Your Supply Chain Management Data Template

This attribute describes a single step or milestone in the supply chain process. It captures key events like order creation, inventory checks, production stages, and delivery confirmations, forming the sequence of operations for each Logistics Order.

In process mining analysis, the sequence of activities is used to automatically discover the process map, identify deviations from the standard procedure, and pinpoint bottlenecks. It is the foundation for analyzing process flow, conformance, and identifying rework loops, as seen in the Quality Control Rework Analysis dashboard.

It defines the steps of the process, allowing for the discovery and visualization of the actual end-to-end supply chain flow.

This is typically derived from event logs, status change records, or transaction codes within various modules of Kinaxis RapidResponse.

The Event Time records the exact moment an activity took place. This timestamp is crucial for ordering events chronologically and calculating durations between different steps in the process.

Analysis heavily relies on this attribute to calculate all time-based metrics, including cycle times, waiting times, and lead times. It is essential for dashboards like Order Fulfillment Cycle Time and KPIs such as Avg Order-to-Delivery Cycle Time. Accurate timestamps are the basis for understanding process performance and identifying delays.

This timestamp provides the chronological order of events and is the basis for all duration and performance calculations, revealing bottlenecks and delays.

This information is typically found alongside activity data in transaction logs or status update records across Kinaxis RapidResponse modules.

The Logistics Order serves as the primary case identifier, grouping all events related to a single fulfillment journey. It allows for end-to-end tracking of a product's movement through the supply chain, from the initial forecast or customer request to the final proof of delivery.

In process mining, analyzing activities by Logistics Order is fundamental. It enables the visualization of complete process flows, calculation of cycle times, and identification of variations and bottlenecks for each individual order. This is the backbone for dashboards like Order Fulfillment Cycle Time and End-to-End Logistics Variants.

This is the essential key for tracing the entire lifecycle of an order, making it possible to analyze process variations, efficiency, and compliance from start to finish.

This identifier is typically a core field in order management or logistics planning modules within Kinaxis RapidResponse.

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

In any process mining dashboard, this information is critical for context. It tells the user how current the analysis is and when the next data update is expected, ensuring that decisions are made based on timely information.

Informs users about the timeliness of the data, ensuring they understand how current the process analysis is and when it was last refreshed.

This is a metadata field generated and stamped onto the dataset during the data extraction, transformation, and loading (ETL) process.

This attribute identifies the originating system for each event log entry. In a complex landscape, events for a single process might come from multiple systems, such as an ERP, a WMS, and Kinaxis itself.

In analysis, this helps in data validation and understanding the technological landscape of the process. It can also help identify data integration issues or discrepancies between different systems involved in the supply chain.

It provides context about the data's origin, which is crucial for data validation, troubleshooting, and understanding process fragmentation across different IT systems.

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

This attribute records the timestamp when the customer officially received the goods. It marks the final step in the order fulfillment process.

It is the counterpart to the 'Requested Delivery Date' and is used to determine if a delivery was on-time or late. This is a core metric for the On-Time Delivery Performance dashboard and is vital for calculating service level agreements (SLAs) and overall supply chain reliability.

It confirms the completion of the delivery process and is essential for calculating actual delivery performance against customer expectations.

This information often comes from a proof of delivery event, which may be recorded in Kinaxis or an integrated transportation management system (TMS).

This attribute identifies the end customer associated with the logistics order. It is crucial for segmenting process analysis from a customer-centric point of view.

Using the Customer ID, it's possible to analyze On-Time Delivery Performance for specific customers, understand which customers have the most complex order flows, and tailor service improvements. This helps in prioritizing high-value customers and managing customer relationships more effectively.

Allows for customer-centric process analysis, helping to evaluate service levels, delivery performance, and fulfillment complexity for key accounts.

Found in customer order tables or workbooks within Kinaxis RapidResponse, often labeled 'Customer Number' or 'Sold-To Party'.

