underlying tables referenced in the script, such as BKPF, BSEG, BSID, BSAD, NAST, UDM_SPS_CASE_ATTR, MHNK, and UDM_P2P_ATTR.
Configure Connection Details: Open the Python script and locate the sap_config dictionary. Populate this dictionary with the correct connection parameters for your target SAP S/4HANA system, including the application server host, system number, client, and the username and password for the dedicated SAP user.
Set Extraction Parameters: In the main section of the script, define the key extraction parameters. Set the company_codes list to include the SAP company codes you want to analyze. Specify the start_date and end_date in 'YYYYMMDD' format to define the time window for the extraction. This date range is crucial for controlling the data volume.
Execute the Python Script: Run the script from your command line or terminal using the command python your_script_name.py. The script will connect to SAP and execute a series of function calls, one for each activity defined. It will print progress messages to the console as it extracts data for each activity type.
Data Aggregation and Transformation: The script automatically gathers the data for each of the 12 activities into separate data structures. It then combines them into a single, unified event log using the pandas library. During this step, it standardizes column names and formats the data correctly.
Calculation of Attributes: After aggregation, the script performs necessary calculations. For instance, it computes the DaysOverdue attribute by comparing the event date to the PaymentDueDate for relevant activities, providing valuable context for the process analysis.
Export to CSV: Once the data is fully processed and transformed, the script saves the final event log as a CSV file named collections_event_log.csv in the same directory where the script is located. This file is formatted for direct upload into process mining software.

Configuration

SAP Connection Parameters: The script requires a dictionary containing the connection details for the target SAP system: ashost (Application Server Host), sysnr (System Number), client (Client ID), user (SAP User ID), and passwd (Password).
SAP Authorizations: Ensure the SAP user has RFC permissions and read access to all required financial and collections management tables. Key authorization objects include S_RFC for remote calls and S_TABU_DIS for table display access.
Date Range: It is critical to set a start_date and end_date to limit the scope of the extraction. For initial analysis, a range of 3 to 6 months is recommended to ensure manageable data volume and script execution time.
Company Code Filter: The company_codes parameter is a mandatory filter to select data for specific legal entities. Extracting data for one or a few company codes at a time is highly recommended for better performance.
Document Type Filter: Within the script's logic, filtering by invoice document types, for example 'RV' or 'RE', is used to ensure only relevant customer invoices are included in the dataset. This should be configured based on your system's specific document type usage.
Performance Considerations: Direct table reads via RFC_READ_TABLE on large tables like BSEG or BSAD can be resource intensive. Run the script during non-peak hours and always use selective filters on indexed fields like company code, fiscal year, and document posting date.

a Sample Query python

import pandas as pd
from pyrfc import Connection
from datetime import datetime, timedelta

class=class="hl-string">"hl-comment"># --- SAP Connection Configuration ---
class=class="hl-string">"hl-comment"># Replace with your SAP system's connection details
sap_config = {
    class="hl-string">'ashost': class="hl-string">'[Your SAP Application Server Host]',
    class="hl-string">'sysnr': class="hl-string">'[Your System Number]',
    class="hl-string">'client': class="hl-string">'[Your Client ID]',
    class="hl-string">'user': class="hl-string">'[Your SAP User]',
    class="hl-string">'passwd': class="hl-string">'[Your SAP Password]'
}

class=class="hl-string">"hl-comment"># --- Helper Functions ---
def rfc_read_to_df(conn, table_name, fields, options):
    class="hl-string">"""Generic function to read an SAP table and return a pandas DataFrame."""
    result = conn.call(class="hl-string">'RFC_READ_TABLE',
                       QUERY_TABLE=table_name,
                       DELIMITER=class="hl-string">',',
                       FIELDS=fields,
                       OPTIONS=options)
    data = result[class="hl-string">'DATA']
    if not data:
        return pd.DataFrame(columns=[f[class="hl-string">'FIELDNAME'] for f in fields])
    
    records = [dict(zip([f[class="hl-string">'FIELDNAME'] for f in fields], row[class="hl-string">'WA'].strip().split(class="hl-string">','))) for row in data]
    return pd.DataFrame(records)

def format_sap_date(date_str):
    class="hl-string">"""Converts SAP date string (YYYYMMDD) to datetime object."""
    if date_str and date_str != class="hl-string">'00000000':
        return datetime.strptime(date_str, class="hl-string">'%Y%m%d')
    return None

def format_sap_time(time_str):
    class="hl-string">"""Converts SAP time string (HHMMSS) to timedelta object."""
    if time_str and len(time_str) == 6:
        return timedelta(hours=int(time_str[0:2]), minutes=int(time_str[2:4]), seconds=int(time_str[4:6]))
    return timedelta(0)

class=class="hl-string">"hl-comment"># --- Activity Extraction Functions ---

def get_invoices_generated(conn, company_codes, start_date, end_date):
    options = [fclass="hl-string">"(BUKRS IN ('{','.join(company_codes)}') AND BUDAT >= '{start_date}' AND BUDAT <= '{end_date}' AND BLART IN ('RV', 'RE'))"] class=class="hl-string">"hl-comment"># Filter for customer invoices
    bkpf_fields = [{class="hl-string">'FIELDNAME': class="hl-string">'BELNR'}, {class="hl-string">'FIELDNAME': class="hl-string">'GJAHR'}, {class="hl-string">'FIELDNAME': class="hl-string">'BUKRS'}, {class="hl-string">'FIELDNAME': class="hl-string">'BUDAT'}, {class="hl-string">'FIELDNAME': class="hl-string">'CPUDT'}, {class="hl-string">'FIELDNAME': class="hl-string">'CPUTM'}, {class="hl-string">'FIELDNAME': class="hl-string">'USNAM'}]
    bseg_fields = [{class="hl-string">'FIELDNAME': class="hl-string">'BELNR'}, {class="hl-string">'FIELDNAME': class="hl-string">'GJAHR'}, {class="hl-string">'FIELDNAME': class="hl-string">'BUKRS'}, {class="hl-string">'FIELDNAME': class="hl-string">'KUNNR'}, {class="hl-string">'FIELDNAME': class="hl-string">'WRBTR'}, {class="hl-string">'FIELDNAME': class="hl-string">'ZFBDT'}]
    
