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Your Inventory Management Data Template

Microsoft Dynamics 365

Inventory Management Attributes (22) Inventory Management Activities (15) Extraction Guides (4)

Your Inventory Management Data Template

This template provides a clear roadmap for collecting the data you need to analyze your inventory management process. It outlines essential attributes, key activities to track, and practical guidance for data extraction. Use this resource to ensure you capture all critical information for effective process mining.

Recommended attributes to collect
Key activities to track
Extraction guidance

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

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Inventory Management Attributes

These are the recommended data fields to include in your event log for comprehensive analysis of your inventory management process.

5 Required 6 Recommended 11 Optional

	Name	Description
	Activity Name ActivityName	The name of the specific inventory management event that occurred, such as 'Goods Receipt Recorded' or 'Picking Completed'.
Description This attribute describes the business activity that was performed at a point in time for a given inventory batch. It represents a single step in the overall inventory management process, forming the sequence of events that constitutes the process flow. Analyzing the sequence of activities is the core of process mining. It helps visualize the process map, identify bottlenecks between activities, measure transition times, and discover non-standard process variations. Each value corresponds to a key milestone in the inventory lifecycle. Why it matters This attribute defines the steps in the process, making it possible to visualize the process map, analyze variants, and identify bottlenecks. Where to get Derived from transaction types, status changes, or work order types within tables like InventTrans, WHSWorkTable, and WHSRFMenuItemTable. Examples Goods Receipt RecordedPut-away CompletedInventory Discrepancy AdjustedPicking CompletedStock Scrapped
	Event Time EventTime	The precise timestamp indicating when the inventory activity occurred.
Description This attribute captures the date and time that an activity was recorded in the system. It provides the chronological order of events for each inventory batch, which is fundamental for process mining. Timestamps are used to calculate cycle times, durations, and waiting times between activities. This allows for performance analysis, bottleneck identification, and monitoring adherence to service level agreements. Accurate and complete timestamps are critical for a meaningful process analysis. Why it matters The timestamp is crucial for ordering events, calculating cycle times and durations, and analyzing process performance over time. Where to get Typically found in transaction tables like InventTrans (e.g., DATEPHYSICAL, CREATEDDATETIME) or work order tables like WHSWorkTable (e.g., CREATEDDATETIME, MODIFIEDDATETIME). Examples 2023-10-26T09:00:00Z2023-10-26T11:34:15Z2023-10-27T14:21:05Z
	Inventory Batch/Lot InventoryBatchLot	The unique identifier for a specific batch or lot of an inventory item, serving as the primary case identifier.
Description The Inventory Batch or Lot number serves as the primary case identifier, grouping all activities related to a specific quantity of a product. This enables tracking the complete lifecycle of a distinct stock quantity, from its receipt into inventory through its various movements and eventual issue or consumption. In process analysis, this attribute allows for a holistic view of the journey of each inventory batch. By following a single batch, analysts can measure end-to-end cycle times, identify common pathways, and uncover deviations from standard operating procedures for specific items or suppliers. Why it matters This is the essential key for tracing the end-to-end lifecycle of an inventory quantity, enabling variant analysis and performance monitoring for each specific batch. Where to get This identifier is typically found in inventory transaction tables like InventTrans and related tables managing batch details like InventBatch. Examples BCH003451LOT2024-A55B001-RAW-STEEL
	Last Data Update LastDataUpdate	Timestamp indicating the last time the data for this process was refreshed from the source system.
Description This attribute records the date and time of the most recent data extraction or refresh. It provides transparency into the freshness of the data being analyzed. This is important for users to understand how current the process analysis is. It helps manage expectations about the data's timeliness and is a key piece of metadata for any report or dashboard, ensuring that decisions are made based on an understood timeframe. Why it matters Informs users about the timeliness of the data, ensuring they are aware of how up-to-date the analysis is. Where to get This is a metadata field generated during the data ingestion pipeline, recording the timestamp of the extraction job. Examples 2024-01-20T05:00:00Z2024-01-21T05:00:00Z
	Source System SourceSystem	Identifies the system from which the data originated, for example, 'Dynamics 365 F&O'.
Description This attribute specifies the source application or platform that generated the event data. In a modern enterprise, data may come from multiple systems, such as an ERP, a Warehouse Management System (WMS), or a Manufacturing Execution System (MES). While often a static value in a single-system analysis, it becomes critical when combining data from multiple sources. It helps ensure data integrity, troubleshoot integration issues, and understand how different systems contribute to the overall process. Why it matters Provides essential context about data origin, which is crucial for data governance and when analyzing processes that span multiple integrated systems. Where to get This is typically a static value added during the data extraction process to label the dataset's origin. Examples Dynamics 365 F&OD365 SCMMicrosoft Dynamics AX
	Event End Time EventEndTime	The timestamp marking the completion of an activity, used for calculating its duration.
Description While many events in inventory management are instantaneous, some activities have a distinct start and end time, such as a quality inspection or a put-away task. This attribute captures the completion time of such activities. Having both a start and end time enables the calculation of processing time for individual activities. This is crucial for analyzing resource efficiency, identifying activities that consume the most time, and understanding the true cost of process steps. Why it matters Enables the calculation of precise activity durations, which helps in identifying inefficient steps and analyzing resource utilization. Where to get May be derived from a separate status update timestamp, for example, the modification datetime on a work order line in WHSWorkTable when its status changes to 'Closed'. Examples 2023-10-26T09:15:00Z2023-10-26T12:01:45Z2023-10-27T15:00:00Z
	Item ID ItemId	The unique identifier for the product or material being handled, also known as SKU.
Description This attribute is the unique code for a specific product, material, or Stock Keeping Unit (SKU). It links every inventory activity to the item being moved, counted, or inspected. Analyzing the process by Item ID allows for deep dives into product-specific behavior. It helps answer questions like which products have the longest put-away times, which are most frequently adjusted, or which are most often scrapped. This is fundamental for inventory optimization and product lifecycle management. Why it matters Links inventory activities to specific products (SKUs), enabling analysis of product-specific process performance, costs, and issues. Where to get A primary key found in most inventory-related tables, such as InventTrans (field: ITEMID), InventBatch, and InventTable. Examples A0001HW-1024RAW-STL-P01
	Quantity Quantity	The quantity of the item involved in the transaction, in the base unit of measure.
Description This attribute represents the number of units of an item affected by an inventory activity. It can be positive for receipts and positive adjustments, or negative for issues, scraps, and negative adjustments. Analyzing quantity is fundamental to understanding the scale and impact of inventory processes. It is used in nearly every dashboard and KPI, from calculating the volume of scrapped goods to measuring the size of inventory discrepancies. It helps prioritize issues based on their material impact. Why it matters Quantifies the physical volume of items in each transaction, which is essential for measuring business impact, throughput, and discrepancy sizes. Where to get Found in core transaction tables like InventTrans (field: QTY). Examples 100-2510500
	Reason Code ReasonCode	A code that explains the reason for a specific transaction, such as an inventory adjustment or a return.
Description Reason codes provide qualitative context for why an activity occurred. They are commonly used for inventory adjustments, stock movements, returns, and scrapping to categorize the underlying business driver. Analyzing by reason code is essential for root cause analysis. It helps uncover why inventory discrepancies happen (e.g., 'Damaged Goods', 'Data Entry Error'), why stock is moved internally (e.g., 'Replenishment', 'Consolidation'), or why items are returned. This information is critical for process improvement initiatives. Why it matters Explains the 'why' behind an event, enabling powerful root cause analysis for issues like inventory adjustments, returns, or scraps. Where to get Found in various tables depending on the transaction, for example, InventJournalTrans (field: REASONREFRECID) or through custom configurations. Examples RC-DMGFOUNDCYCLECOUNT
	User ID UserId	The identifier of the user who performed or is responsible for the activity.
Description This attribute records the user ID associated with an inventory transaction, such as the warehouse worker who completed a picking task or the inventory controller who posted an adjustment. Analyzing activities by user helps identify training needs, recognize high-performing individuals or teams, and investigate the root causes of manual interventions or errors. It is essential for dashboards like 'Manual Inventory Adjustment Drivers' to understand who is making changes and why. Why it matters Tracks user involvement, which is key for analyzing performance, identifying training opportunities, and understanding the drivers of manual adjustments. Where to get Found in various tables, often as a createdBy or modifiedBy field. For example, WHSWorkTable has a USERID field. Examples d.smithj.doewarehouse.worker1
	Warehouse ID WarehouseId	The identifier for the warehouse or physical site where the inventory activity took place.
Description This attribute specifies the warehouse where the inventory is located or where the activity occurred. It provides the geographical or organizational context for each event. Segmenting process analysis by warehouse is crucial for comparing performance across different sites. It helps identify which warehouses are most efficient, which have the highest discrepancy rates, and where bottlenecks are most severe. This supports targeted operational improvements and resource allocation. Why it matters Allows for performance comparison and bottleneck analysis across different physical locations or warehouses. Where to get Found in inventory dimension fields associated with transaction tables. Look for INVENTDIMID in InventTrans, which links to InventDim table where INVENTLOCATIONID is stored. Examples WH-MAINSITE-02DC-WEST
	Counting Journal ID CountingJournalId	The unique identifier for an inventory counting journal.
Description An inventory counting journal is used to record the results of a physical inventory count, such as a cycle count or annual stocktake. This ID groups all the count entries and the resulting adjustments for a specific counting event. Analyzing by Counting Journal ID provides context for inventory adjustment activities. It helps differentiate adjustments resulting from planned counts versus ad-hoc corrections, which is important for understanding the drivers of inventory inaccuracy. Why it matters Groups inventory counts and their resulting adjustments, providing context for why a discrepancy was identified and corrected. Where to get The JOURNALID from the InventJournalTable where the journal type is 'Counting'. Examples ICJ-00561ICJ-00562ICJ-00563
	Is Discrepancy Adjustment IsDiscrepancyAdjustment	A boolean flag that is true if the activity is an 'Inventory Discrepancy Adjusted' event.
Description This calculated attribute is a simple flag (true or false) that identifies activities related to manual inventory adjustments. It simplifies filtering and aggregation when analyzing inventory accuracy. Using this flag makes it easy to calculate KPIs like the 'Inventory Discrepancy Rate' or 'Manual Adjustment Rate'. It allows analysts to quickly isolate these non-standard events to investigate their root causes without complex filtering on the activity name. Why it matters Simplifies the analysis and calculation of KPIs related to inventory accuracy by flagging all discrepancy-related events. Where to get Calculated field: TRUE if ActivityName is 'Inventory Discrepancy Adjusted', otherwise FALSE. Examples truefalse
	Item Value ItemValue	The monetary value of the inventory involved in the transaction.
Description This attribute represents the calculated financial value of the quantity moved or adjusted in a transaction, typically based on the item's current cost price. It translates physical inventory movements into financial impact. Analyzing by item value is crucial for financial reporting and business impact assessment. It is used to calculate KPIs like the 'Scrapped Inventory Value Ratio' and helps prioritize process improvement efforts on activities that have the highest financial consequences, such as high-value discrepancies or scrap events. Why it matters Translates inventory quantities into financial impact, which is critical for prioritizing issues and calculating financial KPIs. Where to get Calculated by multiplying the quantity from InventTrans (QTY) by the item cost from InventTableModule or a related cost table. The posted value might be found in InventTrans.COSTAMOUNTPOSTED. Examples 1500.00-375.5025000.75
	Location ID LocationId	The specific storage location within a warehouse, such as a bin, rack, or aisle.
Description This attribute identifies the precise location within a warehouse where inventory is stored or where an activity is performed. It provides a granular level of detail for analyzing internal warehouse movements. Analyzing by Location ID helps optimize warehouse layout and flow. It can reveal inefficient travel paths, identify congested areas, and pinpoint locations prone to picking errors or inventory discrepancies. This is vital for the 'Warehouse Activity Flow Analysis' and 'Internal Stock Movement Overview' dashboards. Why it matters Provides granular detail for optimizing warehouse layout, analyzing internal movements, and identifying problematic storage areas. Where to get Found in the InventDim table (field: WMSLOCATIONID), which is linked via INVENTDIMID from transaction tables. Examples A1-R2-S3-B4RECV-DOCK-01PACK-STN-5
	Movement Journal ID MovementJournalId	The unique identifier for an inventory movement journal.
Description An inventory movement journal is used to record internal stock transfers that are not part of a formal put-away or picking process, such as moving items between locations for consolidation or replenishment. This ID groups the lines within a single movement transaction. Analyzing by Movement Journal ID provides context for 'Stock Moved Internally' activities. It helps to understand the scope and reason for these movements, which can highlight inefficiencies in warehouse layout or replenishment strategies if they occur too frequently. Why it matters Provides context for internal stock movements, helping to analyze the frequency and reasons for non-standard inventory transfers. Where to get The JOURNALID from the InventJournalTable where the journal type is 'Movement'. Examples IMJ-00945IMJ-00946IMJ-00947
	Picking To Packing Time PickingToPackingTime	The total time taken from when picking is initiated to when packing is completed for an order.
Description This KPI measures the cycle time within the outbound fulfillment process, specifically the duration between the 'Picking Initiated' or 'Picking Completed' activity and the 'Packing Completed' activity. This can be calculated at a case level if the case represents a sales order line, or between related activities. This metric is vital for assessing the efficiency of the pick-and-pack operations. A shorter time indicates a more streamlined fulfillment process, leading to faster order shipment and higher customer satisfaction. It helps pinpoint bottlenecks within the outbound workflow. Why it matters Measures the efficiency of the core order fulfillment process, directly impacting shipment speed and customer satisfaction. Where to get Calculated by finding the time difference between the timestamp of 'Packing Completed' and 'Picking Initiated' activities for the same order or work ID. Examples 25 minutes1 hour 5 minutes48 minutes
	Processing Time ProcessingTime	The duration of an individual activity, calculated from its start and end times.
Description This metric represents the actual time spent performing a specific activity. It is calculated as the difference between the Event End Time and the Event Start Time for a single event. Analyzing processing time is key to identifying which steps in the process are the most time-consuming. This helps focus optimization efforts on the activities with the longest durations, improving overall efficiency and resource utilization. Why it matters Measures the duration of individual activities, helping to pinpoint which specific process steps are creating delays. Where to get Calculated field: EventEndTime - EventTime. Examples 15 minutes2 hours 30 minutes45 seconds
	Put-away Cycle Time PutAwayCycleTime	The total time from goods receipt to when the items are fully put away in their final storage location.
Description This KPI measures the duration from the 'Goods Receipt Recorded' activity to the 'Put-away Completed' activity for a specific inventory batch. It is a case-level metric that quantifies the efficiency of the receiving and put-away process. Monitoring this cycle time is crucial for identifying bottlenecks in the inbound logistics flow. High values can indicate issues with warehouse capacity, resource availability, or process inefficiencies, all of which delay the availability of stock for fulfillment. Why it matters Measures the efficiency of the entire inbound receiving process, directly impacting how quickly new inventory becomes available. Where to get Calculated at the case level by finding the time difference between the timestamp of 'Put-away Completed' and 'Goods Receipt Recorded' for the same InventoryBatchLot. Examples 4 hours 15 minutes1 day 2 hours35 minutes
	Quality Order ID QualityOrderId	The unique identifier for a quality inspection order.
Description When goods require inspection upon receipt or at another process step, a quality order is generated. This attribute is the unique identifier for that order, linking all related quality activities like creation, testing, and pass/fail validation. Tracking by Quality Order ID helps analyze the efficiency and outcomes of the quality control process. It is essential for dashboards like 'Quality Inspection Performance' to measure the lead time from creation to completion and to identify bottlenecks in the inspection workflow. Why it matters Links quality-related activities together, enabling analysis of the quality inspection process duration and outcomes. Where to get The primary key of the InventQualityOrderTable table. Examples QO-00182QO-00183QO-00184
	Return Order ID ReturnOrderId	The unique identifier for a customer return order (RMA).
Description This attribute is the identifier for a Return Material Authorization (RMA) or return order, which tracks the process of receiving goods back from a customer. It links all activities related to a specific return event. This ID is crucial for analyzing the return goods process flow. It allows for measuring the end-to-end cycle time from receipt of the returned item to its final disposition, such as being returned to stock, repaired, or scrapped. This helps identify delays and inefficiencies in the returns process. Why it matters Tracks the end-to-end lifecycle of a customer return, enabling analysis of the return processing cycle time and efficiency. Where to get The sales order ID from SalesTable where the sales type is 'Returned order'. Examples RMA-01002RMA-01003RMA-01004
	Work ID WorkId	The unique identifier for a warehouse work order, such as for picking or put-away.
Description A Work ID is generated in the Warehouse Management module to represent a set of tasks for a warehouse worker, like picking items for an order or putting away received goods. It groups related activities together. Tracking by Work ID allows for analysis of the efficiency of specific warehouse tasks. It helps measure the time taken to complete a put-away or picking process from creation to completion and can be used to analyze worker performance. Why it matters Groups related warehouse tasks, enabling analysis of the efficiency of specific work orders like picking and put-away. Where to get The primary key of the WHSWorkTable table. Examples USMF-000123USMF-000124USMF-000125