This attribute specifies the product being handled in the logistics order. It allows for analysis to be segmented by product type, category, or specific item.

Analyzing the process by product is critical for identifying items that face frequent delays, quality issues, or complex fulfillment paths. This is essential for the Demand Forecast Accuracy and Quality Control Rework Analysis dashboards, helping to pinpoint problematic product lines or supply chains.

Enables process analysis specific to product lines or categories, helping to identify which products have inefficient or problematic fulfillment paths.

This is a standard field in order, shipment, and inventory data within Kinaxis RapidResponse, often labeled as 'SKU', 'Material Number', or 'Product ID'.

This attribute stores the date on which the customer expects to receive their order. It serves as the baseline for measuring delivery performance and customer satisfaction.

This date is directly compared against the 'Actual Delivery Date' to calculate the On-Time Delivery Rate KPI and power the On-Time Delivery Performance dashboard. Understanding the gap between requested and actual delivery dates is key to identifying systemic delays and improving customer service.

This is the primary benchmark for measuring on-time delivery performance and is a critical component of customer satisfaction KPIs.

This is a standard field on customer sales orders within Kinaxis RapidResponse.

This attribute identifies the individual or system user responsible for a specific process step. It helps in understanding workload distribution, identifying training needs, and analyzing performance across different users or teams.

In analysis, it can be used to filter the process map to see how specific users perform their tasks or to identify activities that are frequently handled by a small group of specialists. It can also highlight potential automation opportunities for repetitive, manual tasks.

Attributes user responsibility for process steps, enabling analysis of team performance, workload distribution, and adherence to procedures.

Typically found in transaction logs as 'User Name', 'Changed By', or 'Created By' fields within Kinaxis RapidResponse.

This attribute contains the quantity of a product that was predicted to be ordered in a given period. It represents the output of the demand planning process.

This data is essential for the Demand Forecast Accuracy dashboard and the Forecast-to-Actual Order Deviation KPI. By comparing this value with the actual 'Order Quantity', businesses can assess the reliability of their forecasts, which is critical for optimizing inventory levels and production planning.

Serves as the baseline for measuring forecast accuracy, helping to improve inventory management and reduce costs associated with overstocking or stock-outs.

This data originates from the demand planning or forecasting modules within Kinaxis RapidResponse.

This boolean attribute is derived by comparing the 'Actual Delivery Date' to the 'Requested Delivery Date'. It simplifies performance analysis by categorizing every completed order as either on-time or late.

This flag is the primary driver for the On-Time Delivery Performance dashboard and the On-Time Delivery Rate KPI. It allows for easy filtering, aggregation, and visualization of delivery performance across different dimensions like customer, product, or region.

Provides a clear, binary outcome for delivery performance, simplifying the calculation of on-time KPIs and enabling powerful dashboard visualizations.

This attribute is not found in the source system but is calculated during the data transformation phase for process mining.

This boolean attribute identifies activities that occur as part of a rework cycle. It is typically flagged as 'true' when a production or quality control activity is repeated for the same Logistics Order after an initial attempt.

This flag is essential for the Quality Control Rework Analysis dashboard and the Rework Rate KPI. It allows analysts to easily quantify the frequency of rework and isolate the process flows that are causing inefficiencies, increased costs, and longer cycle times.

Directly flags inefficient rework loops within the process, making it easy to quantify the impact of quality issues on cost and cycle time.

This attribute is calculated within the process mining tool by detecting repeated sequences of activities for a given case.

This attribute specifies how goods are moved from one location to another. It can include standard and expedited options.

It is key for the Expedited Order Overview dashboard and the Expedited Order Percentage KPI. Analyzing this attribute helps identify how often costly expedited shipping is used, which can indicate underlying issues in planning or execution. It also supports transportation leg time analysis by comparing the efficiency of different modes.

Identifies transportation methods, which is key to analyzing logistics costs, transit times, and the frequency of expensive expedited shipments.

