Your Supply Chain Management Data Template

The Activity Name describes a distinct step in the supply chain lifecycle, such as 'Order Allocated', 'Goods Picked', or 'Shipment Dispatched'. These activities form the nodes in the discovered process map.

Analyzing these activities allows for the visualization of the process flow, identification of frequent pathways, and measurement of time spent in each stage. It is fundamental to understanding what happens during the logistics order journey and is used to identify rework, bottlenecks, and deviations from the standard process.

This attribute defines the steps in the process, forming the basis of the process map and enabling analysis of process flow and bottlenecks.

This is typically derived by mapping event codes or status changes from various transaction tables in Manhattan Associates to user-friendly activity names.

The Event Timestamp, or Start Time, records the precise moment an activity was executed. This timestamp is critical for ordering events chronologically and calculating durations between activities.

In process mining, this data is used to construct the timeline of each case, calculate cycle times, identify delays, and analyze process performance over time. Accurate timestamps are the foundation for nearly all time-based process analysis and KPI calculations, such as lead time and on-time performance.

This attribute provides the chronological order of events and is essential for calculating all time-based metrics, such as cycle times and delays.

This information is found in transaction log tables alongside the corresponding event or status update in Manhattan Associates.

The Logistics Order is the central tracking number that connects all related activities from customer order placement to final delivery. Each Logistics Order represents a single fulfillment journey, allowing for a complete analysis of the order's lifecycle, including procurement, warehousing, shipment, and delivery.

In process mining, this attribute is used to group all related events into a single case. Analyzing processes by Logistics Order enables the calculation of end-to-end cycle times, identification of common process variants, and detection of bottlenecks or deviations affecting specific orders.

This is the essential Case ID that groups all related events, making it possible to trace the entire journey of a single order from start to finish.

This is the primary key in the main order management tables within Manhattan Associates. Consult system documentation for the specific table, likely related to order headers.

This attribute provides the date and time of the most recent data pull from the source system. It is a metadata field that helps users understand the freshness of the data they are analyzing.

In process mining dashboards, this information is vital for communicating the timeliness of the insights. It ensures that stakeholders are aware of the data's recency and can make decisions accordingly, preventing analysis based on outdated information.

Indicates the freshness of the data, ensuring that analysis and decisions are based on up-to-date information.

This timestamp is generated and added during the data extraction, transformation, and loading (ETL) process.

This attribute identifies the source information system where the event data originated. For this context, it will typically be 'Manhattan Associates', but it can also differentiate between different modules or integrated systems.

It is important for data governance and for understanding the context of the data, especially when data from multiple systems is combined. It helps in tracing data lineage and troubleshooting data quality issues.

Identifies the origin of the data, which is crucial for data governance, validation, and when merging data from multiple enterprise systems.

This is typically a static value added during the data extraction process to label the dataset with its origin.

This attribute specifies the transportation mode used to move the goods from the warehouse to the destination. Examples include Full Truckload (FTL), Less Than Truckload (LTL), air freight, ocean freight, or parcel.

This dimension is critical for the 'Transport Cost & Efficiency Analysis' dashboard. It allows for comparing the cost and speed of different transport modes and helps in calculating the 'Expedited Shipping Frequency' KPI by identifying when more expensive, faster modes are used.

Helps analyze transportation costs and efficiency, and identifies reliance on expensive expedited shipping options.

Located in the shipment or transportation planning tables within Manhattan Associates TMS module.

Order Type categorizes logistics orders based on their nature or priority. Common types include customer orders, stock transfer orders, or return orders. Each type may have a different expected process flow and service level agreement (SLA).

This attribute is crucial for comparative analysis. By filtering or segmenting the process by Order Type, analysts can understand how different types of orders are handled, compare their cycle times, and identify if certain order types are more prone to delays or exceptions. It directly supports the 'Order Type Cycle Time Comparison' dashboard.

Allows for comparing the performance and process flows of different kinds of orders, which often have unique paths and SLAs.

Located in the order header data within Manhattan Associates. Consult system documentation for the specific field name.

The Product SKU is a unique code that identifies a specific product. A single logistics order can contain multiple SKUs.

Analyzing the process at the product level helps to identify if certain items are associated with process inefficiencies, such as longer picking times, higher rework rates, or frequent stockouts. This attribute is essential for the 'Inventory Replenishment & Stockout Risk' dashboard to analyze reordering timeliness by product.

Enables product-level analysis to uncover issues like stockouts, picking delays, or quality problems tied to specific items.

Found in the order line item tables in Manhattan Associates. This will require joining from the order header to the line item details.

This attribute stores the target date by which the goods should be delivered to the customer or receiving location. It serves as a benchmark for measuring on-time delivery performance.

In analysis, this date is compared against the actual delivery timestamp ('Proof of Delivery Received') to determine if an order was on-time, early, or late. It is a critical component for calculating KPIs like 'Customer On-Time Delivery Rate' and 'Supplier On-Time Delivery Rate', and for building related performance dashboards.

This is the baseline for measuring on-time performance, which is a critical KPI for customer satisfaction and supplier management.

Found in the order header or order line item tables in Manhattan Associates. Consult system documentation.

This attribute identifies the specific employee or system user responsible for performing a given process step. It can be a unique user ID, a name, or a system account name for automated tasks.

Analyzing by user helps in understanding workload distribution, resource performance, and identifying training needs. It is key for dashboards related to resource load and throughput, and for investigating process deviations that may be user-specific.

Enables analysis of resource performance, workload distribution, and helps identify which users or teams are involved in process exceptions.

Usually found in transaction tables alongside the event data, often as a 'UserID' or 'ChangedBy' field. Consult Manhattan Associates documentation.