    df_bkpf = rfc_read_to_df(conn, class="hl-string">'BKPF', bkpf_fields, options)
    if df_bkpf.empty: return pd.DataFrame()

    class=class="hl-string">"hl-comment"># Fetch line item details
    invoice_keys = [fclass="hl-string">"(BELNR = '{row.BELNR}' AND GJAHR = '{row.GJAHR}' AND BUKRS = '{row.BUKRS}')" for index, row in df_bkpf.iterrows()]
    bseg_options = [class="hl-string">" OR ".join(invoice_keys[i:i+50]) for i in range(0, len(invoice_keys), 50)] class=class="hl-string">"hl-comment"># Batch options
    df_bseg_list = [rfc_read_to_df(conn, class="hl-string">'BSEG', bseg_fields, [opt]) for opt in bseg_options]
    df_bseg = pd.concat(df_bseg_list)
    df_bseg = df_bseg[df_bseg[class="hl-string">'KUNNR'] != class="hl-string">''].drop_duplicates([class="hl-string">'BELNR', class="hl-string">'GJAHR', class="hl-string">'BUKRS'])
    
    df = pd.merge(df_bkpf, df_bseg, on=[class="hl-string">'BELNR', class="hl-string">'GJAHR', class="hl-string">'BUKRS'])

    df[class="hl-string">'ActivityName'] = class="hl-string">'Invoice Generated'
    df[class="hl-string">'EventTime'] = df.apply(lambda row: format_sap_date(row[class="hl-string">'CPUDT']) + format_sap_time(row[class="hl-string">'CPUTM']), axis=1)
    df.rename(columns={class="hl-string">'BELNR': class="hl-string">'InvoiceNumber', class="hl-string">'USNAM': class="hl-string">'UserName', class="hl-string">'KUNNR': class="hl-string">'CustomerNumber', class="hl-string">'WRBTR': class="hl-string">'InvoiceAmount', class="hl-string">'ZFBDT': class="hl-string">'PaymentDueDate'}, inplace=True)
    df[class="hl-string">'PaymentDueDate'] = df[class="hl-string">'PaymentDueDate'].apply(format_sap_date)
    return df[[class="hl-string">'InvoiceNumber', class="hl-string">'ActivityName', class="hl-string">'EventTime', class="hl-string">'UserName', class="hl-string">'CustomerNumber', class="hl-string">'InvoiceAmount', class="hl-string">'PaymentDueDate']]

def get_invoices_sent(conn, company_codes, start_date, end_date):
    options = [fclass="hl-string">"KAPPL = 'V3' AND DATUM >= '{start_date}' AND DATUM <= '{end_date}'"] class=class="hl-string">"hl-comment"># V3 is for Billing
    nkey_prefix = [fclass="hl-string">"{client}{cc}" for client in [sap_config[class="hl-string">'client']] for cc in company_codes]
    options[0] += fclass="hl-string">" AND (LEFT(OBJKY, 5) IN ('{','.join(nkey_prefix)}'))"

    fields = [{class="hl-string">'FIELDNAME': class="hl-string">'OBJKY'}, {class="hl-string">'FIELDNAME': class="hl-string">'DATUM'}, {class="hl-string">'FIELDNAME': class="hl-string">'UZEIT'}, {class="hl-string">'FIELDNAME': class="hl-string">'USNAM'}]
    df = rfc_read_to_df(conn, class="hl-string">'NAST', fields, options)
    if df.empty: return pd.DataFrame()

    df[class="hl-string">'InvoiceNumber'] = df[class="hl-string">'OBJKY'].str[5:15].str.lstrip(class="hl-string">'0')
    df[class="hl-string">'ActivityName'] = class="hl-string">'Invoice Sent To Customer'
    df[class="hl-string">'EventTime'] = df.apply(lambda row: format_sap_date(row[class="hl-string">'DATUM']) + format_sap_time(row[class="hl-string">'UZEIT']), axis=1)
    df.rename(columns={class="hl-string">'USNAM': class="hl-string">'UserName'}, inplace=True)
    return df[[class="hl-string">'InvoiceNumber', class="hl-string">'ActivityName', class="hl-string">'EventTime', class="hl-string">'UserName']]

def get_payment_due_passed(conn, company_codes):
    options = [fclass="hl-string">"BUKRS IN ('{','.join(company_codes)}') AND BLART IN ('RV', 'RE') AND ZFBDT < '{datetime.now().strftime('%Y%m%d')}'"]
    fields = [{class="hl-string">'FIELDNAME': class="hl-string">'BELNR'}, {class="hl-string">'FIELDNAME': class="hl-string">'KUNNR'}, {class="hl-string">'FIELDNAME': class="hl-string">'ZFBDT'}]
    df = rfc_read_to_df(conn, class="hl-string">'BSID', fields, options)
    if df.empty: return pd.DataFrame()