Required Recommended Optional

Inventory Management Activities

These are the essential process steps and milestones to capture in your event log for accurate discovery and analysis of your inventory flow.

6 Recommended 9 Optional

	Activity	Description
	Goods Issue Recorded	This activity marks the final removal of the inventory batch from the warehouse, typically due to a customer sale or consumption in production. It is captured when a sales order packing slip or a production picking list journal is posted.
Why it matters This is the primary successful end event for the inventory lifecycle. It concludes the tracking of the batch's time in the warehouse and confirms its final disposition. Where to get Recorded in the InventTrans table as an explicit transaction with a negative quantity. It is generated from posting a sales order packing slip or a production picking list. Capture Identify transactions in InventTrans with a negative quantity linked to a sales order issue or production order consumption. Event type explicit
	Goods Receipt Recorded	This activity marks the initial entry of an inventory batch into the warehouse, typically from a supplier or a production output. This event is captured when a purchase order receipt or a production order 'report as finished' journal is posted, creating a transaction with a positive quantity.
Why it matters This is the primary start event for the inventory lifecycle. It initiates the tracking of put-away cycle times and overall inventory lead time, providing a baseline for performance measurement. Where to get Recorded in the InventTrans table. This is an explicit transaction generated from posting a product receipt for a purchase order or reporting a production order as finished. Capture Identify transactions in InventTrans linked to purchase order receipts or production order reporting with a positive quantity. Event type explicit
	Inventory Discrepancy Adjusted	This activity represents the posting of an inventory adjustment to correct a discrepancy found during a count or for other reasons. This is an explicit transaction that changes the on-hand inventory quantity.
Why it matters This is a crucial event for measuring inventory accuracy and the 'Inventory Discrepancy Rate' KPI. Frequent adjustments signal underlying issues in process control, receiving, or picking. Where to get An explicit transaction is created in the InventTrans table when a counting journal or an inventory adjustment journal is posted. Capture Identify transactions in InventTrans generated from posting an InventJournalTable of type 'Counting' or 'Profit/Loss'. Event type explicit
	Picking Completed	This activity signifies that a worker has finished the physical picking of the inventory batch and moved it to a staging or packing location. It is inferred from the status change of the WMS picking 'work' to 'Closed'.
Why it matters This milestone marks the end of the picking phase and the start of packing or shipping. It is essential for measuring the 'Average Picking-to-Packing Time' and overall fulfillment efficiency. Where to get Inferred from the WHSWorkTable when the 'Work Status' field for a picking work order changes to 'Closed'. Capture Identify the timestamp when the Work Status field on the WHSWorkTable is updated to 'Closed' for the relevant picking work. Event type inferred
	Picking Work Created	Indicates that the system has generated instructions for a warehouse worker to pick an inventory batch for a sales order or production order. This is captured when a new 'work' record for picking is created in the WMS.
Why it matters This event marks the beginning of the outbound fulfillment process. The time between order creation and pick work creation can indicate allocation or planning delays. Where to get A new record is created in the WHSWorkTable with a Work Order Type of 'Sales order' or 'Production pick'. This is triggered by releasing an order to the warehouse. Capture Capture the creation timestamp of a record in the WHSWorkTable where the Work Order Type is related to picking. Event type explicit
	Put-away Completed	This activity represents the completion of the physical movement of the inventory batch into its designated storage bin. It is inferred from the status of the corresponding warehouse 'work' record changing to 'Closed'.
Why it matters This key milestone marks the end of the receiving and put-away process, making the inventory available for fulfillment. It is crucial for measuring the 'Average Put-away Cycle Time' KPI. Where to get Inferred from the WHSWorkTable when the 'Work Status' field for a put-away work order changes to 'Closed'. Capture Identify the timestamp when the Work Status field on the WHSWorkTable is updated to 'Closed' for the relevant batch. Event type inferred
	Counting Journal Created	This activity signifies the initiation of an inventory count for a specific batch or location. This is an explicit event captured when a user creates an inventory counting journal.
Why it matters Marks the beginning of the stock-taking process. The duration and frequency of counting activities are important for understanding operational overhead and inventory accuracy efforts. Where to get Captured from the creation of a record in the InventJournalTable with a journal type of 'Counting'. Capture Use the creation timestamp of the header record in the InventJournalTable for counting journals. Event type explicit
	Inventory Count Performed	This event occurs when a worker enters the counted quantity for the inventory batch into the counting journal. This is captured when the 'counted' field on the journal line is populated.
Why it matters This activity provides the data necessary to identify discrepancies between the system's on-hand quantity and the physical count. It is a critical step before any adjustments are made. Where to get Recorded on the lines of the counting journal (InventJournalTrans) when a user enters a value in the 'Counted' quantity field. Capture Capture the timestamp when the counted quantity is logged in the InventJournalTrans table. Event type explicit
	Packing Completed	Represents the completion of packing the picked items into a container or parcel for shipment. This can be inferred when a container is closed in the packing station interface or when a shipment status is updated.
Why it matters This activity is a key milestone in order fulfillment. Tracking its completion helps analyze the efficiency of the packing station and its contribution to the overall shipping timeline. Where to get This can be inferred from the WHSContainerTable when the container status is changed to 'Closed', or from the WHSShipmentTable when the shipment status advances. Capture Capture the timestamp of the status change to 'Closed' on the WHSContainerTable record associated with the batch. Event type inferred
	Put-away Work Created	Indicates that the system has generated instructions for a warehouse worker to move the received inventory from a staging or receiving area to a storage location. This is captured when a new 'work' record is created in the Warehouse Management System (WMS).
Why it matters This marks the start of the physical put-away process. Analyzing the time from receipt to this point can reveal system or planning delays. Where to get A new record is created in the WHSWorkTable with a Work Order Type of 'Put-away'. This is triggered automatically based on location directives after receipt. Capture Capture the creation timestamp of a record in the WHSWorkTable where the Work Order Type is 'Put-away'. Event type explicit
	Quality Inspection Performed	This activity signifies that a quality inspection has been conducted and the results have been recorded. It is typically inferred from a status change on the quality order from 'Open' to a state indicating completion, such as 'Passed' or 'Failed'.
Why it matters Measures the duration of the quality inspection process itself. Delays here can create significant bottlenecks before stock is available for use or put-away. Where to get Inferred from status updates on the InventQualityOrderTable. The transition from an open status to a validated status marks the event. Capture Monitor the status field on the InventQualityOrderTable and capture the timestamp of the change to a terminal status like 'Pass' or 'Fail'. Event type inferred
	Quality Order Created	Represents the moment an inventory batch is automatically or manually placed on hold for quality inspection upon receipt. This is captured by the creation of a record in the quality orders table, linking it to the source document like a purchase order.
Why it matters Tracking the creation of quality orders helps analyze the time inventory spends in a restricted or 'on-hold' status, which impacts its availability for fulfillment. Where to get Generated in the InventQualityOrderTable. This event is triggered by quality associations configured for items, vendors, or production processes. Capture Capture the creation timestamp of a record in the InventQualityOrderTable for the specific inventory batch. Event type explicit
	Return Goods Received	This activity captures the receipt of a previously sold inventory batch back into the warehouse from a customer. This event is recorded when a return order is processed and the items are received.
Why it matters Tracking returns is essential for understanding product quality issues and the efficiency of the reverse logistics process. It initiates a new sub-process for inspection, restocking, or disposal. Where to get An explicit transaction with a positive quantity is created in the InventTrans table, linked to a Return Material Authorization (RMA) or return order. Capture Identify transactions in InventTrans linked to a return order, which registers the returned batch back into inventory. Event type explicit
	Stock Moved Internally	This event captures the movement of an inventory batch from one location to another within the same warehouse, such as for replenishment or consolidation. It is captured by the posting of an inventory transfer journal or the completion of warehouse movement work.
Why it matters High frequency of internal movements can indicate an inefficient warehouse layout or storage strategy. Analyzing these movements helps optimize inventory placement and reduce handling costs. Where to get Can be an explicit transaction in InventTrans originating from a transfer journal, or an inferred event from the closing of 'Movement' work in the WHSWorkTable. Capture Identify 'Transfer' type transactions in InventTrans or completed work records of type 'Movement' in WHSWorkTable. Event type explicit
	Stock Scrapped	Represents the formal disposal or writing-off of an inventory batch due to it being expired, damaged, or obsolete. This is captured by posting an inventory adjustment journal with a scrap-specific reason code.
Why it matters This is a critical failure or terminal end event. Analyzing scrapped stock helps quantify waste, identify products prone to obsolescence, and improve inventory planning. Where to get An explicit transaction with a negative quantity is created in the InventTrans table when an inventory journal of type 'Profit/Loss' or a counting journal is posted for scrapping. Capture Identify negative quantity transactions in InventTrans originating from an inventory journal posting with a scrap reason code. Event type explicit

