Your Inventory Management Data Template

This attribute describes a specific step or milestone in the inventory management process. Each activity represents a distinct action performed on the inventory batch, like receiving, inspecting, moving, or shipping it.

Analyzing the sequence and frequency of these activities is the foundation of process mining. It helps visualize the process flow, identify deviations from the standard procedure, and pinpoint activities that are causing delays or rework. For example, analysis can reveal if 'Cycle Count Performed' frequently occurs after a failed picking attempt.

It defines the steps of the process, allowing for the visualization and analysis of the inventory journey from start to finish.

This is typically derived from event logs, transaction codes, or status change records within Manhattan Active Inventory. It may require mapping from technical codes to user-friendly names.

This attribute provides the date and time for the start of each recorded activity. It is essential for ordering events chronologically and for calculating durations between different process steps.

In analysis, the start time is used to calculate key performance indicators like put-away cycle time, quality inspection lead time, and overall stock dwell time. By analyzing timestamps, organizations can identify when work is happening, measure performance against schedules, and uncover time-based inefficiencies in the warehouse.

This timestamp is fundamental for sequencing events correctly and calculating all time-based performance metrics and KPIs.

Every transaction or event record in Manhattan Active Inventory should have an associated timestamp field, often named something like 'CREATE_DTTM' or 'EVENT_TIMESTAMP'.

The Inventory Batch or Lot number groups 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 mining, this attribute is crucial for reconstructing the end-to-end journey of each inventory unit. It allows for the analysis of process variants, cycle times, and bottlenecks affecting specific batches, such as identifying batches that experience long quality inspection delays or frequent internal movements before being shipped.

This is the core identifier that connects all related inventory events into a single process instance, enabling end-to-end analysis of the inventory lifecycle.

This identifier is typically found in inventory detail tables or lot master data within Manhattan Active Inventory. Consult system documentation for specific table and field names.

This attribute records the date and time the data was pulled from the source system. It is a metadata field that is crucial for understanding the freshness of the analysis.

In dashboards and reports, this timestamp informs users how up-to-date the underlying data is. It helps manage expectations about data latency and is essential for scheduling data pipeline refreshes.

Ensures users are aware of the data's freshness, which is critical for making timely and informed operational decisions.

This timestamp is generated and added to the dataset by the data extraction, transformation, and loading (ETL) tool or script.

This attribute identifies the source application or module that generated the event data. In a modern logistics environment, data might come from the core WMS, a yard management system, or a transportation management system.

Specifying the source system is important for data governance and for understanding the context of the data. For instance, knowing an event originated from an automated system versus a manual entry portal can be crucial for automation analysis.

Provides essential context about the data's origin, which is critical for data validation, governance, and understanding system interactions.

This is typically a static value added during the data extraction process to identify the origin of the dataset.

This attribute provides the date and time for the completion of each recorded activity. While some events are instantaneous (StartTime equals EndTime), others have a duration, such as a quality inspection or a picking task.

The end time is necessary for calculating the processing time of individual activities. This allows for detailed analysis of which specific tasks are consuming the most time within the overall process, enabling targeted improvement efforts.

Enables the calculation of individual activity durations, helping to pinpoint exact tasks that are causing process delays.

This may be a separate field in the source system (e.g., 'END_DTTM') or could be the same as the start time for instantaneous events. Consult Manhattan Active Inventory documentation for event data structure.

This attribute represents the quantity of the product associated with an inventory event, such as the number of items received, moved, picked, or adjusted.

Quantity is a critical measure for understanding the scale of operations and for calculating several key KPIs. It is used to determine the Manual Adjustment Volume Ratio, Scrappage Rate by Quantity, and Picking Throughput. Analyzing quantity can reveal if large or small orders have different process characteristics.

Provides a measure of volume for each activity, which is essential for calculating throughput, rates, and other volume-based KPIs.

Transaction tables in Manhattan Active Inventory will contain a quantity field associated with each movement or event. It may be named 'QTY', 'QUANTITY', or 'UNITS'.

The Movement Reason Code provides context for why an inventory event occurred. It is particularly important for non-standard activities like internal transfers, adjustments, returns, or scrappage.

This attribute is crucial for root cause analysis. For instance, in the Inventory Discrepancy Overview dashboard, this code would explain why adjustments were made (e.g., 'Damaged Goods', 'Cycle Count Correction', 'Theft'). It is also used to analyze the drivers behind internal stock transfers and scrappage.

