Your Warehouse Management Data Template

This attribute describes a single step in the warehouse management process, such as 'Goods Picked from Storage' or 'Shipment Dispatched'. Each activity represents a distinct business event recorded in the system, associated with a specific timestamp.

Analyzing activities is the core of process mining. It allows for the construction of the process map, showing how work actually flows through the warehouse. This helps to identify bottlenecks, rework loops, and deviations from the standard operating procedure.

It defines the steps of the process, forming the basis of the process map and enabling the analysis of process flow, variations, and bottlenecks.

Event or transaction log tables in Körber WMS, where business events are recorded. This is often derived from transaction codes or status change descriptions.

Event Time is the timestamp associated with each activity, marking the exact moment it occurred. This temporal data is fundamental for calculating durations, cycle times, and waiting times between different steps in the process.

In process analysis, this attribute is used to order events chronologically, build the process flow, and perform any time-based analysis. It is essential for dashboards that measure performance, such as cycle time analysis, and for calculating KPIs like 'Average Order End-to-End Cycle Time'.

This timestamp is critical for ordering events, calculating all time-based metrics like cycle times and waiting times, and understanding process performance.

Located in all transaction and event log tables within Körber WMS, typically named something like 'CreationDate', 'Timestamp', or 'EventDateTime'.

The Warehouse Order is the central identifier that groups all tasks and events related to a specific logistical request, such as an inbound receipt or an outbound shipment. It allows for the end-to-end tracking of an order's lifecycle within the warehouse, from its creation to its final dispatch or cancellation.

In process mining, analyzing by Warehouse Order enables the visualization of the entire process flow for each order. This helps in identifying common paths, deviations, bottlenecks, and the overall cycle time for different types of orders, such as standard vs. expedited.

This is the essential Case ID that connects all related events, allowing for a complete, end-to-end analysis of the warehouse management process for each specific order.

This identifier is typically found in core order management tables within Körber WMS. Consult Körber WMS documentation for specific table and field names, such as order headers.

This attribute specifies the date and time of the most recent data extraction or update. It provides context for the freshness of the data being analyzed, ensuring that users are aware of how current the process view is.

In dashboards and reports, this information is vital for transparency. It helps users understand if they are looking at real-time, daily, or weekly data, which impacts decision-making.

Informs users about the timeliness of the data, which is critical for making accurate and relevant business decisions based on the analysis.

This value is generated and recorded by the data pipeline or ETL tool at the end of each data refresh cycle.

This attribute identifies the originating system for the event data, which is 'Körber WMS' in this case. In environments with multiple integrated systems, this field helps differentiate data sources and trace data lineage.

For analysis, it provides context, especially when combining data from multiple systems. It helps ensure data quality and can be used to filter analysis to a specific system's activities.

Provides crucial context about data origin, ensuring clarity and traceability, especially in environments with multiple interconnected systems.

This is typically a static value added during the data extraction process to identify the source system.

Actual Quantity is the number of units physically counted, picked, packed, or received by the warehouse operator. This value is recorded upon task completion and may sometimes differ from the 'Planned Quantity' due to stock shortages, damages, or human error.

Comparing this attribute against the 'Planned Quantity' is fundamental for the 'Inventory Process Health and Accuracy' dashboard. Discrepancies between the two values are direct indicators of process failures or data inaccuracies that need investigation.

Provides the ground truth for what was physically handled, making it essential for calculating discrepancy rates and ensuring inventory accuracy.

Found in transaction confirmation or task completion records. Field names might include 'ActualQty', 'ConfirmedQuantity', or 'PickedQuantity'.

The Priority Level is a classification assigned to a warehouse order to dictate its handling urgency. For example, an order might be marked as 'Expedited' or 'High Priority', indicating it should be processed ahead of standard orders.

This attribute is essential for the 'Expedited Order Analysis' dashboard and the 'Expedited Shipment %' KPI. It helps in understanding the impact of urgent orders on overall warehouse operations, their associated costs, and whether their processing times are actually faster than standard orders.

Helps analyze the handling of urgent orders, their frequency, and their impact on overall process performance and costs.

Located in the order header data. Look for fields like 'Priority', 'Urgency', or a specific shipping service level indicator.

The Product SKU is the unique identifier for a specific product or material involved in the warehouse order. An order can contain one or more SKUs.

