Your Warehouse Management Data Template

The Activity Name describes a distinct step or event within the warehouse management process. These activities represent the individual tasks performed to fulfill a warehouse order, providing a granular view of the process flow. Examples include creating an order, receiving goods, performing quality checks, picking items, and dispatching shipments.

For process mining analysis, this attribute is crucial for constructing the process map. It defines the nodes in the process graph, allowing analysts to visualize the sequence of events, understand the frequency of different tasks, and identify bottlenecks or inefficient transitions between activities. A clear and consistent naming convention for activities is vital for meaningful process discovery and analysis.

This attribute defines the steps in the process map, which is the foundation of all process mining analysis for identifying bottlenecks and deviations.

Usually found in event logs, task management tables, or transaction data where individual warehouse operations are recorded.

The Event Start Time is a precise timestamp that records the moment a warehouse activity commences. This data point is critical for understanding the timing and duration of process steps. When combined with an end timestamp, it allows for the exact calculation of how long each activity took to complete.

In process mining, this timestamp is fundamental for ordering events chronologically to build an accurate process flow. It is the basis for all time-based analysis, including calculating cycle times between activities, identifying waiting times, and pinpointing bottlenecks where work is delayed. Accurate start times are essential for performance dashboards and KPIs related to process efficiency.

This timestamp is essential for ordering events correctly and calculating cycle times, which are critical for identifying process bottlenecks and delays.

Found in system event logs or transaction records that capture the start of a specific task or status change.

The Last Data Update timestamp specifies when the dataset used for the analysis was last refreshed from the source systems. It provides a clear indication of the data's freshness and relevance, ensuring that users are aware of the time frame covered by the analysis.

This attribute is critical for maintaining trust in the process mining dashboards and analyses. It allows users to understand if they are looking at near real-time information or historical data. Displaying this timestamp prominently in dashboards helps manage user expectations and prevents misinterpretations based on outdated information. It is a key component of data governance and transparency.

This timestamp ensures users understand the freshness of the data, which is vital for making informed, timely decisions based on the process mining insights.

This is typically generated and stored during the data extraction (ETL) process when data is loaded into the process mining platform.

The Source System attribute indicates the origin of the event data, such as a specific Warehouse Management System (WMS), Enterprise Resource Planning (ERP) system, or a legacy application. In complex IT landscapes, a single business process can span multiple systems, and this field helps differentiate where each piece of data comes from.

This information is valuable for data validation and governance, ensuring that the data used for process mining is traced back to its correct origin. It also helps in root cause analysis when data quality issues are detected. For analysis, it can be used to compare process variations or performance metrics between different systems or system versions within the same organization.

It provides context about the data's origin, which is crucial for data validation, troubleshooting, and understanding process variations across different systems.

This information is often added during the data extraction (ETL) process or can be a standard field in data warehouses and integration platforms.

The Warehouse Order ID is a unique key assigned to each logistical work order within the warehouse management system. It connects all related activities, such as receiving, putaway, picking, packing, and shipping, into a single, end-to-end process instance.

In process mining, this attribute is fundamental for reconstructing the lifecycle of each warehouse order. By using the Warehouse Order ID as the case identifier, analysts can visualize the complete flow of an order, identify common paths, discover deviations from the standard process, and measure the total time taken from start to finish. It allows for a comprehensive analysis of the entire warehouse fulfillment process for a specific order.

This is the essential Case ID that links all related warehouse activities together, making it possible to analyze the end-to-end order fulfillment process.

Typically found in the header tables of warehouse order documents or transaction logs related to logistical tasks.

Actual Quantity is the number of units physically handled, counted, and confirmed by a warehouse operator or system during an activity. This reflects the real-world execution of a task, such as the number of items received from a supplier or the quantity picked from a bin to fulfill an order.

This attribute is essential for variance analysis when compared against the Planned Quantity. Discrepancies between planned and actual quantities are direct indicators of process issues, such as short shipments from suppliers, picking errors, or inventory inaccuracies. Process mining can use this data to pinpoint which products, locations, or employees are most frequently associated with these variances, enabling targeted interventions to improve accuracy and reduce costly errors.

This measures the real outcome of a task. Analyzing it against the planned quantity helps identify errors and inefficiencies in picking, receiving, or counting.

Recorded in task confirmation data, where an operator or system enters the quantity of items that were physically handled.

The Event End Time marks the precise moment a warehouse task or activity is finished. This timestamp is the counterpart to the Event Start Time and is essential for measuring the duration of individual activities. Not all events have a distinct duration, but for those that do, like picking or packing, this data is invaluable.

