Your Returns & Refund Processing Data Template

Event Time, or the timestamp, records the exact date and time that an activity took place. Each activity in the event log has a corresponding timestamp, which provides the chronological order of events.

This attribute is critical for all time-based process mining analysis. It is used to calculate cycle times between activities, identify waiting times and bottlenecks, measure overall case duration, and check for compliance with service level agreements (SLAs). The accuracy of the timestamps directly impacts the reliability of any performance analysis.

This timestamp is essential for calculating all duration-based metrics, such as cycle times and waiting times, which are fundamental to performance analysis.

Corresponds to creation or modification date fields in various tables, such as 'SalesTable.createdDateTime' for order creation or 'WMSJournalTrans.createdDateTime' for warehouse journals.

The Return Case ID serves as the primary identifier for each unique return process instance. It links all activities associated with a specific customer return or refund request, from the initial creation of the return order to its final closure.

In process analysis, this ID is fundamental for reconstructing the end-to-end journey of each return. It allows for tracking the complete lifecycle, measuring total cycle times, and analyzing variations between different cases. All events, data, and metrics are aggregated and correlated using this identifier.

This is the essential case identifier that connects all process steps, making it possible to trace and analyze each return from start to finish.

This is typically the Return Material Authorization (RMA) number or the Sales Order number of type 'Returned Order' in the 'Sales and marketing' module. Found in tables like 'SalesTable' where 'SalesType' is 'Returned Order'.

This attribute describes a specific step or event in the return and refund lifecycle, such as 'Return Order Created', 'Item Received', or 'Credit Note Posted'. Each activity represents a distinct point in the process that is recorded in the system.

Analyzing the sequence and frequency of these activities is the core of process mining. It allows for the visualization of process maps, identification of bottlenecks between steps, and discovery of common and uncommon process variants. The set of activities defines the scope of the process being analyzed.

It defines the steps of the process, allowing for the visualization of the process flow and the identification of bottlenecks, rework, and deviations.

This is a conceptual attribute derived from system events. It can be generated by mapping status changes in tables like 'SalesTable' and 'WMSJournalTable' or specific event logs to user-friendly names.

The Disposition Code is assigned during the quality inspection of a returned item. It dictates the subsequent step in the process, such as 'Credit', 'Replace', 'Scrap', or 'Return to Customer'.

This attribute is a critical decision point in the returns process. Analyzing by disposition code allows businesses to understand the outcomes of returns, track the financial impact of scrapping items, and evaluate the efficiency of different resolution paths, like replacement versus refund.

This code determines the path a return case will take after inspection, making it crucial for analyzing process variants and their business outcomes.

This is a key field in the quality management module. It is associated with the Quality Order or Inspection Order processing.

The Product ID, often the Stock Keeping Unit (SKU), identifies the specific item that is being returned by the customer. Each return order line is associated with a Product ID.

Analyzing returns by product is essential for identifying items with high return rates. This can signal quality control problems, inaccurate product descriptions, or manufacturing defects. This analysis helps prioritize product-related investigations and improvements.

Enables analysis of returns on a per-product basis, helping to identify items with quality issues or high return volumes.

This corresponds to the 'ItemId' field in the 'SalesLine' table for the return order.

This attribute identifies the individual user responsible for executing a process step. It could be the warehouse worker who received the item, the quality inspector, or the finance clerk who posted the credit note.

Analyzing the process by user helps in understanding workload distribution, identifying top performers, and detecting potential training needs. It can also be used to investigate cases handled by specific individuals or teams and ensure proper segregation of duties.

It allows for analysis of workload distribution, performance by individual or team, and identification of training or resource allocation opportunities.

Found in 'created by' or 'modified by' fields on transaction records, such as 'SalesTable.createdBy' or linked user IDs in journal tables.

This attribute specifies the channel used by the customer to start the return process, for example, 'Online Portal', 'In-Store', 'Customer Service Call', or 'Mail'.

Segmenting the process analysis by return channel helps in evaluating the performance and efficiency of each channel. A business can compare cycle times, costs, and customer satisfaction across channels to identify best practices and areas for investment or improvement. This is key for the 'Return Channel Utilization Performance' dashboard.

It allows for performance comparison between different return channels, helping to optimize the most efficient and cost-effective ones.