    df[class="hl-string">'ActivityName'] = class="hl-string">'Payment Due Date Passed'
    df[class="hl-string">'EventTime'] = df[class="hl-string">'ZFBDT'].apply(lambda x: format_sap_date(x) + timedelta(days=1))
    df.rename(columns={class="hl-string">'BELNR': class="hl-string">'InvoiceNumber', class="hl-string">'KUNNR': class="hl-string">'CustomerNumber'}, inplace=True)
    return df[[class="hl-string">'InvoiceNumber', class="hl-string">'ActivityName', class="hl-string">'EventTime', class="hl-string">'CustomerNumber']]

def get_dispute_cases(conn, company_codes, start_date, end_date):
    options = [fclass="hl-string">"COMPANY_CODE IN ('{','.join(company_codes)}') AND CREATED_AT >= '{start_date}'"]
    fields = [{class="hl-string">'FIELDNAME': class="hl-string">'CASE_GUID'}, {class="hl-string">'FIELDNAME': class="hl-string">'CREATED_AT'}, {class="hl-string">'FIELDNAME': class="hl-string">'CREATED_BY'}, {class="hl-string">'FIELDNAME': class="hl-string">'CLOSING_DATE'}, {class="hl-string">'FIELDNAME': class="hl-string">'PROCESSOR'}]
    df_case = rfc_read_to_df(conn, class="hl-string">'UDM_SPS_CASE_ATTR', fields, options)
    if df_case.empty: return pd.DataFrame()

    case_guids = df_case[class="hl-string">'CASE_GUID'].unique().tolist()
    obj_options = [fclass="hl-string">"CASE_GUID IN ('{','.join(case_guids)}')"]
    obj_fields = [{class="hl-string">'FIELDNAME': class="hl-string">'CASE_GUID'}, {class="hl-string">'FIELDNAME': class="hl-string">'OBJECT_ID'}]
    df_obj = rfc_read_to_df(conn, class="hl-string">'UDM_SPS_OBJECT', obj_fields, obj_options)

    df = pd.merge(df_case, df_obj, on=class="hl-string">'CASE_GUID')
    df[class="hl-string">'InvoiceNumber'] = df[class="hl-string">'OBJECT_ID'].str[7:17].str.lstrip(class="hl-string">'0')

    df_registered = df.copy()
    df_registered[class="hl-string">'ActivityName'] = class="hl-string">'Dispute Case Registered'
    df_registered[class="hl-string">'EventTime'] = df_registered[class="hl-string">'CREATED_AT'].apply(format_sap_date)
    df_registered.rename(columns={class="hl-string">'CREATED_BY': class="hl-string">'UserName'}, inplace=True)
    
    df_resolved = df[df[class="hl-string">'CLOSING_DATE'].notna() & (df[class="hl-string">'CLOSING_DATE'] != class="hl-string">'00000000')].copy()
    df_resolved[class="hl-string">'ActivityName'] = class="hl-string">'Dispute Resolved'
    df_resolved[class="hl-string">'EventTime'] = df_resolved[class="hl-string">'CLOSING_DATE'].apply(format_sap_date)
    df_resolved.rename(columns={class="hl-string">'PROCESSOR': class="hl-string">'UserName'}, inplace=True)

    return pd.concat([df_registered[[class="hl-string">'InvoiceNumber', class="hl-string">'ActivityName', class="hl-string">'EventTime', class="hl-string">'UserName']], 
                      df_resolved[[class="hl-string">'InvoiceNumber', class="hl-string">'ActivityName', class="hl-string">'EventTime', class="hl-string">'UserName']]])

def get_dunning(conn, company_codes, start_date, end_date):
    options = [fclass="hl-string">"BUKRS IN ('{','.join(company_codes)}') AND LAUFD >= '{start_date}'"]
    h_fields = [{class="hl-string">'FIELDNAME': class="hl-string">'LAUFD'}, {class="hl-string">'FIELDNAME': class="hl-string">'LAUFI'}, {class="hl-string">'FIELDNAME': class="hl-string">'KOART'}]
    df_h = rfc_read_to_df(conn, class="hl-string">'MHNK', h_fields, options)
    df_h = df_h[df_h[class="hl-string">'KOART'] == class="hl-string">'D'] class=class="hl-string">"hl-comment"># Only customers
    if df_h.empty: return pd.DataFrame()

    d_fields = [{class="hl-string">'FIELDNAME': class="hl-string">'LAUFD'}, {class="hl-string">'FIELDNAME': class="hl-string">'LAUFI'}, {class="hl-string">'FIELDNAME': class="hl-string">'BELNR'}]
    dunning_keys = [fclass="hl-string">"(LAUFD = '{row.LAUFD}' AND LAUFI = '{row.LAUFI}')" for index, row in df_h.iterrows()]
    d_options = [class="hl-string">" OR ".join(dunning_keys[i:i+50]) for i in range(0, len(dunning_keys), 50)]
    df_d_list = [rfc_read_to_df(conn, class="hl-string">'MHND', d_fields, [opt]) for opt in d_options]
    df_d = pd.concat(df_d_list).drop_duplicates()

    df = pd.merge(df_h, df_d, on=[class="hl-string">'LAUFD', class="hl-string">'LAUFI'])
    df[class="hl-string">'ActivityName'] = class="hl-string">'Dunning Procedure Initiated'
    df[class="hl-string">'EventTime'] = df[class="hl-string">'LAUFD'].apply(format_sap_date)
    df.rename(columns={class="hl-string">'BELNR': class="hl-string">'InvoiceNumber'}, inplace=True)
    return df[[class="hl-string">'InvoiceNumber', class="hl-string">'ActivityName', class="hl-string">'EventTime']]