Recommended Optional

Extraction Guides

How to get your data from Microsoft Dynamics 365

Steps

Navigate to Data Management Workspace: In Microsoft Dynamics 365, use the search bar to open the 'Data management' workspace.
Create New Export Project: Click the 'Export' tile to create a new data export project. Provide a descriptive name, for example, 'InvMgmt_EventLog_Export'.
Define Project Details: In the project details, specify the 'Data format' as 'CSV'. Set 'Generate data package' to 'Yes'.
Add Required Data Entities: Add the following data entities to the project by clicking '+ Add entity'. You will need to add each one individually. Use the 'Entity name' field to search for them:
- Inventory transactions (InventTrans)
- Inventory batches (InventBatch)
- Quality orders (QualityOrders)
- Warehouse work (WHSWorkTable)
- Inventory counting journal lines (InventJournalTrans_Counting)
- Inventory adjustment journal lines (InventJournalTrans_Movement)
- Sales order packing slip lines (SalesPackingSlipTrans)
- Purchase order receipt lines (VendPackingSlipTrans)
Configure Entity Filters: For each entity, select it and click 'Filter'. It is critical to apply filters to limit the data volume. Apply a date range filter on fields like DATEPHYSICAL for 'Inventory transactions' and CREATEDDATETIME for others. Also, add a filter for the specific legal entity (Company). A recommended date range is the last 3 to 6 months.
Run the Export Job: Once all entities are added and filtered, click 'Export' on the action pane. The system will create a data package containing multiple CSV files, one for each entity.
Download the Data Package: After the export job completes successfully, download the data package, which will be a zip file.
Extract and Unzip Files: Unzip the package to access the individual CSV files for each data entity.
Transform and Combine Data: This is a crucial post-processing step performed outside of Dynamics 365, using a tool like Python, Power Query, or a database staging environment. You must write a script to:
- Load all CSV files.
- For each file, create an 'ActivityName' column and a 'EventTime' column based on specific field values and statuses (as detailed in the query section).
- Standardize column names across all files to match the required attributes (InventoryBatchLot, ActivityName, EventTime, UserId, etc.).
- Union all transformed datasets into a single event log file.
Generate System Attributes: In your transformation script, add the SourceSystem column with a static value of 'Dynamics 365 F&O' and the LastDataUpdate column with the timestamp of when the transformation was run.
Format for Upload: Save the final combined and transformed data as a single CSV file, ensuring it is properly formatted for upload into the process mining tool.

Configuration

Data Entities: The extraction relies on a set of standard entities. The primary ones are InventTrans, WHSWorkTable, and QualityOrders. Ensure these entities are available in your environment.
Export Format: Configure the export project to generate a 'Data package' with the 'CSV' format. This provides separate, easy-to-parse files for each entity.
Date Range Filtering: Always apply a date range filter to manage performance and file size. A period of 3-6 months is recommended for initial analysis. Filter on DATEPHYSICAL or CREATEDDATETIME fields.
Company Filtering: Use the filter option to specify the legal entity (Company) to ensure you are extracting data from the correct business unit.
Incremental Exports: For ongoing analysis, configure the export project as a recurring batch job. This allows for regular, incremental updates to your dataset.
Prerequisites: The user running the export must have appropriate security permissions, typically including the 'Data management administrator' or a similar role, to access the Data Management workspace and the required data entities.

a Sample Query config

The Data Management Framework uses entity configuration, not a single script. The following specifies which entities, fields, and conditions are needed to generate the required activities during the post-export transformation step.

**1. Activity: Goods Receipt Recorded**
*   **Source Entity**: `VendPackingSlipTrans` (Purchase Order Receipt Lines)
*   **Logic**: Each row represents a receipt.
*   **Field Mapping**:
    *   `INVENTBATCHID` -> `InventoryBatchLot`
    *   `'Goods Receipt Recorded'` -> `ActivityName`
    *   `CREATEDDATETIME` -> `EventTime`
    *   `CREATEDBY` -> `UserId`
    *   `ITEMID` -> `ItemId`
    *   `INVENTDIMID` (lookup warehouse) -> `WarehouseId`
    *   `QTY` -> `Quantity`

**2. Activity: Quality Order Created**
*   **Source Entity**: `QualityOrders`
*   **Logic**: Each row represents a quality order creation.
*   **Field Mapping**:
    *   `INVENTBATCHID` -> `InventoryBatchLot`
    *   `'Quality Order Created'` -> `ActivityName`
    *   `CREATEDDATETIME` -> `EventTime`
    *   `CREATEDBY` -> `UserId`
    *   `ITEMID` -> `ItemId`
    *   `INVENTLOCATIONID` -> `WarehouseId`
    *   `QTY` -> `Quantity`

**3. Activity: Quality Inspection Performed**
*   **Source Entity**: `QualityOrders`
*   **Logic**: Filter for rows where `STATUS` is 'Pass' or 'Fail'.
*   **Field Mapping**:
    *   `INVENTBATCHID` -> `InventoryBatchLot`
    *   `'Quality Inspection Performed'` -> `ActivityName`
    *   `MODIFIEDDATETIME` -> `EventTime`
    *   `MODIFIEDBY` -> `UserId`
    *   `ITEMID` -> `ItemId`
    *   `INVENTLOCATIONID` -> `WarehouseId`
    *   `QTY` -> `Quantity`

**4. Activity: Put-away Work Created**
*   **Source Entity**: `WHSWorkTable`
*   **Logic**: Filter for rows where `WORKTYPE` is 'Put'.
*   **Field Mapping**:
    *   `INVENTBATCHID` -> `InventoryBatchLot`
    *   `'Put-away Work Created'` -> `ActivityName`
    *   `CREATEDDATETIME` -> `EventTime`
    *   `USERID` -> `UserId`
    *   `ITEMID` -> `ItemId`
    *   `WAREHOUSEID` -> `WarehouseId`
    *   `QTY` -> `Quantity`

**5. Activity: Put-away Completed**
*   **Source Entity**: `WHSWorkTable`
*   **Logic**: Filter for rows where `WORKTYPE` is 'Put' and `WORKSTATUS` is 'Closed'.
*   **Field Mapping**:
    *   `INVENTBATCHID` -> `InventoryBatchLot`
    *   `'Put-away Completed'` -> `ActivityName`
    *   `MODIFIEDDATETIME` -> `EventTime`
    *   `MODIFIEDBY` -> `UserId`
    *   `ITEMID` -> `ItemId`
    *   `WAREHOUSEID` -> `WarehouseId`
    *   `QTY` -> `Quantity`

**6. Activity: Stock Moved Internally**
*   **Source Entity**: `InventJournalTrans_Movement`
*   **Logic**: Rows from an inventory movement journal.
*   **Field Mapping**:
    *   `INVENTBATCHID` -> `InventoryBatchLot`
    *   `'Stock Moved Internally'` -> `ActivityName`
    *   `TRANSDATE` -> `EventTime`
    *   `CREATEDBY` -> `UserId`
    *   `ITEMID` -> `ItemId`
    *   `INVENTLOCATIONID` -> `WarehouseId`
    *   `QTY` -> `Quantity`

**7. Activity: Counting Journal Created**
*   **Source Entity**: `InventJournalTrans_Counting`
*   **Logic**: The creation of the journal line.
*   **Field Mapping**:
    *   `INVENTBATCHID` -> `InventoryBatchLot`
    *   `'Counting Journal Created'` -> `ActivityName`
    *   `CREATEDDATETIME` -> `EventTime`
    *   `CREATEDBY` -> `UserId`
    *   `ITEMID` -> `ItemId`
    *   `INVENTLOCATIONID` -> `WarehouseId`
    *   `COUNTED` -> `Quantity`

**8. Activity: Inventory Count Performed**
*   **Source Entity**: `InventJournalTrans_Counting`
*   **Logic**: The update of the journal line after counting.
*   **Field Mapping**:
    *   `INVENTBATCHID` -> `InventoryBatchLot`
    *   `'Inventory Count Performed'` -> `ActivityName`
    *   `MODIFIEDDATETIME` -> `EventTime`
    *   `MODIFIEDBY` -> `UserId`
    *   `ITEMID` -> `ItemId`
    *   `INVENTLOCATIONID` -> `WarehouseId`
    *   `COUNTED` -> `Quantity`

**9. Activity: Inventory Discrepancy Adjusted**
*   **Source Entity**: `InventJournalTrans_Counting`
*   **Logic**: The posting of the journal, which creates the inventory transaction.
*   **Field Mapping**:
    *   `INVENTBATCHID` -> `InventoryBatchLot`
    *   `'Inventory Discrepancy Adjusted'` -> `ActivityName`
    *   `TRANSDATE` -> `EventTime`
    *   `CREATEDBY` -> `UserId`
    *   `ITEMID` -> `ItemId`
    *   `INVENTLOCATIONID` -> `WarehouseId`
    *   `QTY` -> `Quantity`

**10. Activity: Picking Work Created**
*   **Source Entity**: `WHSWorkTable`
*   **Logic**: Filter where `WORKTYPE` is 'Pick'.
*   **Field Mapping**:
    *   `INVENTBATCHID` -> `InventoryBatchLot`
    *   `'Picking Work Created'` -> `ActivityName`
    *   `CREATEDDATETIME` -> `EventTime`
    *   `USERID` -> `UserId`
    *   `ITEMID` -> `ItemId`
    *   `WAREHOUSEID` -> `WarehouseId`
    *   `QTY` -> `Quantity`

**11. Activity: Picking Completed**
*   **Source Entity**: `WHSWorkTable`
*   **Logic**: Filter where `WORKTYPE` is 'Pick' and `WORKSTATUS` is 'Closed'.
*   **Field Mapping**:
    *   `INVENTBATCHID` -> `InventoryBatchLot`
    *   `'Picking Completed'` -> `ActivityName`
    *   `MODIFIEDDATETIME` -> `EventTime`
    *   `MODIFIEDBY` -> `UserId`
    *   `ITEMID` -> `ItemId`
    *   `WAREHOUSEID` -> `WarehouseId`
    *   `QTY` -> `Quantity`

**12. Activity: Packing Completed**
*   **Source Entity**: `SalesPackingSlipTrans`
*   **Logic**: Assume packing is complete when the packing slip is posted.
*   **Field Mapping**:
    *   `INVENTBATCHID` -> `InventoryBatchLot`
    *   `'Packing Completed'` -> `ActivityName`
    *   `CREATEDDATETIME` (approximates packing time) -> `EventTime`
    *   `CREATEDBY` -> `UserId`
    *   `ITEMID` -> `ItemId`
    *   `INVENTDIMID` (lookup warehouse) -> `WarehouseId`
    *   `QTY` -> `Quantity`

