Your Warehouse Management Data Template

This attribute records the discrete steps or milestones within the warehouse management process. Examples include 'Goods Picked from Storage', 'Packing Initiated', and 'Shipment Dispatched'. Each activity represents a specific action performed on the warehouse order.

This is a critical attribute for process mining, as it defines the nodes in the process map. Analyzing the sequence, frequency, and duration of these activities allows for the visualization of process flows, identification of deviations from the standard procedure, and pinpointing of bottlenecks where work is delayed.

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

Derived from transaction codes, event logs, or status update tables within Manhattan SCALE that track the progress of warehouse orders.

This attribute provides the precise date and time for each activity recorded in the process. It is the chronological backbone of the event log, establishing the sequence and timing of all warehouse operations for a given order.

The Event Time is essential for all time-based process mining analysis. It is used to calculate cycle times between activities, measure the total duration of a process, identify delays, and analyze process performance over different time periods. Accurate timestamps are crucial for building a reliable process map and deriving meaningful performance metrics.

This timestamp is crucial for sequencing events correctly and calculating all duration-based metrics, such as cycle times and lead times.

Located in event log tables or transaction records alongside the activity information in Manhattan SCALE. Fields are often named something like 'created_ts', 'event_timestamp', or 'status_change_date'.

The Warehouse Order serves as the primary case identifier, grouping all related activities for a single logistical process from start to finish. This allows for the end-to-end tracking of an order's lifecycle within the warehouse, whether it involves receiving goods, putting them away, picking, packing, or shipping.

In process mining analysis, this attribute is fundamental for reconstructing the journey of each order. By linking all events to a specific Warehouse Order, analysts can visualize process flows, measure cycle times for individual orders, and identify variations or bottlenecks that affect fulfillment efficiency.

It is the essential key for linking all related warehouse activities into a single, cohesive process instance, enabling end-to-end analysis.

This identifier is typically found in core order management tables within Manhattan SCALE, such as the order header table.

This attribute indicates the date and time when the data was last extracted from Manhattan SCALE and loaded into the process mining tool. It reflects the freshness of the data being analyzed.

This information is vital for users to understand the timeliness of the analysis. It helps them know if they are looking at real-time data or a snapshot from a specific point in time, which is crucial for making informed operational decisions and for reporting purposes.

It informs users about the freshness of the data, ensuring they understand the time frame covered by the analysis and reports.

This timestamp is typically generated and stored by the ETL tool or data pipeline during the data loading process.

This attribute identifies the source application where the event data was generated. For this process, the value would typically be 'Manhattan SCALE'.

In an environment with multiple integrated systems, this field helps differentiate data sources. It provides context for the data's origin, which can be important for data governance, troubleshooting, and understanding potential variations in data capture processes across different platforms.

It provides essential context about the data's origin, which is critical for data governance, validation, and integration efforts in complex IT landscapes.

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

This attribute records the measured quantity of goods at a key checkpoint, such as goods receipt or picking. It represents what was physically handled, which may differ from what was planned.

Comparing the Actual Quantity to the Planned Quantity is essential for identifying discrepancies. This is the basis for the 'Order Quantity Discrepancy Trends' dashboard and the related KPI. Tracking these differences helps pinpoint issues in supplier accuracy, inventory records, or picking errors, which are critical for maintaining inventory integrity.

It is crucial for identifying discrepancies between planned and executed quantities, highlighting potential inventory accuracy issues or operational errors.

Found in transaction detail records for activities like receiving, counting, or picking in Manhattan SCALE.

The End Time marks the completion of an activity. When paired with a Start Time (EventTime), it allows for the precise calculation of the processing time for individual tasks. Not all events have a distinct end time, but for those that do, such as 'Picking' or 'Packing', this data is highly valuable.

In analysis, End Time is used to calculate the 'ProcessingTime' metric, which is crucial for understanding resource efficiency and task duration. This helps identify which specific activities are taking the longest, contributing to overall cycle time and potential delays.

It enables the precise calculation of processing time for individual activities, helping to identify which tasks consume the most time and resources.

This may be available in the same transaction logs as the start time, in a separate field like 'completed_ts' or 'end_time'. If not directly available, it can sometimes be inferred from the start time of the subsequent activity.

This attribute, often known as an SKU (Stock Keeping Unit), identifies the specific item involved in a warehouse activity. A single warehouse order may contain multiple materials, each handled in separate line items or tasks.

Analyzing the process by Material ID can reveal product-specific issues. For instance, certain items might be more prone to quality inspection failures, picking errors, or longer handling times due to their size, weight, or storage requirements. This helps in optimizing storage strategies and handling procedures for different types of products.

It enables product-level analysis to identify if certain items are associated with process delays, errors, or rework.

