Your Transportation Management Data Template

This attribute describes a single step in the transportation lifecycle, such as 'Shipment Planned', 'Goods Picked Up', or 'Goods Delivered'. Each activity represents a distinct business event that moves the shipment closer to completion.

Analyzing the sequence and duration of these activities is the core of process mining. It allows for the discovery of the actual process flow, identification of bottlenecks between steps, and measurement of adherence to standard operating procedures. Variations in the activity flow can highlight exceptions, rework, or deviations from the optimal path.

It defines the steps in the process map, allowing for visualization and analysis of the shipment's journey, bottlenecks, and deviations.

This information is derived from tracking event tables or status change logs within Oracle Transportation Management. The specific activity name may be concatenated from event codes and descriptions.

This timestamp marks the exact moment an activity, like 'Carrier Assigned' or 'Proof of Delivery Received', was recorded in the system. Timestamps are fundamental for process mining as they establish the chronological sequence of events for each shipment.

The accuracy of these timestamps is critical for all time-based analysis. They are used to calculate cycle times between activities, measure process durations, identify delays, and evaluate performance against service level agreements. Analyzing Event Time helps pinpoint where time is being lost in the process.

This timestamp is essential for ordering events chronologically and calculating all performance metrics, such as cycle times and delays.

Sourced from the timestamp fields associated with events in OTM's tracking event or status history tables.

The Shipment ID uniquely identifies a single movement of goods from a consignor to a consignee. This attribute is the cornerstone of the process analysis, grouping all related events such as planning, booking, pickup, transit, delivery, and invoicing into a single case.

In process mining, analyzing events by Shipment ID allows for a complete end-to-end view of the transportation lifecycle. It is used to calculate key metrics like total cycle time, on-time delivery performance, and cost per shipment, providing a comprehensive understanding of process efficiency and customer service levels.

This is the essential Case ID that connects all process steps, enabling a complete analysis of the entire transportation journey for each individual shipment.

This is a primary key in Oracle Transportation Management, typically found in shipment or order management tables. Consult OTM documentation for specific table and field names.

This attribute identifies the logistics partner assigned to execute the transportation of the goods. Examples include major freight companies, local couriers, or ocean liners.

Analyzing performance by carrier is a primary goal of transportation process mining. This attribute allows for segmenting shipments to compare carriers on metrics like on-time delivery rate, cost per shipment, transit time, and frequency of issues like delays or billing discrepancies. These insights are vital for carrier selection, contract negotiation, and performance management.

Enables performance benchmarking across different carriers, which is critical for optimizing carrier selection, negotiation, and cost reduction.

Found in shipment details within OTM, linked from the assigned carrier or service provider records. It may be stored in tables related to shipments and service providers.

When a shipment is delayed, this attribute provides the reason, such as 'Weather', 'Customs Hold', 'Port Congestion', or 'Mechanical Failure'. This information is captured from carrier updates or manual entries in OTM.

This attribute is essential for root cause analysis of late deliveries. By categorizing and counting delay reasons, businesses can identify the most frequent problems in their supply chain. This enables targeted improvement initiatives, such as selecting more reliable carriers for certain lanes or improving documentation to speed up customs.

Provides critical insight into the root causes of late shipments, enabling targeted actions to mitigate common issues and improve on-time performance.

Typically captured in tracking event details within OTM. It may be associated with specific exception or delay event codes.

This attribute captures the target delivery timestamp for a shipment. It represents the commitment made to the customer and serves as the primary benchmark for measuring on-time performance.

In process mining, this date is compared against the actual delivery timestamp (from the 'Goods Delivered' activity) to determine if a shipment was on-time or late. This is the foundation for the On-Time Delivery Rate KPI and is essential for the Delivery Performance Overview dashboard. Analyzing deviations helps identify systemic causes of delays and improve customer satisfaction.

This is the primary benchmark for measuring on-time delivery performance, a critical KPI for customer satisfaction and operational excellence.

This information is typically stored on the order or shipment level within OTM, often in fields related to delivery dates or appointments.

This attribute represents the total financial cost associated with a shipment, including carrier charges, fuel surcharges, and other accessorial fees. It is a key measure of the financial performance of the transportation process.