**13. Activity: Goods Issue Recorded**
*   **Source Entity**: `SalesPackingSlipTrans`
*   **Logic**: The posting of a packing slip represents the goods issue.
*   **Field Mapping**:
    *   `INVENTBATCHID` -> `InventoryBatchLot`
    *   `'Goods Issue Recorded'` -> `ActivityName`
    *   `CREATEDDATETIME` -> `EventTime`
    *   `CREATEDBY` -> `UserId`
    *   `ITEMID` -> `ItemId`
    *   `INVENTDIMID` (lookup warehouse) -> `WarehouseId`
    *   `QTY` * -1 -> `Quantity`

**14. Activity: Return Goods Received**
*   **Source Entity**: `InventTrans`
*   **Logic**: Filter `InventTrans` for transactions linked to a return order (`TRANTYPE` = 'Sales').
*   **Field Mapping**:
    *   `INVENTBATCHID` -> `InventoryBatchLot`
    *   `'Return Goods Received'` -> `ActivityName`
    *   `DATEPHYSICAL` -> `EventTime`
    *   `CREATEDBY` -> `UserId`
    *   `ITEMID` -> `ItemId`
    *   `INVENTDIMID` (lookup warehouse) -> `WarehouseId`
    *   `QTY` -> `Quantity`

**15. Activity: Stock Scrapped**
*   **Source Entity**: `InventJournalTrans_Movement`
*   **Logic**: Filter for journal lines where a specific scrap reason code is used.
*   **Field Mapping**:
    *   `INVENTBATCHID` -> `InventoryBatchLot`
    *   `'Stock Scrapped'` -> `ActivityName`
    *   `TRANSDATE` -> `EventTime`
    *   `CREATEDBY` -> `UserId`
    *   `ITEMID` -> `ItemId`
    *   `INVENTLOCATIONID` -> `WarehouseId`
    *   `QTY` -> `Quantity`
    *   `REASONREFRECID` (lookup reason code) -> `ReasonCode`

Steps

Configure Data Replication: Ensure that either Azure Synapse Link for Dataverse or the 'Bring Your Own Database' (BYOD) feature is configured for your Microsoft Dynamics 365 F&O environment. This will replicate the necessary data entities to an Azure SQL Database or Synapse Analytics workspace.
Identify Required Entities: Add the following key entities to your replication job to ensure all required data is available: InventTrans, InventDim, WHSWorkTable, InventJournalTable, InventJournalTrans, InventQualityOrderTable, and WHSContainerTable. Allow time for the initial synchronization to complete.
Establish Database Connection: Use a SQL client, such as SQL Server Management Studio (SSMS) or Azure Data Studio, to connect to the target Azure SQL Database or Synapse SQL endpoint where your Dynamics 365 data is replicated.
Prepare the SQL Query: Copy the complete SQL query provided in the 'Query' section of this document into your SQL client.
Set Query Parameters: Locate the parameter section at the top of the query. Modify the @StartDate, @EndDate, and @DataAreaId variables to define the desired time frame and specify the legal entity (company) you wish to analyze.
Execute the Query: Run the modified query against the database. The execution time will vary depending on the data volume and the selected date range.
Review Preliminary Results: Once the query completes, briefly inspect the output in your SQL client to ensure it returns rows and that key columns like InventoryBatchLot, ActivityName, and EventTime are populated as expected.
Export the Event Log: Export the entire result set from your SQL client to a CSV file. Name the file appropriately, for instance, d365_inventory_event_log.csv.
Format for Upload: Open the CSV file and verify that the column headers match the requirements for your process mining tool. Ensure timestamp formats are consistent and there are no data formatting issues.
Upload to ProcessMind: Upload the final, clean CSV file to your ProcessMind project to begin your process analysis.

Configuration

Prerequisites: Access to a Microsoft Dynamics 365 F&O environment with Azure Synapse Link or BYOD already configured. You will also need read permissions on the target Azure SQL or Synapse database.
Data Entities: The query relies on standard entities. If your organization has customized tables or fields for tracking inventory movements, the query may need to be adjusted.
Date Range: It is recommended to start with a data range of 3 to 6 months to ensure a representative dataset while managing query performance. The @StartDate and @EndDate parameters in the script control this range.
Company Filter: The @DataAreaId parameter is critical for filtering the data to a specific legal entity. Running the query without this filter on a multi-company instance will result in mixed data and poor performance.
Performance Considerations: The InventTrans entity can be extremely large. Executing this query may consume significant resources on your database. Consider running it during off-peak hours or applying additional filters (e.g., on specific warehouses or item groups) if performance is an issue. Ensure appropriate indexes exist on date and key identifier columns in the replicated database.

a Sample Query sql

DECLARE @StartDate DATETIME = '2023-01-01';
DECLARE @EndDate DATETIME = '2023-12-31';
DECLARE @DataAreaId NVARCHAR(4) = '[YourCompanyCode]'; -- E.g., 'USMF'

SELECT
    CAST(T.InventoryBatchLot AS NVARCHAR(255)) AS InventoryBatchLot,
    CAST(T.ActivityName AS NVARCHAR(255)) AS ActivityName,
    T.EventTime,
    CAST(T.SourceSystem AS NVARCHAR(255)) AS SourceSystem,
    GETUTCDATE() AS LastDataUpdate,
    T.EventEndTime,
    CAST(T.UserId AS NVARCHAR(255)) AS UserId,
    CAST(T.ItemId AS NVARCHAR(255)) AS ItemId,
    CAST(T.WarehouseId AS NVARCHAR(255)) AS WarehouseId,
    CAST(T.ReasonCode AS NVARCHAR(255)) AS ReasonCode,
    T.Quantity
FROM (
    -- 1. Goods Receipt Recorded (Purchase or Production)
    SELECT
        id.INVENTBATCHID AS InventoryBatchLot,
        'Goods Receipt Recorded' AS ActivityName,
        it.DATEPHYSICAL AS EventTime,
        'Dynamics 365 F&O' AS SourceSystem,
        NULL AS EventEndTime,
        it.CREATEDBY AS UserId,
        it.ITEMID AS ItemId,
        id.INVENTLOCATIONID AS WarehouseId,
        NULL AS ReasonCode,
        it.QTY AS Quantity
    FROM InventTrans it
    JOIN InventDim id ON it.INVENTDIMID = id.INVENTDIMID AND it.DATAAREAID = id.DATAAREAID
    WHERE it.DATAAREAID = @DataAreaId AND it.DATEPHYSICAL BETWEEN @StartDate AND @EndDate
      AND id.INVENTBATCHID <> '' AND it.STATUSRECEIPT = 2 AND it.QTY > 0
      AND (it.TRANSTYPE = 0 OR it.TRANSTYPE = 2) -- Purchase order or Production

    UNION ALL

    -- 2. Quality Order Created
    SELECT
        id.INVENTBATCHID AS InventoryBatchLot,
        'Quality Order Created' AS ActivityName,
        iqo.CREATEDDATETIME AS EventTime,
        'Dynamics 365 F&O' AS SourceSystem,
        NULL AS EventEndTime,
        iqo.CREATEDBY AS UserId,
        iqo.ITEMID AS ItemId,
        id.INVENTLOCATIONID AS WarehouseId,
        NULL AS ReasonCode,
        iqo.QTY AS Quantity
    FROM InventQualityOrderTable iqo
    JOIN InventDim id ON iqo.INVENTDIMID = id.INVENTDIMID AND iqo.DATAAREAID = id.DATAAREAID
    WHERE iqo.DATAAREAID = @DataAreaId AND iqo.CREATEDDATETIME BETWEEN @StartDate AND @EndDate
      AND id.INVENTBATCHID <> ''

    UNION ALL

    -- 3. Quality Inspection Performed
    SELECT
        id.INVENTBATCHID AS InventoryBatchLot,
        'Quality Inspection Performed' AS ActivityName,
        iqo.MODIFIEDDATETIME AS EventTime,
        'Dynamics 365 F&O' AS SourceSystem,
        NULL AS EventEndTime,
        iqo.MODIFIEDBY AS UserId,
        iqo.ITEMID AS ItemId,
        id.INVENTLOCATIONID AS WarehouseId,
        CAST(iqo.STATUS AS NVARCHAR(255)) AS ReasonCode, -- Using status (Pass/Fail) as a reason
        iqo.QTY AS Quantity
    FROM InventQualityOrderTable iqo
    JOIN InventDim id ON iqo.INVENTDIMID = id.INVENTDIMID AND iqo.DATAAREAID = id.DATAAREAID
    WHERE iqo.DATAAREAID = @DataAreaId AND iqo.MODIFIEDDATETIME BETWEEN @StartDate AND @EndDate
      AND id.INVENTBATCHID <> '' AND iqo.STATUS IN (2, 3) -- Pass, Fail

    UNION ALL

    -- 4. Put-away Work Created
    SELECT
        id.INVENTBATCHID AS InventoryBatchLot,
        'Put-away Work Created' AS ActivityName,
        whs.CREATEDDATETIME AS EventTime,
        'Dynamics 365 F&O' AS SourceSystem,
        NULL AS EventEndTime,
        whs.CREATEDBY AS UserId,
        whs.ITEMID AS ItemId,
        whs.WAREHOUSEID AS WarehouseId,
        NULL AS ReasonCode,
        whs.QTY AS Quantity
    FROM WHSWorkTable whs
    JOIN InventDim id ON whs.INVENTDIMID = id.INVENTDIMID AND whs.DATAAREAID = id.DATAAREAID
    WHERE whs.DATAAREAID = @DataAreaId AND whs.CREATEDDATETIME BETWEEN @StartDate AND @EndDate
      AND id.INVENTBATCHID <> '' AND whs.WORKTYPE = 0 -- Put

    UNION ALL

    -- 5. Put-away Completed
    SELECT
        id.INVENTBATCHID AS InventoryBatchLot,
        'Put-away Completed' AS ActivityName,
        whs.CLOSEDTIMESTAMP AS EventTime,
        'Dynamics 365 F&O' AS SourceSystem,
        NULL AS EventEndTime,
        whs.MODIFIEDBY AS UserId,
        whs.ITEMID AS ItemId,
        whs.WAREHOUSEID AS WarehouseId,
        NULL AS ReasonCode,
        whs.QTY AS Quantity
    FROM WHSWorkTable whs
    JOIN InventDim id ON whs.INVENTDIMID = id.INVENTDIMID AND whs.DATAAREAID = id.DATAAREAID
    WHERE whs.DATAAREAID = @DataAreaId AND whs.CLOSEDTIMESTAMP BETWEEN @StartDate AND @EndDate
      AND id.INVENTBATCHID <> '' AND whs.WORKTYPE = 0 AND whs.WORKSTATUS = 4 -- Put, Closed

    UNION ALL

    -- 6. Stock Moved Internally
    SELECT
        id.INVENTBATCHID AS InventoryBatchLot,
        'Stock Moved Internally' AS ActivityName,
        ijt.POSTEDDATETIME AS EventTime,
        'Dynamics 365 F&O' AS SourceSystem,
        NULL AS EventEndTime,
        ijt.CREATEDBY AS UserId,
        ijt.ITEMID AS ItemId,
        id.INVENTLOCATIONID AS WarehouseId,
        NULL AS ReasonCode,
        ijt.QTY AS Quantity
    FROM InventJournalTrans ijt
    JOIN InventJournalTable ijtbl ON ijt.JOURNALID = ijtbl.JOURNALID AND ijt.DATAAREAID = ijtbl.DATAAREAID
    JOIN InventDim id ON ijt.INVENTDIMID = id.INVENTDIMID AND ijt.DATAAREAID = id.DATAAREAID
    WHERE ijt.DATAAREAID = @DataAreaId AND ijt.POSTEDDATETIME BETWEEN @StartDate AND @EndDate
      AND id.INVENTBATCHID <> '' AND ijtbl.JOURNALTYPE = 3 -- Transfer