def get_promise_to_pay(conn, company_codes, start_date, end_date):
    options = [fclass="hl-string">"COMPANY_CODE IN ('{','.join(company_codes)}') AND CREATION_DATE >= '{start_date}'"]
    fields = [{class="hl-string">'FIELDNAME': class="hl-string">'INVOICE_REF'}, {class="hl-string">'FIELDNAME': class="hl-string">'CREATION_DATE'}, {class="hl-string">'FIELDNAME': class="hl-string">'CREATION_TIME'}, {class="hl-string">'FIELDNAME': class="hl-string">'CREATED_BY'}]
    df = rfc_read_to_df(conn, class="hl-string">'UDM_P2P_ATTR', fields, options)
    if df.empty: return pd.DataFrame()
    
    df[class="hl-string">'InvoiceNumber'] = df[class="hl-string">'INVOICE_REF'].str[7:17].str.lstrip(class="hl-string">'0')
    df[class="hl-string">'ActivityName'] = class="hl-string">'Promise To Pay Created'
    df[class="hl-string">'EventTime'] = df.apply(lambda row: format_sap_date(row[class="hl-string">'CREATION_DATE']) + format_sap_time(row[class="hl-string">'CREATION_TIME']), axis=1)
    df.rename(columns={class="hl-string">'CREATED_BY': class="hl-string">'UserName'}, inplace=True)
    return df[[class="hl-string">'InvoiceNumber', class="hl-string">'ActivityName', class="hl-string">'EventTime', class="hl-string">'UserName']]

def get_cleared_items(conn, company_codes, start_date, end_date):
    options = [fclass="hl-string">"BUKRS IN ('{','.join(company_codes)}') AND AUGDT >= '{start_date}' AND AUGDT <= '{end_date}' AND BLART IN ('RV', 'RE')"]
    fields = [{class="hl-string">'FIELDNAME': class="hl-string">'BELNR'}, {class="hl-string">'FIELDNAME': class="hl-string">'AUGDT'}, {class="hl-string">'FIELDNAME': class="hl-string">'AUGBL'}, {class="hl-string">'FIELDNAME': class="hl-string">'RSTGR'}, {class="hl-string">'FIELDNAME': class="hl-string">'USNAM'}, {class="hl-string">'FIELDNAME': class="hl-string">'KUNNR'}]
    df = rfc_read_to_df(conn, class="hl-string">'BSAD', fields, options)
    if df.empty: return pd.DataFrame()
    df.rename(columns={class="hl-string">'BELNR': class="hl-string">'InvoiceNumber', class="hl-string">'KUNNR': class="hl-string">'CustomerNumber', class="hl-string">'USNAM': class="hl-string">'UserName'}, inplace=True)
    df[class="hl-string">'EventTime'] = df[class="hl-string">'AUGDT'].apply(format_sap_date)

    class=class="hl-string">"hl-comment"># Write-offs (Reason Code for write-off needs configuration)
    df_woff = df[df[class="hl-string">'RSTGR'].isin([class="hl-string">'[Your Write-Off Reason Code]'])].copy()
    df_woff[class="hl-string">'ActivityName'] = class="hl-string">'Invoice Written Off'

    class=class="hl-string">"hl-comment"># Normal Payments
    df_paid = df[~df[class="hl-string">'RSTGR'].isin([class="hl-string">'[Your Write-Off Reason Code]'])].copy()
    
    df_received = df_paid.copy()
    df_received[class="hl-string">'ActivityName'] = class="hl-string">'Payment Received'

    df_posted = df_paid.copy()
    df_posted[class="hl-string">'ActivityName'] = class="hl-string">'Payment Posted'
    df_posted[class="hl-string">'EventTime'] = df_posted[class="hl-string">'EventTime'] + timedelta(seconds=1) class=class="hl-string">"hl-comment"># Small offset for ordering

    df_settled = df_paid.copy()
    df_settled[class="hl-string">'ActivityName'] = class="hl-string">'Invoice Settled'
    df_settled[class="hl-string">'EventTime'] = df_settled[class="hl-string">'EventTime'] + timedelta(seconds=2)

    return pd.concat([df_received[[class="hl-string">'InvoiceNumber', class="hl-string">'ActivityName', class="hl-string">'EventTime', class="hl-string">'UserName', class="hl-string">'CustomerNumber']], 
                      df_posted[[class="hl-string">'InvoiceNumber', class="hl-string">'ActivityName', class="hl-string">'EventTime', class="hl-string">'UserName', class="hl-string">'CustomerNumber']], 
                      df_settled[[class="hl-string">'InvoiceNumber', class="hl-string">'ActivityName', class="hl-string">'EventTime', class="hl-string">'UserName', class="hl-string">'CustomerNumber']],
                      df_woff[[class="hl-string">'InvoiceNumber', class="hl-string">'ActivityName', class="hl-string">'EventTime', class="hl-string">'UserName', class="hl-string">'CustomerNumber']]])

def get_collection_calls(conn, company_codes, start_date, end_date):
    class=class="hl-string">"hl-comment"># This is a placeholder for a custom activity. 
    class=class="hl-string">"hl-comment"># Assumes a custom Z-table class="hl-string">'ZCOLL_CALLS' exists.
    class=class="hl-string">"hl-comment"># Fields: BELNR, CALL_DATE, CALL_TIME, UNAME, KUNNR
    class=class="hl-string">"hl-comment"># Please adapt table and field names to your specific implementation.
    try:
        options = [fclass="hl-string">"LEFT(BELNR, 4) IN ('{','.join(company_codes)}') AND CALL_DATE >= '{start_date}'"]
        fields = [{class="hl-string">'FIELDNAME': class="hl-string">'BELNR'}, {class="hl-string">'FIELDNAME': class="hl-string">'CALL_DATE'}, {class="hl-string">'FIELDNAME': class="hl-string">'CALL_TIME'}, {class="hl-string">'FIELDNAME': class="hl-string">'UNAME'}]
        df = rfc_read_to_df(conn, class="hl-string">'[Your Custom Table for Call Logs]', fields, options)
        if df.empty: return pd.DataFrame()