Analyzing Shipment Cost allows for the calculation of the Average Carrier Cost per Shipment KPI. It can be segmented by carrier, route, transportation mode, or shipment type to identify cost-saving opportunities. It is also used in the Freight Bill Discrepancy Analysis to compare planned versus actual costs.

Provides the financial data needed to analyze transportation spend, identify cost-saving opportunities, and measure the financial impact of process inefficiencies.

This information is typically found in the financial or costing sections of a shipment in OTM, often populated after freight rating and settlement.

This attribute categorizes the shipment by the mode of transportation. Common modes include Full Truckload (FTL), Less Than Truckload (LTL), Ocean, Air, and Rail. This is a fundamental characteristic of a shipment that influences cost, transit time, and complexity.

In analysis, Transportation Mode is a critical dimension for segmentation. It allows for comparing process performance and costs across different modes. For instance, it can help identify which modes are more prone to in-transit delays or have higher costs per mile, supporting strategic decisions about logistics networks.

Allows for performance and cost analysis to be segmented by mode of transport, revealing insights specific to truck, air, ocean, or rail logistics.

This is a standard attribute on the shipment level in OTM, often found in a dedicated 'mode' or 'transport type' field.

For international shipments, this attribute identifies the third-party agent or internal team tasked with handling customs documentation and procedures. The performance of this agent can significantly impact the overall transit time.

Analyzing the Customs Clearance Cycle Time by Customs Agent helps to benchmark their performance. It can identify which agents are most efficient in specific countries or for particular commodity types. This insight is valuable for selecting the best customs partners and for working with underperforming agents to improve their processes.

Enables performance measurement of customs brokers, helping to identify the most efficient partners and reduce customs-related delays.

This information may be stored as a service provider or party role on the international shipment details in OTM.

This attribute specifies the final destination country of the shipment, based on the consignee's location. It complements the Origin Country for geographical process analysis.

Similar to the origin, analyzing by Destination Country is vital for understanding performance differences across various trade lanes. It is particularly important for analyzing customs clearance times, final-mile delivery performance, and region-specific carrier effectiveness. This helps in tailoring logistics strategies to the requirements of different destination markets.

Crucial for analyzing performance on specific trade lanes and understanding challenges related to customs, carriers, and regulations in destination markets.

Derived from the address details of the shipment's destination location or consignee, stored in OTM's location master data.

This calculated metric measures the total elapsed time for a shipment case. It is typically calculated as the time difference between the 'Transportation Request Received' activity and the 'Goods Delivered' activity.

This attribute is the basis for the Average End-to-End Cycle Time KPI. It provides a holistic measure of the efficiency of the entire transportation process. Analyzing this duration helps to identify long-running shipments and provides a baseline for measuring the impact of process improvement initiatives.

Provides a single, critical measure of overall process velocity, helping to identify long-running shipments and track improvements over time.

This attribute is not in the source system. It is calculated at the case level during process mining analysis or data transformation.

This boolean attribute is set to 'true' if the freight bill received from the carrier does not match the expected cost calculated in OTM. Discrepancies can arise from incorrect rates, accessorial charges, or other billing errors.

This attribute is the basis for the Freight Bill Discrepancy Rate KPI. Analyzing the frequency of these flags by carrier, lane, or shipment type helps to identify the root causes of billing errors. This supports initiatives to improve data accuracy, streamline the audit process, and prevent overpayments.

Directly supports the Freight Bill Discrepancy Analysis by flagging shipments with billing errors, helping to reduce overpayments and improve financial controls.

This is typically a status or flag set during the freight settlement and invoice matching process within OTM.

This boolean attribute is derived by comparing the timestamp of the 'Goods Delivered' activity with the 'Requested Delivery Date'. It is 'true' if the delivery was on time or early, and 'false' if it was late.

This attribute directly powers the On-Time Delivery Rate KPI and is a key component of the Delivery Performance Overview dashboard. It simplifies the analysis of delivery performance by providing a clear, binary outcome for each shipment, making it easy to filter, aggregate, and visualize on-time versus late shipments.

This calculated flag simplifies the measurement of the On-Time Delivery KPI and allows for easy filtering and analysis of on-time versus late shipments.