    UNION ALL

    -- 7. Counting Journal Created
    SELECT
        id.INVENTBATCHID AS InventoryBatchLot,
        'Counting Journal Created' AS ActivityName,
        ijt.CREATEDDATETIME AS EventTime,
        'Dynamics 365 F&O' AS SourceSystem,
        NULL AS EventEndTime,
        ijt.CREATEDBY AS UserId,
        ijt.ITEMID AS ItemId,
        id.INVENTLOCATIONID AS WarehouseId,
        NULL AS ReasonCode,
        ijt.QTY AS Quantity
    FROM InventJournalTrans ijt
    JOIN InventJournalTable ijtbl ON ijt.JOURNALID = ijtbl.JOURNALID AND ijt.DATAAREAID = ijtbl.DATAAREAID
    JOIN InventDim id ON ijt.INVENTDIMID = id.INVENTDIMID AND ijt.DATAAREAID = id.DATAAREAID
    WHERE ijt.DATAAREAID = @DataAreaId AND ijt.CREATEDDATETIME BETWEEN @StartDate AND @EndDate
      AND id.INVENTBATCHID <> '' AND ijtbl.JOURNALTYPE = 2 -- Counting

    UNION ALL

    -- 8. Inventory Count Performed
    SELECT
        id.INVENTBATCHID AS InventoryBatchLot,
        'Inventory Count Performed' AS ActivityName,
        ijt.MODIFIEDDATETIME AS EventTime,
        'Dynamics 365 F&O' AS SourceSystem,
        NULL AS EventEndTime,
        ijt.MODIFIEDBY AS UserId,
        ijt.ITEMID AS ItemId,
        id.INVENTLOCATIONID AS WarehouseId,
        NULL AS ReasonCode,
        ijt.COUNTED AS Quantity
    FROM InventJournalTrans ijt
    JOIN InventJournalTable ijtbl ON ijt.JOURNALID = ijtbl.JOURNALID AND ijt.DATAAREAID = ijtbl.DATAAREAID
    JOIN InventDim id ON ijt.INVENTDIMID = id.INVENTDIMID AND ijt.DATAAREAID = id.DATAAREAID
    WHERE ijt.DATAAREAID = @DataAreaId AND ijt.MODIFIEDDATETIME BETWEEN @StartDate AND @EndDate
      AND id.INVENTBATCHID <> '' AND ijtbl.JOURNALTYPE = 2 AND ijt.COUNTED IS NOT NULL AND ijt.COUNTED <> 0

    UNION ALL

    -- 9. Inventory Discrepancy Adjusted
    SELECT
        id.INVENTBATCHID AS InventoryBatchLot,
        'Inventory Discrepancy Adjusted' AS ActivityName,
        ijt.POSTEDDATETIME AS EventTime,
        'Dynamics 365 F&O' AS SourceSystem,
        NULL AS EventEndTime,
        ijt.CREATEDBY AS UserId, -- Posting is often done by the creator
        ijt.ITEMID AS ItemId,
        id.INVENTLOCATIONID AS WarehouseId,
        ijt.REASONREFRECID AS ReasonCode,
        ijt.QTY AS Quantity
    FROM InventJournalTrans ijt
    JOIN InventJournalTable ijtbl ON ijt.JOURNALID = ijtbl.JOURNALID AND ijt.DATAAREAID = ijtbl.DATAAREAID
    JOIN InventDim id ON ijt.INVENTDIMID = id.INVENTDIMID AND ijt.DATAAREAID = id.DATAAREAID
    WHERE ijt.DATAAREAID = @DataAreaId AND ijt.POSTEDDATETIME BETWEEN @StartDate AND @EndDate
      AND id.INVENTBATCHID <> '' AND ijtbl.JOURNALTYPE = 7 -- Adjustment

    UNION ALL

    -- 10. Picking Work Created
    SELECT
        id.INVENTBATCHID AS InventoryBatchLot,
        'Picking Work Created' AS ActivityName,
        whs.CREATEDDATETIME AS EventTime,
        'Dynamics 365 F&O' AS SourceSystem,
        NULL AS EventEndTime,
        whs.CREATEDBY AS UserId,
        whs.ITEMID AS ItemId,
        whs.WAREHOUSEID AS WarehouseId,
        NULL AS ReasonCode,
        whs.QTY AS Quantity
    FROM WHSWorkTable whs
    JOIN InventDim id ON whs.INVENTDIMID = id.INVENTDIMID AND whs.DATAAREAID = id.DATAAREAID
    WHERE whs.DATAAREAID = @DataAreaId AND whs.CREATEDDATETIME BETWEEN @StartDate AND @EndDate
      AND id.INVENTBATCHID <> '' AND whs.WORKTYPE = 1 -- Pick

    UNION ALL

    -- 11. Picking Completed
    SELECT
        id.INVENTBATCHID AS InventoryBatchLot,
        'Picking Completed' AS ActivityName,
        whs.CLOSEDTIMESTAMP AS EventTime,
        'Dynamics 365 F&O' AS SourceSystem,
        NULL AS EventEndTime,
        whs.MODIFIEDBY AS UserId,
        whs.ITEMID AS ItemId,
        whs.WAREHOUSEID AS WarehouseId,
        NULL AS ReasonCode,
        whs.QTY AS Quantity
    FROM WHSWorkTable whs
    JOIN InventDim id ON whs.INVENTDIMID = id.INVENTDIMID AND whs.DATAAREAID = id.DATAAREAID
    WHERE whs.DATAAREAID = @DataAreaId AND whs.CLOSEDTIMESTAMP BETWEEN @StartDate AND @EndDate
      AND id.INVENTBATCHID <> '' AND whs.WORKTYPE = 1 AND whs.WORKSTATUS = 4 -- Pick, Closed

    UNION ALL

    -- 12. Packing Completed
    SELECT
        id.INVENTBATCHID AS InventoryBatchLot,
        'Packing Completed' AS ActivityName,
        wc.CLOSEDDATETIME AS EventTime,
        'Dynamics 365 F&O' AS SourceSystem,
        NULL AS EventEndTime,
        wc.MODIFIEDBY AS UserId,
        '[Multiple Items]' AS ItemId, -- Item is not direct on container header
        wc.WAREHOUSE AS WarehouseId,
        NULL AS ReasonCode,
        NULL AS Quantity
    FROM WHSContainerTable wc
    JOIN WHSWORKTABLE whs ON wc.SHIPMENTID = whs.SHIPMENTID AND wc.DATAAREAID = whs.DATAAREAID
    JOIN InventDim id ON whs.INVENTDIMID = id.INVENTDIMID AND whs.DATAAREAID = id.DATAAREAID
    WHERE wc.DATAAREAID = @DataAreaId AND wc.CLOSEDDATETIME BETWEEN @StartDate AND @EndDate
      AND id.INVENTBATCHID <> ''

    UNION ALL

    -- 13. Goods Issue Recorded (Sales or Production)
    SELECT
        id.INVENTBATCHID AS InventoryBatchLot,
        'Goods Issue Recorded' AS ActivityName,
        it.DATEPHYSICAL AS EventTime,
        'Dynamics 365 F&O' AS SourceSystem,
        NULL AS EventEndTime,
        it.CREATEDBY AS UserId,
        it.ITEMID AS ItemId,
        id.INVENTLOCATIONID AS WarehouseId,
        NULL AS ReasonCode,
        it.QTY AS Quantity -- Quantity will be negative
    FROM InventTrans it
    JOIN InventDim id ON it.INVENTDIMID = id.INVENTDIMID AND it.DATAAREAID = id.DATAAREAID
    WHERE it.DATAAREAID = @DataAreaId AND it.DATEPHYSICAL BETWEEN @StartDate AND @EndDate
      AND id.INVENTBATCHID <> '' AND it.STATUSISSUE = 2 AND it.QTY < 0
      AND (it.TRANSTYPE = 1 OR it.TRANSTYPE = 3) -- Sales order or Production line

    UNION ALL

    -- 14. Return Goods Received
    SELECT
        id.INVENTBATCHID AS InventoryBatchLot,
        'Return Goods Received' AS ActivityName,
        it.DATEPHYSICAL AS EventTime,
        'Dynamics 365 F&O' AS SourceSystem,
        NULL AS EventEndTime,
        it.CREATEDBY AS UserId,
        it.ITEMID AS ItemId,
        id.INVENTLOCATIONID AS WarehouseId,
        NULL AS ReasonCode,
        it.QTY AS Quantity
    FROM InventTrans it
    JOIN InventDim id ON it.INVENTDIMID = id.INVENTDIMID AND it.DATAAREAID = id.DATAAREAID
    WHERE it.DATAAREAID = @DataAreaId AND it.DATEPHYSICAL BETWEEN @StartDate AND @EndDate
      AND id.INVENTBATCHID <> '' AND it.STATUSRECEIPT = 2 AND it.TRANSTYPE = 15 -- Sales return order

    UNION ALL

    -- 15. Stock Scrapped
    SELECT
        id.INVENTBATCHID AS InventoryBatchLot,
        'Stock Scrapped' AS ActivityName,
        ijt.POSTEDDATETIME AS EventTime,
        'Dynamics 365 F&O' AS SourceSystem,
        NULL AS EventEndTime,
        ijt.CREATEDBY AS UserId,
        ijt.ITEMID AS ItemId,
        id.INVENTLOCATIONID AS WarehouseId,
        ijt.REASONREFRECID AS ReasonCode,
        ijt.QTY AS Quantity
    FROM InventJournalTrans ijt
    JOIN InventJournalTable ijtbl ON ijt.JOURNALID = ijtbl.JOURNALID AND ijt.DATAAREAID = ijtbl.DATAAREAID
    JOIN InventDim id ON ijt.INVENTDIMID = id.INVENTDIMID AND ijt.DATAAREAID = id.DATAAREAID
    WHERE ijt.DATAAREAID = @DataAreaId AND ijt.POSTEDDATETIME BETWEEN @StartDate AND @EndDate
      AND id.INVENTBATCHID <> '' AND ijtbl.JOURNALTYPE = 7 -- Adjustment
      AND ijt.REASONREFRECID IN ('[YourScrapReasonCode1]', '[YourScrapReasonCode2]') -- Specify scrap reason codes

) AS T
ORDER BY T.InventoryBatchLot, T.EventTime;

Steps

Prerequisite: Register Azure AD Application: Before running the script, you must register an application in your Azure Active Directory (AD). This application will be used to authenticate with the Dynamics 365 API. Navigate to the Azure portal, go to Azure Active Directory, and select 'App registrations'.
Grant API Permissions: Within your new app registration, go to 'API permissions' and add a permission for 'Dynamics ERP'. Select 'Delegated permissions' and check the AX.FullAccess box. Grant admin consent for your organization. This allows the application to read data on behalf of a user.
Create a Client Secret: In the 'Certificates & secrets' section of your app registration, create a new client secret. Copy the secret's value immediately, as it will be hidden later. You will also need the Application (client) ID and the Directory (tenant) ID from the app's 'Overview' page.
Install Python and Libraries: Ensure you have a Python 3 environment installed on your machine. Install the necessary libraries by running the following command in your terminal: pip install pandas requests msal.
Prepare the Python Script: Copy the complete script provided in the 'query' section into a new file and save it as extract_inventory_log.py.
Configure Script Variables: Open the script and update the placeholder variables at the top. You must provide your D365 environment URL, tenant ID, client ID, client secret, and the legal entity (company code) you want to extract data from. Also, set the desired START_DATE and END_DATE for the extraction period.
Execute the Extraction Script: Open a terminal or command prompt, navigate to the directory where you saved the script, and run it using the command: python extract_inventory_log.py.
Monitor the Script Execution: The script will print its progress to the console, indicating which activity it is currently extracting. It will fetch data for each of the 15 activities one by one, handling pagination automatically.
Review the Output File: Once the script completes, it will generate a CSV file named inventory_event_log.csv in the same directory. This file contains the extracted event log.
Prepare for Upload: Open the CSV file in a spreadsheet application to verify its contents. Ensure the column headers match the required attributes (InventoryBatchLot, ActivityName, EventTime, etc.). The file is now ready for upload into the ProcessMind platform.