        df[class="hl-string">'ActivityName'] = class="hl-string">'Collection Call Made'
        df[class="hl-string">'EventTime'] = df.apply(lambda row: format_sap_date(row[class="hl-string">'CALL_DATE']) + format_sap_time(row[class="hl-string">'CALL_TIME']), axis=1)
        df.rename(columns={class="hl-string">'BELNR': class="hl-string">'InvoiceNumber', class="hl-string">'UNAME': class="hl-string">'UserName'}, inplace=True)
        return df[[class="hl-string">'InvoiceNumber', class="hl-string">'ActivityName', class="hl-string">'EventTime', class="hl-string">'UserName']]
    except Exception as e:
        print(fclass="hl-string">"Could not extract 'Collection Call Made'. This is expected if a custom log table is not used. Error: {e}")
        return pd.DataFrame()

class=class="hl-string">"hl-comment"># --- Main Execution Logic ---
if __name__ == class="hl-string">"__main__":
    class=class="hl-string">"hl-comment"># --- Main Parameters ---
    company_codes = [class="hl-string">'[Your Company Code]'] class=class="hl-string">"hl-comment"># e.g., [class="hl-string">'1000', class="hl-string">'1010']
    start_date = class="hl-string">'[Start Date YYYYMMDD]' class=class="hl-string">"hl-comment"># e.g., class="hl-string">'20230101'
    end_date = class="hl-string">'[End Date YYYYMMDD]' class=class="hl-string">"hl-comment"># e.g., class="hl-string">'20231231'

    all_events = []
    try:
        with Connection(**sap_config) as conn:
            print(class="hl-string">"Connection successful. Starting extraction...")
            
            print(class="hl-string">"1/8: Extracting 'Invoice Generated'...")
            all_events.append(get_invoices_generated(conn, company_codes, start_date, end_date))
            
            print(class="hl-string">"2/8: Extracting 'Invoice Sent To Customer'...")
            all_events.append(get_invoices_sent(conn, company_codes, start_date, end_date))

            print(class="hl-string">"3/8: Extracting 'Payment Due Date Passed'...")
            all_events.append(get_payment_due_passed(conn, company_codes))

            print(class="hl-string">"4/8: Extracting Dispute Cases ('Registered', 'Resolved')...")
            all_events.append(get_dispute_cases(conn, company_codes, start_date, end_date))

            print(class="hl-string">"5/8: Extracting 'Dunning Procedure Initiated'...")
            all_events.append(get_dunning(conn, company_codes, start_date, end_date))

            print(class="hl-string">"6/8: Extracting 'Promise To Pay Created'...")
            all_events.append(get_promise_to_pay(conn, company_codes, start_date, end_date))
            
            print(class="hl-string">"7/8: Extracting Cleared Items ('Payment Received', 'Posted', 'Settled', 'Written Off')...")
            all_events.append(get_cleared_items(conn, company_codes, start_date, end_date))

            print(class="hl-string">"8/8: Extracting 'Collection Call Made' (custom)...")
            all_events.append(get_collection_calls(conn, company_codes, start_date, end_date))

    except Exception as e:
        print(fclass="hl-string">"An error occurred during RFC call: {e}")

    print(class="hl-string">"\nCombining all data...")
    final_df = pd.concat([df for df in all_events if not df.empty])

    if not final_df.empty:
        class=class="hl-string">"hl-comment"># Post-processing
        final_df = final_df.sort_values(by=[class="hl-string">'InvoiceNumber', class="hl-string">'EventTime']).reset_index(drop=True)
        final_df[class="hl-string">'InvoiceAmount'] = pd.to_numeric(final_df[class="hl-string">'InvoiceAmount'], errors=class="hl-string">'coerce')
        final_df[class="hl-string">'PaymentDueDate'] = pd.to_datetime(final_df[class="hl-string">'PaymentDueDate'], errors=class="hl-string">'coerce')

        class=class="hl-string">"hl-comment"># Fill forward attributes for a complete event log
        final_df[class="hl-string">'CustomerNumber'] = final_df.groupby(class="hl-string">'InvoiceNumber')[class="hl-string">'CustomerNumber'].ffill().bfill()
        final_df[class="hl-string">'InvoiceAmount'] = final_df.groupby(class="hl-string">'InvoiceNumber')[class="hl-string">'InvoiceAmount'].ffill().bfill()
        final_df[class="hl-string">'PaymentDueDate'] = final_df.groupby(class="hl-string">'InvoiceNumber')[class="hl-string">'PaymentDueDate'].ffill().bfill()
        
        class=class="hl-string">"hl-comment"># Calculate DaysOverdue
        final_df[class="hl-string">'DaysOverdue'] = (final_df[class="hl-string">'EventTime'].dt.date - final_df[class="hl-string">'PaymentDueDate'].dt.date).apply(lambda x: x.days if pd.notnull(x) and x.days > 0 else 0)

        class=class="hl-string">"hl-comment"># Save to CSV
        output_file = class="hl-string">'collections_event_log.csv'
        final_df.to_csv(output_file, index=False, date_format=class="hl-string">'%Y-%m-%d %H:%M:%S')
        print(fclass="hl-string">"Extraction complete. Event log saved to {output_file}")
    else:
        print(class="hl-string">"Extraction resulted in no data. Please check your parameters and SAP system.")