Analyzing by Product SKU helps to understand if certain products have more complex or problematic handling processes. For example, you might discover that fragile items have longer packing times or that certain SKUs are frequently associated with picking discrepancies. This can inform changes to storage strategy or handling procedures.

Allows for analysis of process performance based on specific products, revealing if certain items cause delays or errors.

Found in the order line item tables, linked to the main warehouse order header. Common field names include 'SKU', 'MaterialNumber', or 'ItemCode'.

This attribute identifies the warehouse employee or system user responsible for executing a specific task, such as picking, packing, or putting away goods. It can also refer to an automated system or bot in some cases.

This dimension is critical for resource performance analysis. It helps in understanding workload distribution, identifying top-performing employees, and spotting individuals who may require additional training. It is the basis for the 'Resource Utilization and Workload' dashboard and the 'Throughput per Operator' KPI.

Enables analysis of workforce performance, workload distribution, and resource efficiency, helping to identify training needs and high-performers.

Found in transaction or log tables where user actions are recorded. Look for fields like 'UserID', 'UserName', 'ExecutedBy', or 'OperatorID'.

This metric represents the processing time for a single event, calculated as the difference between its End Time and Start Time. If an End Time is not available, it can be inferred from the time between consecutive events.

Analyzing activity duration is key to pinpointing which specific tasks are consuming the most time in the overall process. This is used in dashboards like 'Resource Utilization and Workload' to understand effort per task and is essential for calculating KPIs like 'Average Quality Inspection Time'.

Directly measures the time spent on individual tasks, helping to identify the longest and most inefficient steps in the warehouse process.

This is typically calculated during data transformation by subtracting the start timestamp from the end timestamp of an activity.

The Carrier is the third-party logistics provider (e.g., FedEx, UPS, DHL) responsible for transporting the goods from the warehouse to the final destination. This is typically assigned during the shipment planning or dispatching phase.

Analyzing by carrier can reveal performance differences between shipping partners. For example, it can help identify if certain carriers are associated with longer staging times or more frequent delays, providing valuable data for carrier contract negotiations and selection.

Allows for performance analysis of different shipping partners, helping to optimize logistics and improve delivery reliability.

Found in shipment or transportation planning tables within Körber WMS. Look for fields like 'CarrierCode', 'ShippingAgent', or 'SCAC'.

Cycle Time is a calculated metric that measures the total elapsed time for a case, from the very first event ('Warehouse Order Created') to the last event ('Warehouse Order Completed'). It represents the end-to-end processing time for an order.

This is a primary key performance indicator in process mining, directly answering the question 'How long does it take?'. It is the core metric for the 'Warehouse Order End-to-End Cycle Time' dashboard and the 'Average Order End-to-End Cycle Time' KPI, used to track overall process health and identify orders that take an unusually long time to fulfill.

This is a critical KPI that measures the overall efficiency of the warehouse process, directly impacting customer satisfaction and operational costs.

This metric is calculated in the process mining tool by taking the difference between the timestamp of the last event and the first event for each Warehouse Order.

The End Time represents the completion timestamp for an activity. While StartTime (EventTime) marks the beginning, End Time marks the conclusion, allowing for the direct calculation of that single activity's duration. Not all events have a distinct end time; for many, the StartTime of the next event is used to infer the duration of the previous one.

This attribute is extremely valuable for accurately calculating the processing time of individual tasks. For example, it is used to determine the 'Average Quality Inspection Time' by measuring the time from when the inspection started to when it ended.

Enables the precise calculation of individual activity processing times, which is crucial for identifying inefficient tasks and resource bottlenecks.

Consult Körber WMS documentation. This may be in transaction tables alongside the start time or in related status history tables.

This attribute specifies the piece of material handling equipment (MHE) or technology used during a warehouse task. This could be a specific forklift, pallet jack, handheld scanner, or an automated guided vehicle (AGV).

Analyzing by equipment helps in understanding resource utilization, maintenance needs, and the impact of different types of equipment on task efficiency. It is a key dimension for the 'Resource Utilization and Workload' dashboard, allowing for a holistic view of both human and machine resources.

Enables analysis of equipment utilization and its impact on task performance, helping to optimize fleet management and identify machinery-related bottlenecks.

Consult Körber WMS documentation. This data may be logged in task execution records, especially if operators log in to specific equipment.

This is a derived boolean attribute that is true if the 'Actual Quantity' differs from the 'Planned Quantity' for any picking-related activity. It serves as a simple indicator of a picking error or inventory problem for a specific task.