Configuration

Authentication: The script uses OAuth 2.0 client credentials flow to authenticate. This requires an Azure AD application registration with a Client ID and Client Secret. The application must be granted API permissions for Dynamics ERP (AX.FullAccess).
API Endpoint: The script connects to the OData REST API of your Dynamics 365 Finance & Operations environment. The D365_RESOURCE variable in the script must be set to the base URL of your D365 instance.
Date Range: The START_DATE and END_DATE variables are critical for controlling the volume of data extracted. For initial analysis, a range of 3 to 6 months is recommended to ensure manageable extraction times.
Company Filter: The COMPANY variable specifies the legal entity (dataAreaId) from which to extract data. The script is configured to query a single company. To extract from all, you would need to modify the API requests to include cross-company=true and adjust filtering.
Performance: Extracting data via the OData API can be time-consuming for large datasets. The script processes each of the 15 activities sequentially. Running the script during off-peak hours is advisable to minimize impact on system performance.
Prerequisites: A functioning Python 3 environment with the pandas, requests, and msal libraries is required. You must have administrative access to Azure AD to register the application or coordinate with an administrator who can provide the necessary credentials.

a Sample Query python

import requests
import pandas as pd
import msal
import datetime
import time

class=class="hl-string">"hl-comment"># --- CONFIGURATION --- 
class=class="hl-string">"hl-comment"># Replace with your specific Dynamics 365 and Azure AD details
D365_RESOURCE = class="hl-string">"https://[your_d365_url].operations.dynamics.com"
TENANT_ID = class="hl-string">"[your_tenant_id]"
CLIENT_ID = class="hl-string">"[your_client_id]"
CLIENT_SECRET = class="hl-string">"[your_client_secret]"
COMPANY = class="hl-string">"[your_company_code]" class=class="hl-string">"hl-comment"># e.g., class="hl-string">"USMF"

class=class="hl-string">"hl-comment"># Set the date range for the extraction
START_DATE = class="hl-string">"2023-01-01"
END_DATE = class="hl-string">"2023-12-31"

class=class="hl-string">"hl-comment"># --- AUTHENTICATION --- 
def get_access_token():
    class="hl-string">"""Authenticates with Azure AD to get an access token."""
    authority = fclass="hl-string">"https://login.microsoftonline.com/{TENANT_ID}"
    app = msal.ConfidentialClientApplication(
        client_id=CLIENT_ID,
        authority=authority,
        client_credential=CLIENT_SECRET
    )
    result = app.acquire_token_for_client(scopes=[fclass="hl-string">"{D365_RESOURCE}/.default"])
    if class="hl-string">"access_token" in result:
        return result[class="hl-string">'access_token']
    else:
        raise Exception(fclass="hl-string">"Error acquiring token: {result.get('error_description')}")

class=class="hl-string">"hl-comment"># --- DATA FETCHING --- 
def fetch_data(access_token, entity_url, params):
    class="hl-string">"""Fetches data from a D365 OData entity, handling pagination."""
    headers = {
        class="hl-string">'Authorization': fclass="hl-string">'Bearer {access_token}',
        class="hl-string">'Content-Type': class="hl-string">'application/json'
    }
    all_records = []
    
    while entity_url:
        try:
            response = requests.get(entity_url, headers=headers, params=params)
            response.raise_for_status() class=class="hl-string">"hl-comment"># Raise an exception for bad status codes
            data = response.json()
            all_records.extend(data[class="hl-string">'value'])
            entity_url = data.get(class="hl-string">'@odata.nextLink')
            params = None class=class="hl-string">"hl-comment"># Parameters are only needed for the first request
        except requests.exceptions.RequestException as e:
            print(fclass="hl-string">"An error occurred: {e}")
            print(fclass="hl-string">"Response content: {response.text}")
            break class=class="hl-string">"hl-comment"># Exit loop on error
    return all_records

class=class="hl-string">"hl-comment"># --- MAIN SCRIPT --- 
def main():
    class="hl-string">"""Main function to orchestrate the data extraction process."""
    print(class="hl-string">"Acquiring access token...")
    token = get_access_token()
    all_events = []
    last_data_update = datetime.datetime.now(datetime.timezone.utc).isoformat()

    class=class="hl-string">"hl-comment"># Define queries for each activity
    activity_queries = [
        {
            class="hl-string">'activity_name': class="hl-string">'Goods Receipt Recorded',
            class="hl-string">'entity': class="hl-string">'InventoryTransactions',
            class="hl-string">'select': class="hl-string">'TransactionDate,InventDimId,ItemId,InventoryLotId,Quantity,CreatedByUserId,InventTransId',
            class="hl-string">'filter': fclass="hl-string">"TransactionDate ge {START_DATE}T00:00:00Z and TransactionDate le {END_DATE}T23:59:59Z and (IssueStatus eq Microsoft.Dynamics.DataEntities.InventIssueStatus'Purchased' or IssueStatus eq Microsoft.Dynamics.DataEntities.InventIssueStatus'Received') and Quantity gt 0",
            class="hl-string">'mapping': {class="hl-string">'InventoryBatchLot': class="hl-string">'InventoryLotId', class="hl-string">'EventTime': class="hl-string">'TransactionDate', class="hl-string">'UserId': class="hl-string">'CreatedByUserId', class="hl-string">'ItemId': class="hl-string">'ItemId', class="hl-string">'Quantity': class="hl-string">'Quantity'}
        },
        {
            class="hl-string">'activity_name': class="hl-string">'Quality Order Created',
            class="hl-string">'entity': class="hl-string">'QualityOrders',
            class="hl-string">'select': class="hl-string">'CreatedDateTime,QualityOrderNumber,ItemId,InventoryLotId,CreatedByUserId',
            class="hl-string">'filter': fclass="hl-string">"CreatedDateTime ge {START_DATE}T00:00:00Z and CreatedDateTime le {END_DATE}T23:59:59Z",
            class="hl-string">'mapping': {class="hl-string">'InventoryBatchLot': class="hl-string">'InventoryLotId', class="hl-string">'EventTime': class="hl-string">'CreatedDateTime', class="hl-string">'UserId': class="hl-string">'CreatedByUserId', class="hl-string">'ItemId': class="hl-string">'ItemId'}
        },
        {
            class="hl-string">'activity_name': class="hl-string">'Quality Inspection Performed',
            class="hl-string">'entity': class="hl-string">'QualityOrders',
            class="hl-string">'select': class="hl-string">'ModifiedDateTime,QualityOrderNumber,ItemId,InventoryLotId,ValidatedByUserId',
            class="hl-string">'filter': fclass="hl-string">"ModifiedDateTime ge {START_DATE}T00:00:00Z and ModifiedDateTime le {END_DATE}T23:59:59Z and (QualityOrderStatus eq Microsoft.Dynamics.DataEntities.InventQualityOrderStatus'Pass' or QualityOrderStatus eq Microsoft.Dynamics.DataEntities.InventQualityOrderStatus'Fail')",
            class="hl-string">'mapping': {class="hl-string">'InventoryBatchLot': class="hl-string">'InventoryLotId', class="hl-string">'EventTime': class="hl-string">'ModifiedDateTime', class="hl-string">'UserId': class="hl-string">'ValidatedByUserId', class="hl-string">'ItemId': class="hl-string">'ItemId'}
        },
        {
            class="hl-string">'activity_name': class="hl-string">'Put-away Work Created',
            class="hl-string">'entity': class="hl-string">'WarehouseWorkHeaders',
            class="hl-string">'select': class="hl-string">'CreatedDateTime,WorkId,LoadId,OrderNumber,InventoryLotId,WarehouseId,WorkCreatedByUserId',
            class="hl-string">'filter': fclass="hl-string">"CreatedDateTime ge {START_DATE}T00:00:00Z and CreatedDateTime le {END_DATE}T23:59:59Z and (WorkOrderType eq Microsoft.Dynamics.DataEntities.WHSWorkOrderType'Purchase' or WorkOrderType eq Microsoft.Dynamics.DataEntities.WHSWorkOrderType'FinishedGoodsPutAway')",
            class="hl-string">'mapping': {class="hl-string">'InventoryBatchLot': class="hl-string">'InventoryLotId', class="hl-string">'EventTime': class="hl-string">'CreatedDateTime', class="hl-string">'UserId': class="hl-string">'WorkCreatedByUserId', class="hl-string">'WarehouseId': class="hl-string">'WarehouseId'}
        },
        {
            class="hl-string">'activity_name': class="hl-string">'Put-away Completed',
            class="hl-string">'entity': class="hl-string">'WarehouseWorkHeaders',
            class="hl-string">'select': class="hl-string">'ModifiedDateTime,WorkId,LoadId,OrderNumber,InventoryLotId,WarehouseId,ClosedByUserId',
            class="hl-string">'filter': fclass="hl-string">"ModifiedDateTime ge {START_DATE}T00:00:00Z and ModifiedDateTime le {END_DATE}T23:59:59Z and WorkStatus eq Microsoft.Dynamics.DataEntities.WHSWorkStatus'Closed' and (WorkOrderType eq Microsoft.Dynamics.DataEntities.WHSWorkOrderType'Purchase' or WorkOrderType eq Microsoft.Dynamics.DataEntities.WHSWorkOrderType'FinishedGoodsPutAway')",
            class="hl-string">'mapping': {class="hl-string">'InventoryBatchLot': class="hl-string">'InventoryLotId', class="hl-string">'EventTime': class="hl-string">'ModifiedDateTime', class="hl-string">'UserId': class="hl-string">'ClosedByUserId', class="hl-string">'WarehouseId': class="hl-string">'WarehouseId'}
        },
        {
            class="hl-string">'activity_name': class="hl-string">'Stock Moved Internally',
            class="hl-string">'entity': class="hl-string">'WarehouseWorkHeaders',
            class="hl-string">'select': class="hl-string">'ModifiedDateTime,WorkId,InventoryLotId,WarehouseId,ClosedByUserId',
            class="hl-string">'filter': fclass="hl-string">"ModifiedDateTime ge {START_DATE}T00:00:00Z and ModifiedDateTime le {END_DATE}T23:59:59Z and WorkStatus eq Microsoft.Dynamics.DataEntities.WHSWorkStatus'Closed' and (WorkOrderType eq Microsoft.Dynamics.DataEntities.WHSWorkOrderType'InventoryMovement' or WorkOrderType eq Microsoft.Dynamics.DataEntities.WHSWorkOrderType'TransferIssue')",
            class="hl-string">'mapping': {class="hl-string">'InventoryBatchLot': class="hl-string">'InventoryLotId', class="hl-string">'EventTime': class="hl-string">'ModifiedDateTime', class="hl-string">'UserId': class="hl-string">'ClosedByUserId', class="hl-string">'WarehouseId': class="hl-string">'WarehouseId'}
        },
        {
            class="hl-string">'activity_name': class="hl-string">'Counting Journal Created',
            class="hl-string">'entity': class="hl-string">'InventoryCountingJournalHeaders',
            class="hl-string">'select': class="hl-string">'CreatedDateTime,JournalNumber,CreatedByUserId,WarehouseId',
            class="hl-string">'filter': fclass="hl-string">"CreatedDateTime ge {START_DATE}T00:00:00Z and CreatedDateTime le {END_DATE}T23:59:59Z",
            class="hl-string">'mapping': {class="hl-string">'InventoryBatchLot': class="hl-string">'JournalNumber', class="hl-string">'EventTime': class="hl-string">'CreatedDateTime', class="hl-string">'UserId': class="hl-string">'CreatedByUserId', class="hl-string">'WarehouseId': class="hl-string">'WarehouseId'} class=class="hl-string">"hl-comment"># Note: Batch not directly on header
        },
        {
            class="hl-string">'activity_name': class="hl-string">'Inventory Count Performed',
            class="hl-string">'entity': class="hl-string">'InventoryCountingJournalLines',
            class="hl-string">'select': class="hl-string">'ModifiedDateTime,JournalNumber,ItemId,InventoryLotId,CountedQuantity,ModifiedByUserId',
            class="hl-string">'filter': fclass="hl-string">"ModifiedDateTime ge {START_DATE}T00:00:00Z and ModifiedDateTime le {END_DATE}T23:59:59Z and CountedQuantity gt 0",
            class="hl-string">'mapping': {class="hl-string">'InventoryBatchLot': class="hl-string">'InventoryLotId', class="hl-string">'EventTime': class="hl-string">'ModifiedDateTime', class="hl-string">'UserId': class="hl-string">'ModifiedByUserId', class="hl-string">'ItemId': class="hl-string">'ItemId', class="hl-string">'Quantity': class="hl-string">'CountedQuantity'}
        },
        {
            class="hl-string">'activity_name': class="hl-string">'Inventory Discrepancy Adjusted',
            class="hl-string">'entity': class="hl-string">'InventoryCountingJournalHeaders',
            class="hl-string">'select': class="hl-string">'ModifiedDateTime,JournalNumber,PostedByUserId,WarehouseId',
            class="hl-string">'filter': fclass="hl-string">"ModifiedDateTime ge {START_DATE}T00:00:00Z and ModifiedDateTime le {END_DATE}T23:59:59Z and JournalStatus eq Microsoft.Dynamics.DataEntities.InventJournalStatus'Posted'",
            class="hl-string">'mapping': {class="hl-string">'InventoryBatchLot': class="hl-string">'JournalNumber', class="hl-string">'EventTime': class="hl-string">'ModifiedDateTime', class="hl-string">'UserId': class="hl-string">'PostedByUserId', class="hl-string">'WarehouseId': class="hl-string">'WarehouseId'} class=class="hl-string">"hl-comment"># Note: Batch not directly on header
        },
        {
            class="hl-string">'activity_name': class="hl-string">'Picking Work Created',
            class="hl-string">'entity': class="hl-string">'WarehouseWorkHeaders',
            class="hl-string">'select': class="hl-string">'CreatedDateTime,WorkId,InventoryLotId,WarehouseId,WorkCreatedByUserId',
            class="hl-string">'filter': fclass="hl-string">"CreatedDateTime ge {START_DATE}T00:00:00Z and CreatedDateTime le {END_DATE}T23:59:59Z and (WorkOrderType eq Microsoft.Dynamics.DataEntities.WHSWorkOrderType'Sales' or WorkOrderType eq Microsoft.Dynamics.DataEntities.WHSWorkOrderType'RawMaterialPicking')",
            class="hl-string">'mapping': {class="hl-string">'InventoryBatchLot': class="hl-string">'InventoryLotId', class="hl-string">'EventTime': class="hl-string">'CreatedDateTime', class="hl-string">'UserId': class="hl-string">'WorkCreatedByUserId', class="hl-string">'WarehouseId': class="hl-string">'WarehouseId'}
        },
        {
            class="hl-string">'activity_name': class="hl-string">'Picking Completed',
            class="hl-string">'entity': class="hl-string">'WarehouseWorkHeaders',
            class="hl-string">'select': class="hl-string">'ModifiedDateTime,WorkId,InventoryLotId,WarehouseId,ClosedByUserId',
            class="hl-string">'filter': fclass="hl-string">"ModifiedDateTime ge {START_DATE}T00:00:00Z and ModifiedDateTime le {END_DATE}T23:59:59Z and WorkStatus eq Microsoft.Dynamics.DataEntities.WHSWorkStatus'Closed' and (WorkOrderType eq Microsoft.Dynamics.DataEntities.WHSWorkOrderType'Sales' or WorkOrderType eq Microsoft.Dynamics.DataEntities.WHSWorkOrderType'RawMaterialPicking')",
            class="hl-string">'mapping': {class="hl-string">'InventoryBatchLot': class="hl-string">'InventoryLotId', class="hl-string">'EventTime': class="hl-string">'ModifiedDateTime', class="hl-string">'UserId': class="hl-string">'ClosedByUserId', class="hl-string">'WarehouseId': class="hl-string">'WarehouseId'}
        },
        {
            class="hl-string">'activity_name': class="hl-string">'Packing Completed',
            class="hl-string">'entity': class="hl-string">'WarehouseContainers',
            class="hl-string">'select': class="hl-string">'ModifiedDateTime,ContainerId,ShipmentId,ContainerPackingPolicy',
            class="hl-string">'filter': fclass="hl-string">"ModifiedDateTime ge {START_DATE}T00:00:00Z and ModifiedDateTime le {END_DATE}T23:59:59Z and IsContainerClosed eq true",
            class="hl-string">'mapping': {class="hl-string">'InventoryBatchLot': class="hl-string">'ContainerId', class="hl-string">'EventTime': class="hl-string">'ModifiedDateTime'} class=class="hl-string">"hl-comment"># Note: Batch not directly on container entity
        },
        {
            class="hl-string">'activity_name': class="hl-string">'Goods Issue Recorded',
            class="hl-string">'entity': class="hl-string">'InventoryTransactions',
            class="hl-string">'select': class="hl-string">'TransactionDate,InventDimId,ItemId,InventoryLotId,Quantity,CreatedByUserId,InventTransId',
            class="hl-string">'filter': fclass="hl-string">"TransactionDate ge {START_DATE}T00:00:00Z and TransactionDate le {END_DATE}T23:59:59Z and IssueStatus eq Microsoft.Dynamics.DataEntities.InventIssueStatus'Deducted' and Quantity lt 0",
            class="hl-string">'mapping': {class="hl-string">'InventoryBatchLot': class="hl-string">'InventoryLotId', class="hl-string">'EventTime': class="hl-string">'TransactionDate', class="hl-string">'UserId': class="hl-string">'CreatedByUserId', class="hl-string">'ItemId': class="hl-string">'ItemId', class="hl-string">'Quantity': class="hl-string">'Quantity'}
        },
        {
            class="hl-string">'activity_name': class="hl-string">'Return Goods Received',
            class="hl-string">'entity': class="hl-string">'WarehouseWorkHeaders',
            class="hl-string">'select': class="hl-string">'ModifiedDateTime,WorkId,InventoryLotId,WarehouseId,ClosedByUserId',
            class="hl-string">'filter': fclass="hl-string">"ModifiedDateTime ge {START_DATE}T00:00:00Z and ModifiedDateTime le {END_DATE}T23:59:59Z and WorkStatus eq Microsoft.Dynamics.DataEntities.WHSWorkStatus'Closed' and WorkOrderType eq Microsoft.Dynamics.DataEntities.WHSWorkOrderType'SalesReturn'",
            class="hl-string">'mapping': {class="hl-string">'InventoryBatchLot': class="hl-string">'InventoryLotId', class="hl-string">'EventTime': class="hl-string">'ModifiedDateTime', class="hl-string">'UserId': class="hl-string">'ClosedByUserId', class="hl-string">'WarehouseId': class="hl-string">'WarehouseId'}
        },
        {
            class="hl-string">'activity_name': class="hl-string">'Stock Scrapped',
            class="hl-string">'entity': class="hl-string">'InventoryMovementJournalLinesV2',
            class="hl-string">'select': class="hl-string">'ModifiedDateTime,JournalNumber,ItemId,InventoryLotId,Quantity,ReasonCode,ModifiedByUserId',
            class="hl-string">'filter': fclass="hl-string">"ModifiedDateTime ge {START_DATE}T00:00:00Z and ModifiedDateTime le {END_DATE}T23:59:59Z and Quantity lt 0", class=class="hl-string">"hl-comment"># Further filter by a specific ReasonCode in your system if applicable
            class="hl-string">'mapping': {class="hl-string">'InventoryBatchLot': class="hl-string">'InventoryLotId', class="hl-string">'EventTime': class="hl-string">'ModifiedDateTime', class="hl-string">'UserId': class="hl-string">'ModifiedByUserId', class="hl-string">'ItemId': class="hl-string">'ItemId', class="hl-string">'Quantity': class="hl-string">'Quantity', class="hl-string">'ReasonCode': class="hl-string">'ReasonCode'}
        },
    ]