Steps

Create the ABAP Program: Open the ABAP Editor using transaction code SE38. Enter a name for your new program, for example, Z_PM_CREDIT_COLLECTIONS_EXTRACT, and click 'Create'. Provide a suitable title and set the program type to 'Executable Program'.
Define the Selection Screen: In the program, define a selection screen to allow users to filter the data. Key parameters should include Company Code (S_BUKRS), Document Posting Date (S_BLDAT), and Customer Number (S_KUNNR). This allows for targeted and manageable extractions.
Define the Output Structure: Create a local data type that defines the structure of your final event log. This structure must include InvoiceNumber, ActivityName, EventTime, and other recommended attributes like UserName, CustomerNumber, InvoiceAmount, PaymentDueDate, and DaysOverdue.
Implement Data Selection Logic: Write the ABAP logic to select data for each of the 12 required activities. This involves querying multiple SAP tables such as BKPF, BSEG, BSAD, UDM_DISPUTE, MHNK, FDM_P2P_ATTR, and NAST. Use SELECT statements with UNION ALL or append results to a single internal table corresponding to your defined output structure.
Handle Calculated Events: For activities like 'Payment Due Date Passed', which do not have a direct entry in a table, implement specific logic. This involves selecting open invoices and calculating if the due date has passed relative to the extraction run date. The event timestamp for this should be set to the day after the due date.
Consolidate Data: Ensure the data selected for each activity is correctly transformed and moved into the fields of your final internal table. For example, map BKPF-BLDAT to EventTime for the 'Invoice Generated' activity.
Execute the Program: Save and activate your ABAP program. Execute it via transaction SE38 or create a new transaction code for it. Fill in the selection criteria, such as the date range and company code, and run the program.
Generate the Output File: The program should be designed to write the contents of the final internal table to a flat file. Use ABAP statements like OPEN DATASET, LOOP AT... TRANSFER..., and CLOSE DATASET to create a CSV or tab-delimited file on the SAP application server.
Download the File: Use transaction AL11 to navigate the application server directories and locate your generated file. Alternatively, use transaction CG3Y to download the file from the application server directly to your local computer.
Prepare for Upload: Ensure the downloaded file is formatted correctly as a CSV with a header row matching the required attributes. Verify that the date and time formats are consistent. The file is now ready to be uploaded to the process mining tool.

Configuration

Date Range (S_BLDAT): This parameter filters invoices based on their posting date. It is critical for controlling the volume of data. We recommend extracting data for a period of 3 to 6 months to ensure a manageable dataset size while capturing enough process variations.
Company Code (S_BUKRS): This mandatory parameter restricts the extraction to one or more specific company codes. This is essential for focusing the analysis on a particular legal entity and for performance reasons.
Customer Number (S_KUNNR): An optional parameter to filter the extraction for a specific customer or range of customers. Useful for detailed analysis or testing on a small data sample.
Document Type (S_BLART): An optional filter to include only specific document types that represent customer invoices, such as 'RV' or custom invoice types. This helps exclude irrelevant financial documents from the extraction.
Output File Path: The ABAP program must be configured with a valid directory path on the SAP application server where the user running the report has write permissions. This is typically a shared directory like /usr/sap/trans/tmp/.
Prerequisites: The user executing the program needs authorization to access financial accounting tables (BKPF, BSEG, BSAD), credit management tables (UKM_ITEM), dispute management tables (UDM_DISPUTE), and dunning tables (MHNK). Additionally, ABAP development authorizations are required to create and modify the report.

a Sample Query abap

REPORT Z_PM_CREDIT_COLLECTIONS_EXTRACT.

*&---------------------------------------------------------------------*
*& Data Types for Final Event Log
*&---------------------------------------------------------------------*
TYPES: BEGIN OF ty_event_log,
         invoicenumber  TYPE belnr_d,
         activityname   TYPE string,
         eventtime      TYPE timestamp,
         username       TYPE uname,
         customernumber TYPE kunnr,
         invoiceamount  TYPE wrbtr,
         paymentduedate TYPE dats,
         daysoverdue    TYPE int4,
       END OF ty_event_log.

DATA: lt_event_log TYPE TABLE OF ty_event_log.

*&---------------------------------------------------------------------*
*& Selection Screen
*&---------------------------------------------------------------------*
SELECT-OPTIONS: s_bukrs FOR bkpf-bukrs OBLIGATORY,
                s_bldat FOR bkpf-bldat OBLIGATORY,
                s_kunnr FOR bseg-kunnr,
                s_blart FOR bkpf-blart.

PARAMETERS: p_fpath TYPE string DEFAULT '/usr/sap/trans/tmp/collections_log.csv'.

*&---------------------------------------------------------------------*
*& Main Processing Block
*&---------------------------------------------------------------------*
START-OF-SELECTION.