Located in the order line item tables in Manhattan SCALE, linked to the main warehouse order.

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

This attribute allows for segmenting the process analysis. By filtering on Order Type, one can compare the performance of different processes, such as the receiving process versus the shipping process. This is crucial because their steps, resources, and performance targets are often very different, and analyzing them together can be misleading.

It allows for the separation and comparison of distinct processes, like inbound vs. outbound, which have different flows and performance expectations.

Found in the order header data in Manhattan SCALE. The field may be named 'order_type', 'transaction_type', or similar.

This attribute represents the service level agreement (SLA) or target delivery date for an outbound warehouse order. It is the deadline that the warehouse operations are measured against for on-time performance.

This date is a critical benchmark for performance evaluation. It is used in the 'Shipment On-Time Performance' dashboard and the 'On-Time Shipment Rate' KPI to determine whether an order was fulfilled on time. Analyzing orders that miss this date helps identify systemic causes of delays and improve customer satisfaction.

It serves as the primary benchmark for measuring on-time shipment performance and adherence to customer service level agreements.

Typically stored in the order header table in Manhattan SCALE, often populated from an upstream ERP or Order Management System.

This attribute captures the ID of the user responsible for executing a specific warehouse task, such as picking, packing, or putaway. It links process activities to the human resources involved.

Analyzing performance by User/Operator ID is essential for understanding resource utilization and individual or team efficiency. It helps identify top performers, training needs, and workload distribution. This data is key for the 'Resource Utilization by Operator' dashboard and related KPIs, enabling managers to optimize staffing and task assignments.

It links process performance to specific individuals or teams, enabling analysis of workload, productivity, and resource allocation.

This information is typically recorded in transaction logs in Manhattan SCALE, often in fields like 'user_id', 'operator', or 'executed_by'.

This attribute is the timestamp marking the final step of the warehouse process for an order, such as 'Shipment Dispatched'. It represents the factual completion time of the fulfillment process.

This date is directly compared against the 'Requested Completion Date' to calculate on-time shipment performance. It is the factual data point that confirms when the warehouse's responsibility for the order ended. Analyzing this attribute helps in accurately measuring fulfillment cycle times and performance against SLAs.

It provides the factual timestamp of order completion, used to calculate actual cycle times and measure performance against requested dates.

This corresponds to the timestamp of the final activity in the process, such as 'Shipment Dispatched' or 'Warehouse Order Completed'.

This attribute identifies the logistics partner or carrier (e.g., FedEx, UPS, DHL) assigned to an outbound shipment. It links the warehouse process to the downstream transportation leg.

Analyzing performance by carrier can reveal important insights into the supply chain. It helps determine if certain carriers are associated with longer staging times, frequent delays, or specific handling requirements. This information can be used to evaluate carrier performance and optimize logistics partnerships.

It connects warehouse operations to logistics partners, allowing for performance analysis by carrier to identify potential transportation-related bottlenecks.

Found in the shipment or transportation planning tables within Manhattan SCALE, often linked to the order header.

This attribute specifies the piece of material handling equipment (MHE) or technology used to perform a warehouse task. This could include specific forklift IDs, pallet jack numbers, or handheld RF scanner IDs.

This data is valuable for resource utilization analysis beyond just human operators. It helps in understanding the usage patterns of expensive equipment, planning for maintenance, and ensuring that the right tools are available when needed. It directly supports KPIs like 'Resource Utilization (Picking)' by providing another dimension for analysis.

It enables analysis of equipment utilization and its impact on process efficiency, helping to manage maintenance schedules and investment in MHE.

Consult Manhattan SCALE documentation or system logs. This might be tracked in task execution records if the system is configured to capture it.

This boolean attribute is derived by comparing the 'Actual Completion Date' with the 'Requested Completion Date'. It is true if the order was completed on time and false if it was late.

This attribute simplifies performance analysis by allowing for easy filtering and aggregation. It is the basis for calculating the 'On-Time Shipment Rate' KPI and powers dashboards that track adherence to customer SLAs. It quickly separates compliant orders from non-compliant ones, enabling root cause analysis on the late orders.

It simplifies performance reporting by converting date comparisons into a simple true/false flag, making it easy to calculate the on-time shipment rate.

Calculated by comparing 'ActualCompletionDate' <= 'RequestedCompletionDate'. This logic is applied in the process mining tool.

This boolean attribute identifies instances of rework, such as a 'Goods Picked from Storage' activity being performed more than once for the same material within a single order. It is derived by analyzing patterns in the process flow.

Flagging rework is crucial for identifying process inefficiencies and errors. It helps quantify the 'Picking Rework Rate' KPI and allows analysts to isolate and investigate cases with repeated steps. Understanding the drivers of rework is key to reducing operational costs and improving process quality.