    for query_def in activity_queries:
        print(fclass="hl-string">"Extracting: {query_def['activity_name']}...")
        entity_name = query_def[class="hl-string">'entity']
        full_url = fclass="hl-string">"{D365_RESOURCE}/data/{entity_name}"
        params = {
            class="hl-string">'$select': query_def[class="hl-string">'select'],
            class="hl-string">'$filter': query_def[class="hl-string">'filter']
        }
        records = fetch_data(token, full_url, params)
        
        for record in records:
            if not record.get(query_def[class="hl-string">'mapping'][class="hl-string">'InventoryBatchLot']):
                continue class=class="hl-string">"hl-comment"># Skip records without a batch/lot ID

            event = {
                class="hl-string">'InventoryBatchLot': record.get(query_def[class="hl-string">'mapping'][class="hl-string">'InventoryBatchLot']),
                class="hl-string">'ActivityName': query_def[class="hl-string">'activity_name'],
                class="hl-string">'EventTime': record.get(query_def[class="hl-string">'mapping'][class="hl-string">'EventTime']),
                class="hl-string">'SourceSystem': class="hl-string">'Dynamics 365 F&O',
                class="hl-string">'LastDataUpdate': last_data_update,
                class="hl-string">'EventEndTime': None,
                class="hl-string">'UserId': record.get(query_def[class="hl-string">'mapping'].get(class="hl-string">'UserId')),
                class="hl-string">'ItemId': record.get(query_def[class="hl-string">'mapping'].get(class="hl-string">'ItemId')),
                class="hl-string">'WarehouseId': record.get(query_def[class="hl-string">'mapping'].get(class="hl-string">'WarehouseId')),
                class="hl-string">'ReasonCode': record.get(query_def[class="hl-string">'mapping'].get(class="hl-string">'ReasonCode')),
                class="hl-string">'Quantity': record.get(query_def[class="hl-string">'mapping'].get(class="hl-string">'Quantity'))
            }
            all_events.append(event)
        print(fclass="hl-string">"Found {len(records)} records for {query_def['activity_name']}.")

    if not all_events:
        print(class="hl-string">"No events found for the specified criteria.")
        return

    print(class="hl-string">"\nProcessing and saving data...")
    df = pd.DataFrame(all_events)
    df[class="hl-string">'EventTime'] = pd.to_datetime(df[class="hl-string">'EventTime'])
    df.sort_values(by=[class="hl-string">'InventoryBatchLot', class="hl-string">'EventTime'], inplace=True)
    
    output_filename = class="hl-string">'inventory_event_log.csv'
    df.to_csv(output_filename, index=False, date_format=class="hl-string">'%Y-%m-%dT%H:%M:%S.%fZ')
    print(fclass="hl-string">"Extraction complete. Event log saved to {output_filename}")

if __name__ == class="hl-string">"__main__":
    main()

Steps

Navigate to Data Management Workspace: In Microsoft Dynamics 365, use the search bar to open the 'Data management' workspace.
Create New Export Project: Click the 'Export' tile to create a new data export project. Provide a descriptive name, for example, 'InvMgmt_EventLog_Export'.
Define Project Details: In the project details, specify the 'Data format' as 'CSV'. Set 'Generate data package' to 'Yes'.
Add Required Data Entities: Add the following data entities to the project by clicking '+ Add entity'. You will need to add each one individually. Use the 'Entity name' field to search for them:
- Inventory transactions (InventTrans)
- Inventory batches (InventBatch)
- Quality orders (QualityOrders)
- Warehouse work (WHSWorkTable)
- Inventory counting journal lines (InventJournalTrans_Counting)
- Inventory adjustment journal lines (InventJournalTrans_Movement)
- Sales order packing slip lines (SalesPackingSlipTrans)
- Purchase order receipt lines (VendPackingSlipTrans)
Configure Entity Filters: For each entity, select it and click 'Filter'. It is critical to apply filters to limit the data volume. Apply a date range filter on fields like DATEPHYSICAL for 'Inventory transactions' and CREATEDDATETIME for others. Also, add a filter for the specific legal entity (Company). A recommended date range is the last 3 to 6 months.
Run the Export Job: Once all entities are added and filtered, click 'Export' on the action pane. The system will create a data package containing multiple CSV files, one for each entity.
Download the Data Package: After the export job completes successfully, download the data package, which will be a zip file.
Extract and Unzip Files: Unzip the package to access the individual CSV files for each data entity.
Transform and Combine Data: This is a crucial post-processing step performed outside of Dynamics 365, using a tool like Python, Power Query, or a database staging environment. You must write a script to:
- Load all CSV files.
- For each file, create an 'ActivityName' column and a 'EventTime' column based on specific field values and statuses (as detailed in the query section).
- Standardize column names across all files to match the required attributes (InventoryBatchLot, ActivityName, EventTime, UserId, etc.).
- Union all transformed datasets into a single event log file.
Generate System Attributes: In your transformation script, add the SourceSystem column with a static value of 'Dynamics 365 F&O' and the LastDataUpdate column with the timestamp of when the transformation was run.
Format for Upload: Save the final combined and transformed data as a single CSV file, ensuring it is properly formatted for upload into the process mining tool.