The Carrier Name identifies the logistics partner or company that physically transports the goods. This could be a major shipping company or a local courier.

Analyzing by carrier is key to evaluating their performance in terms of on-time delivery, cost, and efficiency. It is a primary dimension for the 'Transport Cost & Efficiency Analysis' dashboard, enabling comparison of cycle times and costs across different carriers.

Enables performance evaluation of transportation partners, helping to optimize carrier selection based on cost and reliability.

Found in the shipment or freight order tables, typically within the TMS module of Manhattan Associates.

This attribute identifies the end customer receiving the shipment. It can be an individual or a business entity.

Customer information is vital for analyzing service levels and identifying patterns specific to certain customers. It supports the 'Customer On-Time Delivery Rate' dashboard by allowing segmentation of delivery performance by customer, which can reveal if delays are concentrated with particular accounts or regions.

Allows for segmenting analysis by customer, which is key to understanding customer-specific issues and measuring service levels.

Located in the order header data, often linked via a customer ID. Consult Manhattan Associates documentation.

This attribute specifies the final delivery point for the goods. It could be a customer's distribution center, a retail store, or an end-consumer's address. It is often represented by a city, state, or postal code.

Paired with the Origin Location, this attribute is essential for the 'Origin-Destination Lead Time Matrix' dashboard. Analyzing lead times by destination helps to identify delivery challenges in specific regions and evaluate the efficiency of the distribution network.

Helps in analyzing delivery performance across different geographic areas and identifying regional bottlenecks or carrier issues.

This is the ship-to address information found in the order header or shipment details in Manhattan Associates.

This boolean attribute is derived by comparing the 'Proof of Delivery Received' event timestamp with the 'RequestedDeliveryDate'. It flags each order as either on-time (true) or late (false).

This attribute directly powers the 'Customer On-Time Delivery Rate' KPI and dashboard. It simplifies the analysis by providing a clear, binary outcome for each case, making it easy to filter for late orders and investigate the root causes of delays.

Directly measures customer service levels and simplifies the analysis of on-time performance by categorizing every order as on-time or late.

This is not a field in the source system. It is calculated during data transformation by comparing the actual delivery timestamp to the requested delivery date.

This attribute specifies the starting point of the shipment, such as a distribution center, manufacturing plant, or supplier location. It is typically represented by a location code or name.

Origin and destination data are fundamental for logistics network analysis. This attribute supports the 'Origin-Destination Lead Time Matrix' dashboard, helping to visualize and analyze lead times for different shipping lanes and identify regional or facility-specific bottlenecks.

Crucial for logistics network optimization and identifying performance variations between different shipping lanes or facilities.

This information is typically stored in the shipment or order header tables within Manhattan Associates.

The Shipment ID is a reference number for a consolidated group of goods being transported together. A single shipment can include multiple logistics orders, especially if they are going to the same destination.

While the Logistics Order is the case ID, the Shipment ID is an important contextual attribute. It allows for analysis of activities at the shipment level, such as 'Carrier Assigned' or 'Shipment Dispatched', and helps in understanding consolidation efficiency and transportation planning.

Links orders that are shipped together, enabling analysis of transportation efficiency and consolidation strategies.

Generated and stored in the transportation and shipment planning tables within Manhattan Associates.

This attribute identifies the vendor or supplier from whom goods or raw materials are procured. It is relevant for logistics orders that are initiated by a purchase order.

Analyzing by supplier is essential for evaluating vendor performance. It allows for the creation of supplier scorecards, monitoring on-time delivery rates, and identifying which suppliers are frequently associated with delays in the 'Goods Received' step. This directly supports the 'Supplier On-Time Delivery Performance' dashboard.

Enables supplier performance analysis, helping to identify reliable partners and pinpoint sources of inbound supply chain delays.

Linked from purchase order data tables within Manhattan Associates, often requiring a join from the logistics order to the purchase order header.

This attribute represents the total cost incurred for the logistics order. It can be broken down into components like transportation cost, warehousing cost, and handling fees. The level of detail depends on the source data.

Cost data is invaluable for value-based process mining. It supports the 'Transport Cost & Efficiency Analysis' dashboard by providing the financial context to cycle times. By analyzing costs, businesses can identify high-cost process variants and make data-driven decisions to optimize for both speed and expense.

Provides a financial dimension to the process, allowing for analysis of cost drivers and the financial impact of inefficiencies.

Cost data may come from multiple tables in Manhattan Associates or require integration with a financial system. Consult documentation for freight cost or order costing tables.

This attribute identifies the specific warehouse facility responsible for fulfilling the order. For multi-warehouse operations, this is a critical dimension for analysis.

It directly supports the 'Warehouse Picking Bottleneck Analysis' dashboard by allowing performance to be compared across different facilities. Analysts can identify which warehouses are more efficient, which have longer picking cycle times, and where resources may need to be reallocated.

Enables comparative performance analysis across different warehouses to identify facility-specific bottlenecks and best practices.

This is typically part of the order or shipment data in Manhattan Associates WMS, indicating the fulfilling location.

This metric measures the time elapsed between the 'Goods Picked' activity and the 'Goods Packed' activity. It isolates the performance of a critical part of the warehouse fulfillment process.

This calculated attribute is the foundation for the 'Warehouse Picking Cycle Time' KPI and the 'Warehouse Picking Bottleneck Analysis' dashboard. It allows for direct measurement and monitoring of picking efficiency, helping to identify bottlenecks and the impact of any process improvement initiatives in the warehouse.

Isolates the performance of the warehouse picking process, making it possible to specifically target and measure improvements in this critical area.

This is a calculated metric. It is derived by taking the time difference between the timestamps of the 'Goods Packed' and 'Goods Picked' activities for each Logistics Order.