Your Transportation Management Data Template

This attribute describes a single step in the transportation process, such as 'Shipment Planned', 'Carrier Tendered', or 'Goods Delivered'. These activities form the nodes of the discovered process map, and their sequence defines the process flow for each shipment.

Analyzing the sequence and frequency of these activities is core to process mining. It helps to identify the most common process paths (variants), discover bottlenecks where activities are delayed, and pinpoint rework loops where activities like 'Tender Rejected' are repeated.

It defines the steps of the process, allowing for the visualization of the shipment journey and the identification of process inefficiencies.

Derived from event logs, status change records, or transaction codes within various modules of Blue Yonder TMS. This often requires mapping system events to business-friendly activity names.

The Shipment ID is the central key that links all activities and events related to the movement of goods from a starting point to a destination. Each unique ID represents a complete transportation case, encompassing everything from the initial request to the final payment.

In process mining analysis, this attribute is fundamental for reconstructing the end-to-end journey of each shipment. It allows for the grouping of events like 'Shipment Planned', 'Goods Picked Up', and 'Goods Delivered' into a coherent process flow, enabling the calculation of cycle times and the identification of process variants for individual shipments.

This is the essential Case ID that connects all related transportation events, making it possible to analyze the entire lifecycle of a shipment.

This is a primary key in shipment or load management modules within Blue Yonder TMS. Consult system documentation for the specific table, likely related to shipment headers.

Event Time provides the precise date and time for each activity in the shipment process. It is the chronological backbone of the event log, enabling the ordering of activities and the calculation of durations between them.

In analysis, this timestamp is critical for calculating all time-based KPIs, such as End-to-End Shipment Cycle Time, Customs Clearance Duration, and On-Time Delivery performance. It allows for the identification of when delays occur and how long each stage of the process takes.

This timestamp is essential for ordering events, calculating cycle times, and analyzing process performance over time.

This is typically found alongside status or event records in the transaction logs of Blue Yonder TMS. Each event or status change should have an associated timestamp.

This attribute indicates the freshness of the data. It records the date and time the event log was last updated from Blue Yonder TMS.

In analysis, this is important for understanding the timeliness of the dashboards and KPIs. It allows users to know if they are looking at real-time information or data from a previous period, which is crucial for making informed operational decisions.

It informs users about the recency of the data, which is critical for the relevance and accuracy of the analysis.

This is a metadata field typically generated and added during the data extraction (ETL) process.

This attribute specifies the origin of the event data, which in this case is Blue Yonder TMS. It is particularly useful in environments where data from multiple systems might be combined for a broader process view.

For analysis, it helps in filtering data and understanding its context. Maintaining this information ensures data lineage and is a best practice for data governance.

It provides crucial context about data origin, ensuring traceability and helping to manage data from multiple sources.

This is typically a static value added during the data extraction, transformation, and loading (ETL) process.

This attribute is the timestamp associated specifically with the final delivery activity. It records the precise moment the shipment reached its destination and was confirmed as delivered.

It is a crucial data point for performance measurement. The 'On-Time Delivery Rate' KPI is calculated by comparing this timestamp with the 'RequestedDeliveryDate'. Furthermore, it marks the end point for calculating the 'Overall Throughput to Delivery' KPI.

This timestamp is essential for calculating the on-time delivery rate and measuring the total shipment transit time.

This is the timestamp of the 'Goods Delivered' status update, often received via an EDI message from the carrier or a manual entry in Blue Yonder TMS.

The Carrier Name identifies the third-party company assigned to execute the transportation of goods. This could be a trucking company, an airline, a shipping line, or a freight forwarder.

This attribute is essential for performance analysis, particularly for the 'Carrier Performance Comparison' dashboard. It allows for filtering and segmenting data to compare carriers on metrics like on-time delivery rates, pickup adherence, and average delay durations. This helps in strategic carrier selection and relationship management.

It enables performance comparison and analysis across different carriers to optimize carrier selection and improve service quality.

Found in the shipment or load details within Blue Yonder TMS, often linked from a master data table for carriers.

This attribute specifies the final destination country for the shipment, derived from the consignee's address or the delivery location.

Similar to Origin Country, this attribute is used for geographical segmentation. It allows analysts to compare performance for different trade lanes (e.g., USA to Canada vs. USA to Mexico), analyze delivery challenges in specific countries, and assess the impact of cross-border complexities on cycle times.

It allows for performance analysis by destination, which is crucial for understanding trade lane complexities and regional delivery challenges.

Stored as part of the destination location or consignee address data in the shipment details within Blue Yonder TMS.

This attribute specifies the transportation mode. Common values include Less-than-Truckload (LTL), Full Truckload (FTL), Air Freight, Ocean, and Rail.

In process analysis, the mode of transport is a critical dimension for filtering and comparison. Processes, cycle times, and costs can vary significantly between different modes. For example, the 'Customs Clearance Bottleneck Analysis' is highly relevant for Air and Ocean shipments but less so for domestic truck shipments. Analyzing performance by mode helps tailor improvement initiatives to specific logistics contexts.

It allows for segmented analysis, as different transport modes have distinct processes, costs, and typical cycle times.

This is a standard field in shipment planning and rating modules within Blue Yonder TMS.

This attribute specifies the starting country for the shipment journey. It is derived from the shipper's address or the pickup location details.

In analysis, Origin Country is a powerful dimension for segmenting the data. It helps in understanding regional differences in process performance, carrier availability, and cycle times. For example, it is crucial for analyzing customs clearance times for international shipments.

It enables geographical analysis of process performance, helping to identify regional bottlenecks or variations in efficiency.

Stored as part of the origin location or shipper address data in the shipment details within Blue Yonder TMS.