Configuration

Data Entities: The extraction relies on a set of standard entities. The primary ones are InventTrans, WHSWorkTable, and QualityOrders. Ensure these entities are available in your environment.
Export Format: Configure the export project to generate a 'Data package' with the 'CSV' format. This provides separate, easy-to-parse files for each entity.
Date Range Filtering: Always apply a date range filter to manage performance and file size. A period of 3-6 months is recommended for initial analysis. Filter on DATEPHYSICAL or CREATEDDATETIME fields.
Company Filtering: Use the filter option to specify the legal entity (Company) to ensure you are extracting data from the correct business unit.
Incremental Exports: For ongoing analysis, configure the export project as a recurring batch job. This allows for regular, incremental updates to your dataset.
Prerequisites: The user running the export must have appropriate security permissions, typically including the 'Data management administrator' or a similar role, to access the Data Management workspace and the required data entities.

a Sample Query config

The Data Management Framework uses entity configuration, not a single script. The following specifies which entities, fields, and conditions are needed to generate the required activities during the post-export transformation step.

**1. Activity: Goods Receipt Recorded**
*   **Source Entity**: `VendPackingSlipTrans` (Purchase Order Receipt Lines)
*   **Logic**: Each row represents a receipt.
*   **Field Mapping**:
    *   `INVENTBATCHID` -> `InventoryBatchLot`
    *   `'Goods Receipt Recorded'` -> `ActivityName`
    *   `CREATEDDATETIME` -> `EventTime`
    *   `CREATEDBY` -> `UserId`
    *   `ITEMID` -> `ItemId`
    *   `INVENTDIMID` (lookup warehouse) -> `WarehouseId`
    *   `QTY` -> `Quantity`

**2. Activity: Quality Order Created**
*   **Source Entity**: `QualityOrders`
*   **Logic**: Each row represents a quality order creation.
*   **Field Mapping**:
    *   `INVENTBATCHID` -> `InventoryBatchLot`
    *   `'Quality Order Created'` -> `ActivityName`
    *   `CREATEDDATETIME` -> `EventTime`
    *   `CREATEDBY` -> `UserId`
    *   `ITEMID` -> `ItemId`
    *   `INVENTLOCATIONID` -> `WarehouseId`
    *   `QTY` -> `Quantity`

**3. Activity: Quality Inspection Performed**
*   **Source Entity**: `QualityOrders`
*   **Logic**: Filter for rows where `STATUS` is 'Pass' or 'Fail'.
*   **Field Mapping**:
    *   `INVENTBATCHID` -> `InventoryBatchLot`
    *   `'Quality Inspection Performed'` -> `ActivityName`
    *   `MODIFIEDDATETIME` -> `EventTime`
    *   `MODIFIEDBY` -> `UserId`
    *   `ITEMID` -> `ItemId`
    *   `INVENTLOCATIONID` -> `WarehouseId`
    *   `QTY` -> `Quantity`

**4. Activity: Put-away Work Created**
*   **Source Entity**: `WHSWorkTable`
*   **Logic**: Filter for rows where `WORKTYPE` is 'Put'.
*   **Field Mapping**:
    *   `INVENTBATCHID` -> `InventoryBatchLot`
    *   `'Put-away Work Created'` -> `ActivityName`
    *   `CREATEDDATETIME` -> `EventTime`
    *   `USERID` -> `UserId`
    *   `ITEMID` -> `ItemId`
    *   `WAREHOUSEID` -> `WarehouseId`
    *   `QTY` -> `Quantity`

**5. Activity: Put-away Completed**
*   **Source Entity**: `WHSWorkTable`
*   **Logic**: Filter for rows where `WORKTYPE` is 'Put' and `WORKSTATUS` is 'Closed'.
*   **Field Mapping**:
    *   `INVENTBATCHID` -> `InventoryBatchLot`
    *   `'Put-away Completed'` -> `ActivityName`
    *   `MODIFIEDDATETIME` -> `EventTime`
    *   `MODIFIEDBY` -> `UserId`
    *   `ITEMID` -> `ItemId`
    *   `WAREHOUSEID` -> `WarehouseId`
    *   `QTY` -> `Quantity`

**6. Activity: Stock Moved Internally**
*   **Source Entity**: `InventJournalTrans_Movement`
*   **Logic**: Rows from an inventory movement journal.
*   **Field Mapping**:
    *   `INVENTBATCHID` -> `InventoryBatchLot`
    *   `'Stock Moved Internally'` -> `ActivityName`
    *   `TRANSDATE` -> `EventTime`
    *   `CREATEDBY` -> `UserId`
    *   `ITEMID` -> `ItemId`
    *   `INVENTLOCATIONID` -> `WarehouseId`
    *   `QTY` -> `Quantity`

**7. Activity: Counting Journal Created**
*   **Source Entity**: `InventJournalTrans_Counting`
*   **Logic**: The creation of the journal line.
*   **Field Mapping**:
    *   `INVENTBATCHID` -> `InventoryBatchLot`
    *   `'Counting Journal Created'` -> `ActivityName`
    *   `CREATEDDATETIME` -> `EventTime`
    *   `CREATEDBY` -> `UserId`
    *   `ITEMID` -> `ItemId`
    *   `INVENTLOCATIONID` -> `WarehouseId`
    *   `COUNTED` -> `Quantity`

**8. Activity: Inventory Count Performed**
*   **Source Entity**: `InventJournalTrans_Counting`
*   **Logic**: The update of the journal line after counting.
*   **Field Mapping**:
    *   `INVENTBATCHID` -> `InventoryBatchLot`
    *   `'Inventory Count Performed'` -> `ActivityName`
    *   `MODIFIEDDATETIME` -> `EventTime`
    *   `MODIFIEDBY` -> `UserId`
    *   `ITEMID` -> `ItemId`
    *   `INVENTLOCATIONID` -> `WarehouseId`
    *   `COUNTED` -> `Quantity`

**9. Activity: Inventory Discrepancy Adjusted**
*   **Source Entity**: `InventJournalTrans_Counting`
*   **Logic**: The posting of the journal, which creates the inventory transaction.
*   **Field Mapping**:
    *   `INVENTBATCHID` -> `InventoryBatchLot`
    *   `'Inventory Discrepancy Adjusted'` -> `ActivityName`
    *   `TRANSDATE` -> `EventTime`
    *   `CREATEDBY` -> `UserId`
    *   `ITEMID` -> `ItemId`
    *   `INVENTLOCATIONID` -> `WarehouseId`
    *   `QTY` -> `Quantity`

**10. Activity: Picking Work Created**
*   **Source Entity**: `WHSWorkTable`
*   **Logic**: Filter where `WORKTYPE` is 'Pick'.
*   **Field Mapping**:
    *   `INVENTBATCHID` -> `InventoryBatchLot`
    *   `'Picking Work Created'` -> `ActivityName`
    *   `CREATEDDATETIME` -> `EventTime`
    *   `USERID` -> `UserId`
    *   `ITEMID` -> `ItemId`
    *   `WAREHOUSEID` -> `WarehouseId`
    *   `QTY` -> `Quantity`

**11. Activity: Picking Completed**
*   **Source Entity**: `WHSWorkTable`
*   **Logic**: Filter where `WORKTYPE` is 'Pick' and `WORKSTATUS` is 'Closed'.
*   **Field Mapping**:
    *   `INVENTBATCHID` -> `InventoryBatchLot`
    *   `'Picking Completed'` -> `ActivityName`
    *   `MODIFIEDDATETIME` -> `EventTime`
    *   `MODIFIEDBY` -> `UserId`
    *   `ITEMID` -> `ItemId`
    *   `WAREHOUSEID` -> `WarehouseId`
    *   `QTY` -> `Quantity`

**12. Activity: Packing Completed**
*   **Source Entity**: `SalesPackingSlipTrans`
*   **Logic**: Assume packing is complete when the packing slip is posted.
*   **Field Mapping**:
    *   `INVENTBATCHID` -> `InventoryBatchLot`
    *   `'Packing Completed'` -> `ActivityName`
    *   `CREATEDDATETIME` (approximates packing time) -> `EventTime`
    *   `CREATEDBY` -> `UserId`
    *   `ITEMID` -> `ItemId`
    *   `INVENTDIMID` (lookup warehouse) -> `WarehouseId`
    *   `QTY` -> `Quantity`

**13. Activity: Goods Issue Recorded**
*   **Source Entity**: `SalesPackingSlipTrans`
*   **Logic**: The posting of a packing slip represents the goods issue.
*   **Field Mapping**:
    *   `INVENTBATCHID` -> `InventoryBatchLot`
    *   `'Goods Issue Recorded'` -> `ActivityName`
    *   `CREATEDDATETIME` -> `EventTime`
    *   `CREATEDBY` -> `UserId`
    *   `ITEMID` -> `ItemId`
    *   `INVENTDIMID` (lookup warehouse) -> `WarehouseId`
    *   `QTY` * -1 -> `Quantity`

**14. Activity: Return Goods Received**
*   **Source Entity**: `InventTrans`
*   **Logic**: Filter `InventTrans` for transactions linked to a return order (`TRANTYPE` = 'Sales').
*   **Field Mapping**:
    *   `INVENTBATCHID` -> `InventoryBatchLot`
    *   `'Return Goods Received'` -> `ActivityName`
    *   `DATEPHYSICAL` -> `EventTime`
    *   `CREATEDBY` -> `UserId`
    *   `ITEMID` -> `ItemId`
    *   `INVENTDIMID` (lookup warehouse) -> `WarehouseId`
    *   `QTY` -> `Quantity`

**15. Activity: Stock Scrapped**
*   **Source Entity**: `InventJournalTrans_Movement`
*   **Logic**: Filter for journal lines where a specific scrap reason code is used.
*   **Field Mapping**:
    *   `INVENTBATCHID` -> `InventoryBatchLot`
    *   `'Stock Scrapped'` -> `ActivityName`
    *   `TRANSDATE` -> `EventTime`
    *   `CREATEDBY` -> `UserId`
    *   `ITEMID` -> `ItemId`
    *   `INVENTLOCATIONID` -> `WarehouseId`
    *   `QTY` -> `Quantity`
    *   `REASONREFRECID` (lookup reason code) -> `ReasonCode`

Your Inventory Management Data Template

Your Inventory Management Data Template

Inventory Management Attributes

Inventory Management Activities

Extraction Guides

Dynamics 365 Data Management Framework Export

Steps

Configuration

a Sample Query config

Direct SQL Query via Azure Synapse Link or BYOD

Steps

Configuration

a Sample Query sql

Python Script Extraction via Dynamics 365 OData API

Steps

Configuration

a Sample Query python

Microsoft Power BI and Power Query Dataflow Extraction

Steps

Configuration

a Sample Query config

Ready to Get Started?

Optimize Inventory Management, Boost Flow Today

Your Inventory Management Data Template

Your Inventory Management Data Template

Inventory Management Attributes

Inventory Management Activities

Extraction Guides

Dynamics 365 Data Management Framework Export

Steps

Configuration

a Sample Query config

Direct SQL Query via Azure Synapse Link or BYOD

Steps

Configuration

a Sample Query sql

Python Script Extraction via Dynamics 365 OData API

Steps

Configuration

a Sample Query python

Microsoft Power BI and Power Query Dataflow Extraction

Steps

Configuration

a Sample Query config

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Ready to Get Started?

Optimize Inventory Management, Boost Flow Today