Explains the 'why' behind inventory movements and adjustments, enabling powerful root cause analysis for discrepancies, scrappage, and transfers.

This is often a field in transaction tables associated with specific movement types. Look for fields like 'REASON_CODE' or 'MOVEMENT_TYPE' in Manhattan Active Inventory.

The Stock Keeping Unit, or SKU, is a distinct code used to track a product. It allows for the differentiation of items based on their characteristics, such as size, color, and brand.

In process mining, analyzing by SKU helps identify product-specific issues. For instance, it can reveal if certain SKUs have longer quality inspection times, are more prone to damage and scrappage, or are frequently involved in inventory adjustments. This is a key attribute for dashboards like Put-away Cycle Time Analysis and Inventory Scrappage Trends.

Allows for product-level analysis, helping to identify if process issues are concentrated on specific items or product categories.

The SKU is a fundamental field in item master data and inventory transaction tables. Look for fields named 'SKU', 'ITEM_ID', or 'PRODUCT_CODE'.

This attribute captures the user ID or name of the person responsible for performing a given task, such as picking, packing, or adjusting inventory. It can also represent an automated system or bot ID for automated tasks.

Analyzing performance by user is critical for identifying training opportunities, recognizing high-performing individuals, and understanding workload distribution. It is a key dimension in dashboards related to picking and packing performance and for investigating manual adjustment hotspots.

Links process activities to specific users, enabling performance analysis, workload balancing, and identification of training needs.

This information is typically stored alongside transaction data in fields like 'USER_ID', 'EXECUTED_BY', or 'RESOURCE_ID'. Consult Manhattan Active Inventory documentation.

This attribute specifies the location, for example a zone, aisle, or bin, associated with an inventory event. It provides the geographical context for stock movements and activities within the warehouse.

This is a vital dimension for analysis, used in nearly every dashboard. It helps identify bottleneck areas, compare performance across different locations, and investigate the root causes of inventory discrepancies. For example, it can show if a particular zone has a high rate of picking errors or slow put-away times.

Provides spatial context to inventory events, enabling analysis of bottlenecks, efficiency, and issues tied to specific physical locations.

Location data is a core part of any WMS and should be available in inventory transaction tables, often in fields like 'LOCATION_ID', 'BIN_CODE', or 'ZONE'. Consult Manhattan Active Inventory documentation.

This metric measures the time spent actively working on a specific task. It is calculated by subtracting the Event Start Time from the Event End Time for a single activity. For instantaneous events, this duration is zero.

Analyzing processing time helps identify which specific steps in the process are the most time-consuming. For instance, it can differentiate between a long picking cycle time caused by travel versus a long time spent at the pick face. This helps focus improvement efforts more precisely.

Measures the duration of individual tasks, helping to isolate and analyze the most time-consuming steps within the overall process.

This is a calculated field, derived using the EventStartTime and EventEndTime attributes from the raw data.

This attribute describes the state of the inventory at a given point in time. It indicates whether the stock is available for allocation to orders, undergoing quality control, blocked for other reasons, or in transit.

Tracking changes in inventory status can provide valuable insights. For example, analyzing the time spent in an 'On Hold' or 'In Inspection' status helps quantify delays in inventory availability. This attribute is useful for understanding the reasons behind long stock dwell times.

Indicates the availability of stock for fulfillment, helping to analyze delays caused by non-available statuses like quality inspection or holds.

This is a key field in inventory balance or stock level tables within Manhattan Active Inventory. Look for 'STATUS_CODE' or 'INVENTORY_STATUS'.

This boolean attribute indicates if a put-away task met its target completion time. It is calculated by comparing the actual put-away cycle time (from 'Put-away Initiated' to 'Put-away Completed') against a predefined business target, for example, 4 hours.

This attribute is used to calculate the Put-away On-Time Rate KPI. It simplifies analysis by allowing users to easily filter for late put-aways and investigate their root causes, such as the warehouse location, SKU category, or user involved. This helps in monitoring and enforcing operational efficiency standards.

Directly measures adherence to internal service levels for stock put-away, helping to quickly identify and analyze process failures.

This is a calculated field. It requires a business rule or SLA definition (e.g., 'Target Put-away Time = 4 hours') to be applied to the calculated cycle time.

This attribute represents the financial value of the inventory associated with an event, calculated as Item Quantity multiplied by the unit cost. It is particularly relevant for financial impact analysis.