This information might be stored on the return order header ('SalesTable') or derived from the user who created the order. It may require custom logic or a dedicated field.

The Return Reason Code captures the customer's stated reason for the return, such as 'Defective Item', 'Wrong Size', 'Not as Described', or 'No Longer Needed'. This information is typically collected when the return is initiated.

Analyzing return reasons is vital for root cause analysis. It helps businesses identify product quality issues, problems with product descriptions, or logistical errors. Insights from this data can drive improvements in product design, marketing, and supply chain operations to reduce future returns.

Provides critical insight into why returns are happening, enabling root cause analysis to reduce return rates and improve customer satisfaction.

This is typically stored on the return order line level. Look for reason code fields in the 'SalesLine' table for return orders.

This attribute is the final, confirmed amount that was refunded to the customer. This value is recorded when the credit note is created and posted.

This is a critical attribute for financial analysis and is directly used in the 'Refund Amount Discrepancy Analysis' dashboard and the 'Refund Amount Accuracy Rate' KPI. Analyzing this data helps to understand the financial impact of returns and any adjustments made during the process.

This represents the actual financial impact of the return and is crucial for calculating refund accuracy and understanding financial outcomes.

This value can be found in the posted credit note transaction details. It is related to the 'CustTrans' and 'CustInvoiceJour' tables for the credit note.

When a refund is processed, a financial document known as a credit note or credit memo is generated. This attribute stores the unique ID of that document.

This ID provides a direct link from the operational return process to the financial records in the accounting system. It is useful for auditing purposes and for deep dives into financial discrepancies, allowing an analyst to trace a return case all the way to the specific financial transaction that settled it.

It links the operational return process to the corresponding financial transaction, which is crucial for auditing and financial reconciliation.

The credit note number is typically found in the 'InvoiceId' field of the 'CustInvoiceJour' table, where the transaction type is 'Credit note'. This can be linked back to the return order.

The Customer ID is the unique identifier for the customer account associated with the return. This links the return transaction back to a specific customer in the CRM or customer database.

Analyzing returns by customer allows for the identification of customers with unusually high return activity, which might indicate fraudulent behavior or chronic dissatisfaction. It can also be used to segment customers, for example, to offer premium return services to high-value customers.

It links the return process to a specific customer, enabling customer-level analysis and identification of return patterns or potential fraud.

This is the 'CustAccount' field in the 'SalesTable' for the return order.

The End Time represents the completion timestamp for an activity. While StartTime marks the beginning, EndTime marks the conclusion, allowing for the calculation of the processing time for that specific task.

This attribute is crucial for detailed performance analysis, especially for tasks with a measurable duration, like 'Item Inspection'. By comparing the StartTime and EndTime, analysts can precisely measure the active processing time of tasks, distinguishing it from the waiting time between tasks. This helps pinpoint inefficiencies within specific activities, not just between them.

It enables the calculation of active processing time for individual activities, helping to differentiate between waiting time and actual work time.

This often needs to be derived. For example, it could be the 'modifiedDateTime' of a status change that concludes an activity, or it could be the StartTime of the subsequent activity.

This is a calculated boolean attribute that indicates if a return meets all the criteria defined in the company's return policy. This could be based on factors like the return window, item condition, or reason for return.

This attribute directly supports the 'Return Approval Compliance Overview' dashboard and 'Compliant Return Approval Rate' KPI. It allows the business to quantify policy compliance, identify cases that are approved as exceptions, and analyze the reasons and frequency of such exceptions. This is vital for governance and cost control.

It directly measures compliance with business rules, helping to identify and reduce non-compliant return approvals that can lead to revenue loss.

This is a derived attribute. The logic would need to be built by comparing attributes of the return (e.g., return date vs. purchase date, return reason) against predefined business rules.

This attribute records the date and time when the data was last extracted from the source system and updated in the process mining tool. It provides a reference point for the freshness of the data being analyzed.

Knowing the last data update time is important for understanding the timeliness of the analysis. It helps users interpret the dashboards and KPIs correctly, knowing if they are looking at real-time data or a snapshot from a specific point in time. This is key for operational monitoring.

Indicates the freshness of the data, ensuring that analysts are aware of how current their process insights are.

This is a metadata attribute generated and stored during the data ingestion pipeline, typically representing the timestamp of the ETL job completion.