This flag simplifies analysis by allowing users to quickly filter for all orders that experienced a picking discrepancy. It is used to calculate the 'Picking Discrepancy Rate' KPI and helps power the 'Inventory Process Health and Accuracy' dashboard by highlighting specific points of failure.

Provides a clear, binary indicator of picking errors, simplifying the analysis needed to identify and quantify inventory accuracy issues.

Calculated during data transformation. The logic is: IF (ActualQuantity != PlannedQuantity) THEN true ELSE false for relevant picking activities.

The Order Type defines the business purpose of the warehouse order. Common types include customer shipments (outbound), supplier receipts (inbound), stock transfers between warehouse locations (internal), or returns.

This is a powerful attribute for filtering and comparative analysis. It allows you to analyze and compare the process flows and performance for different kinds of logistical operations, for example, to see if the inbound process is more or less efficient than the outbound process.

Allows for the segmentation of analysis by the purpose of the order, revealing performance differences between processes like inbound receipts and outbound shipments.

Typically located in the order header table in Körber WMS. Look for a field named 'OrderType', 'TransactionType', or similar.

Planned Quantity represents the target number of units for a given task as specified by the warehouse order. For example, if an order requires picking 10 units of a specific SKU, the planned quantity for that picking task is 10.

This attribute is crucial for identifying discrepancies when compared with the 'Actual Quantity'. It is a key input for calculating the 'Picking Discrepancy Rate' and 'Inventory Discrepancy Rate' KPIs, which are vital for maintaining inventory accuracy.

Serves as the baseline for measuring accuracy in tasks like picking and receiving, enabling the detection of inventory discrepancies.

Available in task or order line item tables. Look for fields like 'OrderQuantity', 'PlannedQty', or 'ExpectedQuantity'.

This is the target completion or shipment date for an outbound order, often dictated by customer expectations or service level agreements (SLAs). It serves as the primary deadline against which actual performance is measured.

This date is crucial for the 'Expedited Order Analysis' dashboard. Comparing the 'Requested Completion Date' with the 'Actual Completion Date' (the timestamp of the 'Shipment Dispatched' or 'Warehouse Order Completed' activity) helps determine on-time performance and identify orders that are at risk of being late.

Provides the baseline for measuring on-time performance and fulfilling service level agreements (SLAs), highlighting potentially late orders.

Located in the order header table. Common field names include 'RequiredDeliveryDate', 'RequestedShipDate', or 'SLA'.

SLA Status is a calculated attribute that categorizes each order based on its timeliness relative to the 'RequestedCompletionDate'. It can have values like 'On Time', 'Late', or 'In Progress'.

This attribute provides an immediate view of service level performance. It allows for quick filtering and analysis of all late orders to understand the root causes, such as specific bottlenecks or resource issues. This is a crucial element for any analysis focused on customer satisfaction and operational reliability.

Directly measures adherence to service level agreements, allowing for easy identification and root cause analysis of late orders.

This is calculated in the data transformation layer by comparing the timestamp of the 'Warehouse Order Completed' event to the 'RequestedCompletionDate'.

This attribute identifies the physical coordinate within the warehouse, like a rack, shelf, or bin. It is relevant for activities like 'Goods Put Away in Storage' and 'Goods Picked from Storage'.

This data is used in the 'Putaway Efficiency and Location Usage' dashboard to analyze travel times, location utilization, and the effectiveness of storage strategies. For example, it can help determine if high-velocity items are stored in easily accessible locations to minimize picking time.

Helps optimize warehouse layout and storage strategy by analyzing travel times and the efficiency of putaway and picking tasks for specific locations.

Found in inventory, task, or location master data tables. Look for fields such as 'BinCode', 'LocationID', or 'StorageBin'.

The Warehouse ID specifies the physical location or facility where the warehouse order is being processed. For organizations with multiple distribution centers, this is a key dimension for analysis.

This attribute allows for benchmarking performance across different sites. For example, you can compare the 'Average Order End-to-End Cycle Time' between Warehouse A and Warehouse B to identify best practices or operational issues specific to a location.

Enables performance comparison and benchmarking across different physical warehouse locations, highlighting regional or facility-specific issues.

This information is usually available in order header or site configuration tables. It might be represented as 'Plant', 'Site', or 'LocationCode'.