This attribute is not in the source system. It is calculated during data transformation by comparing the 'Goods Delivered' activity timestamp against the 'RequestedDeliveryDate' attribute.

This attribute is a boolean flag that is set to 'true' if a shipment undergoes a significant re-routing event after it has already been planned and booked. This could be due to customer request, unexpected disruptions, or optimization opportunities.

This attribute directly supports the Shipment Rerouting Rate KPI and the Re-routing and Expediting Insights dashboard. Tracking the frequency of re-routes helps to identify underlying issues in planning or execution. It highlights operational instabilities and associated costs, enabling managers to find ways to minimize costly and disruptive changes.

Helps quantify the frequency and impact of operational exceptions like re-routes, highlighting inefficiencies in the planning and execution process.

This flag is derived by detecting specific re-routing events in the event log or by comparing the final route against the initially planned route.

This attribute records when the data was most recently pulled from Oracle Transportation Management. It reflects the freshness of the data being analyzed, not the time the business event occurred.

This timestamp is crucial for understanding the timeliness of the process mining analysis. It helps users know if they are looking at real-time information or a snapshot from a specific point in time. It is essential for managing data refresh cycles and ensuring that decisions are based on current information.

Indicates the freshness of the data, ensuring users are aware of how current the analysis is and when the next data refresh is expected.

This is typically a system-generated timestamp added during the data ingestion or ETL process.

This attribute specifies the starting country of the shipment, based on the shipper's location. It is a key piece of geographical information used for logistical analysis.

Analyzing processes by Origin Country helps in understanding regional variations in performance. For example, customs clearance times, carrier costs, and transit durations can vary significantly depending on the country of origin. This segmentation is crucial for managing international logistics and identifying region-specific bottlenecks.

Enables geographical analysis of the transportation process, helping to identify region-specific bottlenecks, costs, and performance variations.

Derived from the address details of the shipment's source location or shipper, which is stored in OTM's location master data.

This attribute provides a high-level summary of where the shipment is in the overall process. Unlike an activity, which is a point-in-time event, the status represents the state of the shipment case.

While process mining focuses on the flow of activities, the overall status is useful for filtering cases and for providing context in dashboards. For example, an analysis might focus only on 'Completed' shipments or investigate why many shipments are stuck in an 'In Transit' status for an unusually long time.

Provides a high-level summary of a shipment's progress, which is useful for filtering and segmenting cases in analysis.

Available on the main shipment record in OTM, representing its current state in the workflow.

This attribute provides a business-specific categorization for the shipment, for example 'Standard', 'Expedited', 'Hazardous Materials', or 'Inter-company'. This classification helps in applying different business rules, priorities, or analytical views.

Using Shipment Type as a dimension in process mining allows for comparing processes for different kinds of shipments. This can reveal if certain types are more prone to delays, have higher costs, or follow non-standard process paths. These insights help in tailoring processes to better handle the specific needs of each shipment type.

Allows for the segmentation of analysis based on business context, helping to understand if different types of shipments follow different processes or have different performance levels.

This is typically a configurable field or attribute on the shipment or order record in OTM.

This attribute specifies the source application where the data was generated. In a complex logistics environment, data may come from OTM, a warehouse management system (WMS), a carrier's system via EDI, or other integrated platforms.

Understanding the source system is important for data governance, quality assessment, and troubleshooting. It helps in understanding the context of the data and can explain variations in data granularity or timeliness. For analysis, it can be used to filter for events originating only from a specific, trusted system.

It provides crucial context about data origin, which is important for data validation and for analyses in multi-system environments.

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

This attribute identifies the individual user or system agent that executed a specific event, such as 'Shipment Planned' or 'Carrier Assigned'. This information provides accountability and insight into resource involvement in the process.

In process mining, analyzing activities by user helps to understand workload distribution, identify training needs, and compare the performance of different users or teams. It can also be used to distinguish between activities performed manually versus those executed automatically by the system.

Provides accountability and enables analysis of workload and performance by individual user or team.

Sourced from user ID fields (e.g., INSERT_USER) associated with status changes or events in OTM's history or log tables.