It flags inefficient loops and repeated work within the process, which are often hidden sources of delays and increased operational costs.

This is a calculated attribute, typically derived within the process mining tool by defining rules that detect specific repeated activity patterns (e.g., self-loops or short loops).

This metric calculates the total duration for each warehouse order, from the 'Warehouse Order Created' event to the 'Warehouse Order Completed' event. It is a key performance indicator that measures the end-to-end efficiency of the entire warehouse fulfillment process.

In dashboards and analysis, this attribute provides a high-level view of overall performance. It is used to track trends, identify outliers (extended cycle times), and benchmark performance over time. It directly supports the 'Overall Order Fulfillment Cycle Time' dashboard and the corresponding KPI.

This is a critical KPI that measures the end-to-end speed and efficiency of the warehouse, directly impacting customer satisfaction and operational costs.

This is a calculated field, derived by taking the difference between the timestamps of the first and last events for each Warehouse Order.

This attribute indicates the quantity of an item that was expected to be received, picked, or handled according to the original warehouse order or task instruction. It is the baseline against which the actual-world operation is measured.

This value is used in conjunction with 'Actual Quantity' to calculate discrepancies. Frequent differences between planned and actual quantities can signal problems with supplier shipments, inventory accuracy, or picking processes, making this a key attribute for quality and accuracy analysis.

It acts as the baseline for measuring quantity accuracy, helping to identify discrepancies that impact inventory and order fulfillment.

Located in order line item or task detail tables within Manhattan SCALE.

This attribute records the result of the 'Quality Inspection Performed' activity. It indicates whether the received goods met the required quality standards or if further action is needed.

This is a critical attribute for quality management analysis. It helps in tracking the 'Quality Control Compliance Rate' not just in terms of adherence but also in outcomes. Analyzing failure rates by supplier or material can help improve procurement decisions and reduce the downstream costs associated with poor quality goods.

It provides the outcome of quality checks, enabling analysis of supplier quality, product issues, and the effectiveness of the inspection process.

Consult Manhattan SCALE documentation. This would likely be stored in tables related to quality management or receiving tasks.

This metric is calculated as 'Actual Quantity' minus 'Planned Quantity'. A non-zero value indicates a discrepancy in the amount of goods handled versus what was expected. The value can be positive (overage) or negative (shortage).

This attribute is the foundation of the 'Order Quantity Discrepancy Rate' KPI. It quantifies the magnitude of errors in receiving or picking, providing a clear metric for dashboards that track inventory accuracy. Analyzing the trends and root causes of these discrepancies is vital for improving operational precision.

It directly quantifies inventory and order fulfillment errors, providing a clear metric for tracking accuracy and the financial impact of discrepancies.

This is a calculated field derived in the process mining tool by the formula: ActualQuantity - PlannedQuantity.

This calculated metric measures the duration of a critical part of the inbound process: the time between the 'Goods Received and Counted' activity and the 'Goods Put Away in Storage' activity. It quantifies the efficiency of the receiving dock and putaway operations.

This duration is a key input for the 'Goods Receipt to Putaway Cycle' dashboard and its corresponding KPI. High values for this metric can indicate bottlenecks at the receiving dock, delays in creating putaway tasks, or inefficient travel paths, all of which delay inventory availability.

It isolates and measures the efficiency of a critical inbound process step, highlighting bottlenecks that delay inventory availability.

This is a calculated field, derived by finding the time difference between the 'Goods Received and Counted' and 'Goods Put Away in Storage' events for a given case.

The Shipment ID is a higher-level identifier that consolidates one or more warehouse orders that are being transported together on the same vehicle or as part of the same consignment. It links individual orders to a specific transportation event.

In analysis, the Shipment ID allows for a broader view of logistics operations. It can be used to analyze the efficiency of the consolidation process at the staging and loading phases, measure on-time performance at a shipment level, and understand how order consolidation impacts overall lead times.

It groups multiple orders into a single shipping event, enabling analysis of the consolidation, staging, and loading processes.

Found in transportation or shipment management tables within Manhattan SCALE. This ID typically links back to multiple warehouse orders.

This attribute identifies the physical location (e.g., Aisle 5, Bin 3, Level C) involved in a storage or retrieval activity. It provides spatial context to the warehouse process.

Analyzing activities by storage location is crucial for optimizing warehouse layout and material flow. It can help identify frequently accessed locations, inefficient travel paths during picking, or areas prone to congestion. This data supports analysis of putaway and picking efficiency, revealing opportunities to improve storage strategies.

It provides spatial context to warehouse movements, enabling analysis of layout efficiency, travel times, and picking path optimization.

Contained within task-level data for putaway and picking activities in Manhattan SCALE, in fields such as 'location_id', 'bin_code', or 'source_location'.