  " 1. Invoice Generated
  SELECT
    b~belnr AS invoicenumber,
    'Invoice Generated' AS activityname,
    CAST( CONCAT( h~bldat, h~cpuzt ) AS p DECIMALS 0 ) AS eventtime,
    h~usnam AS username,
    b~kunnr AS customernumber,
    b~wrbtr AS invoiceamount,
    b~netdt AS paymentduedate,
    0       AS daysoverdue
  FROM bkpf AS h
  INNER JOIN bseg AS b ON h~bukrs = b~bukrs AND h~belnr = b~belnr AND h~gjahr = b~gjahr
  WHERE h~bukrs IN @s_bukrs
    AND h~bldat IN @s_bldat
    AND b~kunnr IN @s_kunnr
    AND h~blart IN @s_blart
    AND b~koart = 'D' " Customer items
    AND b~shkzg = 'S' " Debit
  INTO CORRESPONDING FIELDS OF TABLE @lt_event_log.

  " 2. Invoice Sent To Customer (Example from NAST)
  SELECT
    a~belnr AS invoicenumber,
    'Invoice Sent To Customer' AS activityname,
    CAST( CONCAT( n~datvr, n~uhrvr ) AS p DECIMALS 0 ) AS eventtime,
    n~usnam AS username,
    a~kunnr AS customernumber,
    a~wrbtr AS invoiceamount,
    a~netdt AS paymentduedate,
    0       AS daysoverdue
  FROM nast AS n
  INNER JOIN vbrk AS v ON n~objky = v~vbeln
  INNER JOIN bseg AS a ON v~xblnr = a~belnr
  WHERE n~kschl = '[Your Output Type]' " e.g., 'RD00'
    AND n~vstat = '1' " Processed successfully
    AND a~bukrs IN @s_bukrs
    AND a~gjahr = SUBSTRING( CONV string( n~datvr ), 0, 4 )
    AND a~kunnr IN @s_kunnr
  APPENDING CORRESPONDING FIELDS OF TABLE @lt_event_log.

  " 3. Payment Due Date Passed (Calculated Event)
  SELECT
    belnr AS invoicenumber,
    'Payment Due Date Passed' AS activityname,
    CAST( CONCAT( netdt + 1, '000000' ) AS p DECIMALS 0 ) AS eventtime,
    ''    AS username, " System event, no user
    kunnr AS customernumber,
    wrbtr AS invoiceamount,
    netdt AS paymentduedate,
    1     AS daysoverdue
  FROM bsid " Open customer items
  WHERE bukrs IN @s_bukrs
    AND bldat IN @s_bldat
    AND kunnr IN @s_kunnr
    AND netdt < sy-datum
  APPENDING CORRESPONDING FIELDS OF TABLE @lt_event_log.

  " 4. Dispute Case Registered
  SELECT
    l~ref_doc_nr AS invoicenumber,
    'Dispute Case Registered' AS activityname,
    CAST( CONCAT( h~created_at_date, h~created_at_time ) AS p DECIMALS 0 ) AS eventtime,
    h~created_by AS username,
    i~kunnr AS customernumber,
    i~wrbtr AS invoiceamount,
    i~netdt AS paymentduedate,
    0       AS daysoverdue
  FROM udm_dispute AS h
  INNER JOIN udm_dispute_link AS l ON h~case_guid = l~case_guid
  INNER JOIN bseg AS i ON l~ref_doc_nr = i~belnr AND l~ref_bukrs = i~bukrs AND l~ref_gjahr = i~gjahr
  WHERE l~ref_bukrs IN @s_bukrs
    AND i~bldat IN @s_bldat
    AND i~kunnr IN @s_kunnr
  APPENDING CORRESPONDING FIELDS OF TABLE @lt_event_log.

  " 5. Dunning Procedure Initiated
  SELECT
    d~belnr AS invoicenumber,
    'Dunning Procedure Initiated' AS activityname,
    CAST( CONCAT( h~laufd, '000000' ) AS p DECIMALS 0 ) AS eventtime,
    h~usnam AS username,
    d~kunnr AS customernumber,
    d~swnet AS invoiceamount, " Dunning amount
    b~netdt AS paymentduedate,
    d~vzsk1 AS daysoverdue
  FROM mhnk AS h
  INNER JOIN mhnd AS d ON h~laufd = d~laufd AND h~laufi = d~laufi
  INNER JOIN bseg AS b ON d~belnr = b~belnr AND d~bukrs = b~bukrs AND d~gjahr = b~gjahr
  WHERE d~bukrs IN @s_bukrs
    AND b~bldat IN @s_bldat
    AND d~kunnr IN @s_kunnr
    AND d~mslev = '1' " First dunning level
  APPENDING CORRESPONDING FIELDS OF TABLE @lt_event_log.

  " 6. Collection Call Made (Assuming logged in FDM_COLL_IN contact history)
  SELECT
    i~belnr AS invoicenumber,
    'Collection Call Made' AS activityname,
    CAST( CONCAT( c~contact_date, c~contact_time ) AS p DECIMALS 0 ) AS eventtime,
    c~processor AS username,
    c~kunnr AS customernumber,
    i~wrbtr AS invoiceamount,
    i~netdt AS paymentduedate,
    0 AS daysoverdue
  FROM fdm_coll_in AS c
  INNER JOIN bseg AS i ON c~kunnr = i~kunnr AND c~bukrs = i~bukrs
  WHERE c~bukrs IN @s_bukrs
    AND i~bldat IN @s_bldat
    AND c~kunnr IN @s_kunnr
    AND c~result = '[Your Call Result Code]'
  APPENDING CORRESPONDING FIELDS OF TABLE @lt_event_log.