This attribute captures the target delivery date that the logistics process is aiming to meet. It represents the customer's expectation or the internal service level agreement (SLA) for the shipment.

This date is the baseline for calculating the 'On-Time Delivery Rate' KPI. By comparing the 'ActualDeliveryTime' to the 'RequestedDeliveryDate', the analysis can determine if a shipment was early, on-time, or late. This is fundamental for the 'On-Time Pickup and Delivery Performance' dashboard.

It serves as the primary benchmark for measuring on-time delivery performance and customer satisfaction.

This information typically originates from an upstream system like an ERP or Order Management System and is stored in the shipment request details in Blue Yonder TMS.

Shipment Status indicates the current state of the shipment within its lifecycle, such as 'Planned', 'In-Transit', 'Delivered', or 'Cancelled'. It provides a snapshot of where the shipment is in the process.

In process mining, analyzing the final status of cases is important for outcome analysis. For instance, comparing the process flows of 'Delivered' shipments versus 'Cancelled' shipments can reveal patterns that lead to undesirable outcomes. It also helps in monitoring the active workload by filtering for shipments that are not yet complete.

It provides a quick overview of the shipment's current state and helps differentiate between completed, in-progress, and cancelled shipments.

This is a key field on the shipment header or main status tracking table in Blue Yonder TMS.

This attribute records the exact time the carrier physically collected the shipment from the origin point. It marks the official start of the in-transit phase.

This data point is essential for measuring carrier performance. It is compared against the 'ScheduledPickupTime' to calculate the 'On-Time Pickup Rate' and 'Average Pickup Delay Duration' KPIs. Analyzing deviations helps in identifying issues with specific carriers or pickup locations.

This timestamp is used to accurately measure pickup performance and identify early-stage delays in the transportation process.

This is the timestamp of the 'Goods Picked Up' status update, typically received from the carrier via EDI or entered manually in Blue Yonder TMS.

This attribute captures the reason provided for why a shipment milestone was missed. Examples include 'Weather Delay', 'Customs Hold', or 'Carrier Capacity Issue'. This information is often provided by the carrier.

This is critical for the 'On-Time Pickup and Delivery Performance' dashboard. Instead of just knowing a shipment was late, this attribute explains why. Analyzing the most common delay reasons allows the logistics team to proactively mitigate risks and work with carriers to address recurring problems.

It explains the root cause of delays, enabling proactive risk management and targeted improvements with carriers.

This data is often captured in event or exception management sections of Blue Yonder TMS, frequently populated from carrier EDI updates (e.g., EDI 214).

When a freight bill audit results in a discrepancy, this attribute provides the reason. Examples include 'Incorrect Rate', 'Duplicate Invoice', or 'Missing Proof of Delivery'.

This attribute is key to the 'Proof of Delivery and Billing Accuracy' dashboard and the 'Freight Bill Rework Rate' KPI. Analyzing the frequency of different discrepancy reasons helps to identify the root causes of billing errors, whether they originate from carrier mistakes, contract misalignments, or internal process issues. This allows for targeted actions to reduce invoice rework.

It provides the root cause for billing errors, enabling targeted improvements to reduce freight bill rework and payment delays.

Located in the freight audit and payment module of Blue Yonder TMS, associated with exception or rejection logs.

This boolean attribute is derived by comparing the 'ActualDeliveryTime' with the 'RequestedDeliveryDate'. It is true if the actual delivery is on or before the requested date, and false otherwise.

As a calculated metric, it simplifies analysis and visualization for the 'On-Time Delivery Rate' KPI. It allows for easy filtering and aggregation to create dashboards that show on-time performance percentages over time, by carrier, or by transport mode, directly supporting the 'On-Time Pickup and Delivery Performance' dashboard.

This simplifies on-time performance analysis and allows for quick filtering and aggregation in dashboards and KPIs.

This attribute is not in the source system. It is calculated during the data transformation process using the formula: ActualDeliveryTime <= RequestedDeliveryDate.

This attribute stores the appointment time that was scheduled and agreed upon with the carrier for collecting the shipment. It is a key milestone in the shipment plan.

This timestamp is used as the baseline for calculating the 'On-Time Pickup Rate' and 'Average Pickup Delay Duration' KPIs. Comparing it with the 'ActualPickupTime' helps identify delays at the very beginning of the shipment's journey, which often have a cascading effect on subsequent milestones.

It is the benchmark for measuring on-time pickup performance, a key indicator of carrier reliability and planning accuracy.

Located in the appointment scheduling or load planning modules of Blue Yonder TMS.

This calculated metric measures the total elapsed time for a shipment case. It is typically calculated as the difference between the timestamp of the last event (e.g., 'Payment Processed') and the first event (e.g., 'Shipment Request Received').

This attribute directly supports the 'Shipment End-to-End Cycle Time' KPI and dashboard. Analyzing its distribution helps to understand overall process efficiency, identify outliers (exceptionally long-running cases), and track the impact of improvement initiatives on the total process duration.

It provides a high-level measure of overall process efficiency and is a key metric for identifying long-running or problematic shipments.

This metric is calculated within the process mining tool or during data transformation by subtracting the minimum event time from the maximum event time for each case (ShipmentId).

This attribute identifies the logistics planner, coordinator, or system user responsible for executing a specific event or status change in the TMS. For automated events, this might be a system or service account ID.

Analyzing by user helps to understand workload distribution, individual performance, and training needs. It can reveal if certain users are associated with higher rates of rework or delays, or if specific teams are more efficient than others. This supports resource management and targeted process improvement efforts.

It enables analysis of performance and workload by user or team, helping to identify training opportunities and resource constraints.

This information should be available in the transaction or event logs, often as a 'Changed By' or 'User ID' field associated with each record.