Item Value is essential for the Inventory Scrappage Trends dashboard, as it allows for quantifying the financial loss associated with scrapped goods. It helps prioritize cost-reduction efforts by focusing on high-value items that are frequently written off. It also adds a financial dimension to inventory adjustments and discrepancies.

Translates inventory movements into financial impact, which is critical for quantifying the cost of scrappage, discrepancies, and excess stock.

This is often derived by multiplying the Item Quantity from the transaction table by the unit cost from the item master or financial data. Unit cost may be stored in fields like 'STANDARD_COST' or 'UNIT_PRICE'.

The SKU Category provides a way to group similar products for aggregated analysis. This could be based on product type, storage requirements (e.g., refrigerated), or sales velocity (e.g., A, B, C items).

This attribute allows for higher-level insights than individual SKU analysis. It helps answer questions like, 'Do electronics take longer to put away than other categories?' or 'Is scrappage concentrated in our seasonal goods category?'. It is used in the Put-away Cycle Time and Quality Inspection Lead Time dashboards.

Facilitates aggregated analysis by grouping products, helping to reveal process trends and issues that are common to entire product families.

This is typically part of the item master data, linked to the SKU. Consult Manhattan Active Inventory's product or item master configuration.

Stock Dwell Time is a key performance indicator that measures how long inventory sits idle in the warehouse. It is calculated as the time difference between the 'Put-away Completed' event and the 'Goods Issue Recorded' event for a specific inventory batch.

Monitoring this KPI helps businesses optimize inventory levels and improve cash flow. Long dwell times can indicate overstocking, slow-moving products, or inefficiencies in order fulfillment. Reducing dwell time is often a primary goal of inventory management.

Measures inventory holding time, directly supporting the strategic goal of optimizing inventory levels and reducing carrying costs.

This is a calculated field, derived by finding the 'Put-away Completed' and 'Goods Issue Recorded' events for a given case and subtracting their timestamps.

This attribute identifies the supplier from whom the inventory batch was received. This information is most relevant at the beginning of the inventory lifecycle, specifically during goods receipt and quality inspection.

Analyzing processes by supplier is key to managing supplier performance. For example, the Quality Inspection Lead Time dashboard uses this attribute to determine if inventory from certain suppliers consistently experiences longer inspection delays, which could indicate quality issues or documentation problems.

Links inventory processes to specific suppliers, enabling analysis of supplier-related performance, such as quality inspection delays.

This information is usually available on the purchase order or advance shipping notice associated with the goods receipt event. It may require joining with purchasing data.

While the Inventory Batch/Lot tracks the item, the Task Identifier tracks the specific work instruction given to a user or system. For example, a single batch of 100 items might be put away via two separate tasks of 50 items each.

This attribute allows for a more granular analysis of operational efficiency. It can link a 'Picking Task Created' event to its corresponding 'Picking Completed' event, enabling precise measurement of task execution time. This is useful for analyzing picking and put-away performance at the individual task level.

Provides a granular link between task creation and completion events, enabling precise measurement of discrete work assignments.

Task management or execution tables in Manhattan Active Inventory will contain a unique ID for each task. Look for 'TASK_ID' or 'WORK_ORDER_ID'.

The Unit of Measure (UoM) provides context for the Item Quantity attribute. It specifies whether the quantity refers to individual items, cases containing multiple items, or entire pallets.

Understanding the UoM is crucial for accurate analysis, especially when comparing different products or transactions. It ensures that KPIs like picking throughput are calculated correctly and consistently, preventing the misinterpretation of data where quantities might be recorded in different units.

Provides essential context to quantity fields, ensuring that metrics and comparisons are accurate and meaningful.

This is a standard field in item master data and is usually carried over into transaction tables. Look for fields like 'UOM' or 'UNIT'.

This attribute identifies the specific facility, such as a distribution center or warehouse, where the inventory activity is taking place. It provides a higher-level grouping than the specific Warehouse Location (bin, aisle).

Analyzing by warehouse is essential for companies with multiple facilities. It allows for performance comparison between sites, helping to identify best practices at high-performing warehouses and areas for improvement at others. It is a key dimension for the Warehouse Activity Throughput dashboard.

Enables high-level comparison of inventory process performance across different physical sites or distribution centers.

This information is typically part of the location master data or can be derived from the Warehouse Location code. Look for fields like 'WH_ID' or 'SITE_CODE'.