  " 7. Promise To Pay Created
  SELECT
    s~belnr AS invoicenumber,
    'Promise To Pay Created' AS activityname,
    CAST( CONCAT( h~creation_date, h~creation_time ) AS p DECIMALS 0 ) AS eventtime,
    h~created_by AS username,
    s~kunnr AS customernumber,
    s~dmbtr AS invoiceamount,
    s~netdt AS paymentduedate,
    0 AS daysoverdue
  FROM fdm_p2p_attr AS h
  INNER JOIN bseg AS s ON h~opbel = s~belnr AND h~opbuk = s~bukrs AND h~opgjr = s~gjahr
  WHERE h~opbuk IN @s_bukrs
    AND s~bldat IN @s_bldat
    AND s~kunnr IN @s_kunnr
  APPENDING CORRESPONDING FIELDS OF TABLE @lt_event_log.

  " 8. Payment Received, 9. Payment Posted, 11. Invoice Settled
  SELECT
    belnr AS invoicenumber,
    'Invoice Settled' AS activityname,
    CAST( CONCAT( augdt, '000000' ) AS p DECIMALS 0 ) AS eventtime,
    augbl AS username, " Often batch user ID in clearing doc header
    kunnr AS customernumber,
    wrbtr AS invoiceamount,
    netdt AS paymentduedate,
    augdt - netdt AS daysoverdue
  FROM bsad " Cleared customer items
  WHERE bukrs IN @s_bukrs
    AND bldat IN @s_bldat
    AND kunnr IN @s_kunnr
    AND augdt <> '00000000'
    AND rstgr = '' " Exclude write-offs
  APPENDING CORRESPONDING FIELDS OF TABLE @lt_event_log.

  " 10. Dispute Resolved
  SELECT
    l~ref_doc_nr AS invoicenumber,
    'Dispute Resolved' AS activityname,
    CAST( CONCAT( h~closing_date, h~closing_time ) AS p DECIMALS 0 ) AS eventtime,
    h~closed_by AS username,
    i~kunnr AS customernumber,
    i~wrbtr AS invoiceamount,
    i~netdt AS paymentduedate,
    0 AS daysoverdue
  FROM udm_dispute AS h
  INNER JOIN udm_dispute_link AS l ON h~case_guid = l~case_guid
  INNER JOIN bseg AS i ON l~ref_doc_nr = i~belnr AND l~ref_bukrs = i~bukrs AND l~ref_gjahr = i~gjahr
  WHERE l~ref_bukrs IN @s_bukrs
    AND i~bldat IN @s_bldat
    AND i~kunnr IN @s_kunnr
    AND h~closing_date <> '00000000'
  APPENDING CORRESPONDING FIELDS OF TABLE @lt_event_log.

  " 12. Invoice Written Off
  SELECT
    belnr AS invoicenumber,
    'Invoice Written Off' AS activityname,
    CAST( CONCAT( augdt, '000000' ) AS p DECIMALS 0 ) AS eventtime,
    augbl AS username,
    kunnr AS customernumber,
    wrbtr AS invoiceamount,
    netdt AS paymentduedate,
    augdt - netdt AS daysoverdue
  FROM bsad " Cleared customer items
  WHERE bukrs IN @s_bukrs
    AND bldat IN @s_bldat
    AND kunnr IN @s_kunnr
    AND rstgr <> '' " Reason code exists for write-off
  APPENDING CORRESPONDING FIELDS OF TABLE @lt_event_log.

  " Output to file
  DATA: lv_string TYPE string.
  OPEN DATASET p_fpath FOR OUTPUT IN TEXT MODE ENCODING UTF-8.
  IF sy-subrc = 0.
    " Header
    lv_string = 'InvoiceNumber,ActivityName,EventTime,UserName,CustomerNumber,InvoiceAmount,PaymentDueDate,DaysOverdue'.
    TRANSFER lv_string TO p_fpath.
    " Data
    LOOP AT lt_event_log ASSIGNING FIELD-SYMBOL(<fs_event>).
      CONCATENATE <fs_event>-invoicenumber
                  <fs_event>-activityname
                  <fs_event>-eventtime
                  <fs_event>-username
                  <fs_event>-customernumber
                  <fs_event>-invoiceamount
                  <fs_event>-paymentduedate
                  <fs_event>-daysoverdue
      INTO lv_string SEPARATED BY ','.
      TRANSFER lv_string TO p_fpath.
    ENDLOOP.
    CLOSE DATASET p_fpath.
  ELSE.
    MESSAGE 'Error opening file.' TYPE 'E'.
  ENDIF.

  WRITE: 'Extraction complete. File created at:', p_fpath.

Your Credit Management & Collections Data Template

Your Credit Management & Collections Data Template

Credit Management & Collections Attributes

Credit Management & Collections Activities

Extraction Guides

SAP S/4HANA Credit Management CDS View Extraction

Steps

Configuration

a Sample Query sql

Direct SAP HANA DB Query on Credit Management Tables

Steps

Configuration

a Sample Query sql

Python Script Extraction via SAP RFC and BAPIs

Steps

Configuration

a Sample Query python

Custom ABAP Report for Credit & Collections Data

Steps

Configuration

a Sample Query abap

Ready to Get Started?

Unlock Better Cash Flow: Optimize Credit Management & Collections Today

Your Credit Management & Collections Data Template

Your Credit Management & Collections Data Template

Credit Management & Collections Attributes

Credit Management & Collections Activities

Extraction Guides

SAP S/4HANA Credit Management CDS View Extraction

Steps

Configuration

a Sample Query sql

Direct SAP HANA DB Query on Credit Management Tables

Steps

Configuration

a Sample Query sql

Python Script Extraction via SAP RFC and BAPIs

Steps

Configuration

a Sample Query python

Custom ABAP Report for Credit & Collections Data

Steps

Configuration

a Sample Query abap

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Ready to Get Started?

Unlock Better Cash Flow: Optimize Credit Management & Collections Today