In process mining, having both start and end times allows for a more sophisticated analysis of process performance. It enables the calculation of processing times (the time spent actively working on a task) versus waiting times (the time between tasks). This distinction is critical for identifying true operational bottlenecks versus resource availability issues. Analyzing activity durations helps in performance benchmarking, resource planning, and identifying opportunities for operational improvement.

It enables the calculation of precise activity durations, helping to differentiate between active processing time and idle waiting time for better bottleneck analysis.

Found in system event logs or transaction records that capture the completion of a specific task or a status change.

The Order Type classifies warehouse orders based on their business purpose. Common types include inbound orders for receiving goods from suppliers, outbound orders for shipping products to customers, and internal orders for movements like replenishment or inventory adjustments.

This attribute is fundamental for segmenting and comparing different warehouse processes. The process flow for an inbound order is vastly different from that of an outbound order. By using Order Type as a filter, analysts can create separate process maps and dashboards for each flow, allowing for a more accurate and relevant analysis. Comparing KPIs like cycle time across different order types can help in allocating resources more effectively and tailoring processes to meet specific demands.

This allows for the separation and comparison of distinct processes like inbound, outbound, and internal movements, which have different flows and performance goals.

Typically located in the header data of the warehouse order document.

Planned Quantity represents the target number of units for a specific warehouse task. For an inbound order, it is the quantity expected to be received. For an outbound order, it is the quantity that needs to be picked and shipped. This value is typically derived from the originating business document, such as a purchase order or sales order.

This attribute serves as a crucial baseline for performance and accuracy analysis. By comparing the Planned Quantity to the Actual Quantity, businesses can measure picking accuracy, receiving discrepancies, and inventory record accuracy. Analyzing variances can help identify systemic issues with suppliers, internal processes, or inventory data, leading to improvements in operational efficiency and customer satisfaction.

This provides the baseline for measuring accuracy. Comparing it with the actual quantity is key to calculating KPIs like Picking Accuracy and identifying discrepancies.

Found in the line item details of a warehouse order or task, often sourced from an associated sales order, purchase order, or transfer order.

The Requested Completion Date represents the target deadline for finalizing a warehouse order, typically for outbound shipments. This date is often driven by customer commitments, carrier pickup schedules, or internal service level agreements (SLAs). It serves as the primary benchmark for measuring on-time performance.

In process mining, this attribute is essential for On-Time Shipment Performance analysis. By comparing the Actual Completion Date with the Requested Completion Date, businesses can calculate their on-time rate and identify the root causes of delays. Analyzing orders that missed their requested date can reveal specific bottlenecks, resource constraints, or process steps that consistently contribute to lateness, enabling targeted improvement efforts.

This is the benchmark for measuring on-time performance. Comparing it to the actual completion date is key to calculating the On-Time Shipment Rate KPI.

Typically found in the header data of the warehouse order, often inherited from the associated sales order or customer request.

The Storage Location identifies the physical place within the warehouse associated with an activity. This could be a source location for a picking task or a destination location for a putaway task. The granularity can range from a broad zone to a specific rack, shelf, and bin number.

Analyzing data by Storage Location provides insights into warehouse layout and efficiency. Process mining can reveal travel times between different locations, identify 'hot spots' with high activity, and highlight inefficient putaway or picking paths. This information is invaluable for optimizing slotting strategies, where frequently picked items are placed in easily accessible locations to minimize travel time and improve overall picking efficiency. It can also help in redesigning the warehouse layout to reduce congestion and improve flow.

It enables analysis of warehouse layout and travel efficiency, helping to optimize picking paths and storage strategies to reduce cycle times.

Found in the details of warehouse tasks, inventory records, and material movement transactions.

The User ID uniquely identifies the person or resource responsible for executing a specific warehouse task. This could be a warehouse worker, a shift supervisor, or even an automated system like a robot or conveyor belt controller. Tracking who performed each activity is key to understanding resource allocation and performance.

This attribute enables powerful analyses related to workforce and resource management. It can be used to analyze productivity levels across different users or teams, identify training needs, and ensure adherence to standard operating procedures. By filtering the process map by User ID, analysts can compare how different individuals or teams perform the same process, uncovering best practices or areas needing improvement. It is also crucial for compliance and auditing purposes.

This attribute is vital for resource performance analysis, enabling comparisons of efficiency and quality between different employees, teams, or shifts.

Typically available in transaction logs or task execution records where the user who confirmed or completed the action is recorded.