This attribute defines the Service Level Agreement (SLA) deadline for resolving a return case. It is the date by which the customer is expected to have a final resolution, such as a posted refund or a shipped replacement.

This target date is essential for performance monitoring against service commitments. It is used to calculate the 'Resolution SLA Adherence Rate' KPI and power the 'Refund Resolution SLA Performance' dashboard. Comparing this date with the actual completion date of the process allows the business to identify SLA breaches and proactively manage aging cases.

It is the benchmark against which process performance is measured, enabling the tracking of SLA compliance and identification of late cases.

This may not be a standard field. It is often calculated based on the return creation date plus a predefined SLA period (e.g., 14 days). It might be stored in a custom field.

This attribute represents the initial refund amount as requested or expected at the beginning of the return process. It is typically based on the original purchase price of the returned items.

This value serves as a baseline for the 'Refund Amount Discrepancy Analysis'. By comparing the requested amount to the actual amount refunded, the business can identify discrepancies caused by restocking fees, partial refunds for damaged goods, or other adjustments. This helps in monitoring financial accuracy and policy adherence.

It serves as a baseline to measure financial accuracy by comparing it against the actual refund amount processed.

This is typically the line amount or total amount from the original sales order line being returned, found in 'SalesLine.LineAmount'.

This attribute indicates the current status of the return order header, such as 'Open', 'Invoiced', or 'Canceled'. It provides a high-level view of where the case is in its lifecycle.

While activities provide granular process steps, the overall status is useful for filtering and segmenting cases. For example, an analyst might want to focus only on 'Open' cases to understand current workload, or analyze the process flow of cases that ultimately get 'Canceled'.

Provides a high-level summary of the case's state, which is useful for filtering cases and understanding outcomes like cancellations.

This information is located in the 'SalesStatus' or 'DocumentStatus' field of the 'SalesTable'.

This attribute classifies the return case based on the resolution type sought by the customer or offered by the business. Common types include a monetary 'Refund', an exchange for a 'Replacement' item, or 'Repair'.

This categorization is useful for analyzing different process paths. The process for issuing a refund is significantly different from the process for shipping a replacement item. Segmenting by Return Type allows for a more accurate analysis of the cycle times and bottlenecks specific to each resolution path.

It allows for the segmentation of analysis based on the intended outcome, as refund and replacement processes have different steps and cycle times.

This may be a custom field on the return order header or derived based on the disposition code or subsequent transactions like the creation of a replacement sales order.

This calculated attribute provides a simple status of SLA compliance, typically 'On Time' or 'Late'. It is determined by comparing the timestamp of the final activity (e.g., 'Return Order Closed') with the 'RefundSlaTargetDate'.

This attribute simplifies performance reporting on dashboards like 'Refund Resolution SLA Performance'. Instead of requiring users to compare dates, it provides a direct and easily understandable status. This allows for quick filtering and aggregation to calculate the overall 'Resolution SLA Adherence Rate'.

Provides a simple, at-a-glance indicator of SLA compliance, making it easy to filter for late cases and analyze the root causes of delays.

This is a derived attribute, calculated by comparing the timestamp of the final resolution activity against the 'RefundSlaTargetDate' attribute.

This attribute identifies the source information system where the data originated. In this context, it will primarily be 'Microsoft Dynamics 365'.

In larger organizations, a process might span multiple systems. Specifying the source system for each event is crucial for data governance, troubleshooting data extraction issues, and understanding the technological landscape of the process. It confirms the origin of the data being analyzed.

It provides crucial context about data origin, which is essential for data governance, validation, and understanding the system landscape of the process.

This is typically a static value added during the data extraction, transformation, and loading (ETL) process to label the dataset's origin.

This attribute identifies the specific physical warehouse or return center that processes the returned item. Different locations may have different processes, resources, or performance levels.

Analyzing the process by warehouse allows for performance benchmarking between locations. It can help identify which facilities are most efficient at processing returns, highlight regional bottlenecks, and inform decisions about resource allocation and process standardization across the logistics network.

Enables performance comparison between different warehouses or return centers, helping to identify regional bottlenecks or best practices.

This information is stored in the 'InventLocationId' field on inventory-related transactions, such as the Arrival Journal ('WMSJournalTable') or on the 'SalesLine'.