The Actual Completion Date is the timestamp that marks the finalization of all activities related to a warehouse order. For outbound orders, this is usually the moment the shipment is dispatched from the warehouse. For inbound orders, it might be when all goods are put away and available in inventory.

This attribute is the factual counterpart to the Requested Completion Date and is critical for performance measurement. It is used directly in the calculation of on-time delivery rates and overall order fulfillment cycle time. In root cause analysis, filtering for cases with a late Actual Completion Date allows analysts to drill down into the process flows of delayed orders and identify common patterns or causes.

This provides the actual outcome for on-time analysis. It's used to calculate total cycle time and to determine if an order was early, on time, or late.

This is the timestamp of the final event in the order's lifecycle, such as 'Shipment Dispatched' or 'Warehouse Order Completed'.

The Equipment ID specifies the piece of material handling equipment (MHE) used to perform a warehouse task. This can include forklifts, pallet jacks, handheld scanners, voice-picking headsets, or automated systems like conveyors and sortation machines.

Analyzing process data by Equipment ID is crucial for asset utilization and maintenance planning. It helps answer questions such as which equipment is used most frequently, whether there are equipment-related bottlenecks, and how equipment performance impacts overall task duration. This information can guide decisions on equipment procurement, fleet balancing across the warehouse, and scheduling of preventative maintenance to minimize downtime.

This attribute helps analyze resource utilization for machinery, identifying equipment-related bottlenecks and informing maintenance or procurement decisions.

Often recorded in task confirmation data, where an operator logs the equipment used to perform the work, or from automated system logs.

Order Priority is a classification that signifies the relative importance or urgency of a warehouse order. This often dictates the speed and order in which tasks are assigned and executed. For example, an 'expedited' or 'high' priority order may be processed ahead of 'standard' priority orders, even if it arrived later.

Analyzing the process based on Order Priority is crucial for evaluating service level agreement (SLA) compliance. Process mining can verify if high-priority orders are indeed being processed faster than standard ones. It can also reveal if prioritizing certain orders creates significant delays or bottlenecks for others. This analysis helps in refining prioritization rules and resource allocation strategies to balance speed for urgent orders with overall warehouse efficiency.

This helps analyze if service level agreements are being met by verifying that high-priority orders are processed faster than lower-priority ones.

Usually found in the header data of a warehouse order, often determined by the source sales order or business requirements.

The Product ID, often referred to as a SKU or Material Number, is the unique code assigned to each distinct item stored in the warehouse. This identifier links activities to specific products, allowing for a detailed analysis of how different types of items are handled.

Analyzing the process by Product ID can reveal significant insights. For example, you can compare the handling times for bulky items versus small items, identify products that are frequently associated with process deviations like quality holds, or analyze the fulfillment process for high-demand versus low-demand products. This level of detail is essential for inventory management optimization, warehouse layout planning, and tailoring logistical processes to product characteristics.

It allows for product-level analysis, helping to uncover process variations and bottlenecks related to specific items or product categories.

This information is found in the item-level details of warehouse orders, tasks, and inventory records.

The Shipment ID is a unique key that groups one or more warehouse orders together for a single outbound delivery. A shipment may contain multiple orders going to the same destination or being handled by the same carrier. This identifier links the warehouse activities to the transportation and logistics phase.

This attribute allows for analysis at a consolidated shipment level rather than just individual orders. It can be used to analyze the efficiency of the staging and loading processes, measure the total time from when the first item for a shipment is picked to when the truck departs, and correlate warehouse performance with transportation data. This broader view helps in optimizing the entire outbound logistics chain, not just the steps within the warehouse walls.

It groups multiple warehouse orders into a single delivery, enabling analysis of the consolidated outbound process, including staging and loading efficiency.

Found in shipment or transportation documents, often linked to warehouse orders during the wave planning or allocation stage.

The Warehouse ID is a unique code that identifies the physical facility, such as a distribution center or warehouse, where the logged activities occurred. This is particularly important for organizations that operate a network of multiple logistics sites.

This attribute enables comparative analysis across different locations. By filtering or segmenting the data by Warehouse ID, companies can benchmark performance, compare process efficiency, and identify best practices at high-performing sites that can be replicated elsewhere. It also helps in understanding regional variations in demand, resource utilization, and operational challenges, providing a basis for network-wide strategic decisions.

It allows for performance benchmarking and process comparison across different distribution centers, helping to identify best practices and regional issues.

This is a key organizational data field, typically available in the header of almost all warehouse-related transaction documents.