This information is typically stored in shipment or transportation documents within Kinaxis RapidResponse or an integrated TMS.

This is a case-level metric that measures the end-to-end duration of the entire order fulfillment process. It is calculated by taking the timestamp of the last activity ('Proof Of Delivery Signed') and subtracting the timestamp of the first relevant activity ('Customer Order Received').

This metric is the cornerstone of the Order Fulfillment Cycle Time dashboard and the Avg Order-to-Delivery Cycle Time KPI. It provides a high-level view of the overall efficiency and speed of the supply chain, helping to track improvements and identify systemic delays over time.

Represents the total end-to-end duration of the fulfillment process, serving as a critical KPI for measuring overall supply chain speed and efficiency.

This is not a field in the source system but is a metric calculated at the case level within the process mining software.

This attribute specifies the number of units of a particular product included in a customer order. It is a fundamental piece of transactional data.

In analysis, order quantity is used to compare against forecasted quantities to measure forecast accuracy. It also helps in prioritizing high-volume orders and understanding the scale of production and logistics operations required for different orders.

Crucial for measuring demand forecast accuracy by comparing it to forecasted amounts and for understanding the volume drivers in the supply chain.

This is a standard field in customer order line item data within Kinaxis RapidResponse.

This attribute identifies the specific physical location, such as a factory or production site, involved in the supply chain process. It provides geographical and organizational context to production-related activities.

Analyzing the process by plant allows for performance comparison across different sites. It can help identify which plants are more efficient, which have higher rework rates, and where production bottlenecks are most common, supporting resource utilization analysis.

Provides location context for production activities, enabling performance benchmarking and bottleneck analysis across different manufacturing sites.

This is a common attribute in production, inventory, and logistics data within Kinaxis RapidResponse.

This attribute is the key identifier for procurement activities. It links the request for materials ('Purchase Requisition Created') to the official order sent to a supplier ('Purchase Order Issued') and the subsequent receipt of those materials ('Raw Materials Received').

While the Logistics Order is the case ID, the Purchase Order Number is a critical business document reference that allows for drilling down into specific supplier transactions. It is essential for analyzing the Supplier Delivery Performance dashboard in detail.

Acts as a key reference for all procurement activities, enabling detailed analysis of the purchasing sub-process and supplier performance.

This is a primary field in purchasing and procurement modules or workbooks within Kinaxis RapidResponse.

This attribute captures the specific reason for a quality failure. It provides critical context when a 'Quality Control Passed' activity does not occur, and instead, the process loops back to production for rework.

In the Quality Control Rework Analysis dashboard, this data provides the 'why' behind rework events. Analyzing rejection reasons helps to identify the root causes of quality problems, such as material defects, equipment malfunction, or operator error, enabling targeted improvement actions.

Provides crucial context for quality failures and rework loops, enabling root cause analysis to improve product quality and reduce waste.

This information would be found in quality management modules or tables, linked to quality inspection events.

This attribute identifies the physical resource involved in a process step, particularly in manufacturing. It could be a specific machine, an assembly line, or a packing station.

Analyzing the process by resource is central to the Supply Chain Resource Utilization dashboard. It helps to understand the workload and performance of different resources, identify which machines are bottlenecks, and optimize resource allocation for better throughput and efficiency.

Identifies the specific equipment or line used, which is essential for analyzing resource utilization, capacity constraints, and production bottlenecks.

This data is often found in manufacturing execution systems (MES) that integrate with Kinaxis, or within detailed production planning data.

This attribute identifies the supplier involved in procurement-related activities, such as 'Purchase Order Issued' and 'Raw Materials Received'.

It is essential for the Supplier Delivery Performance dashboard, allowing the business to measure and compare the on-time performance and lead times of different suppliers. This analysis helps in supplier relationship management, identifying reliable partners, and mitigating risks in the inbound supply chain.

Enables performance analysis of suppliers, which is critical for identifying procurement bottlenecks and ensuring a reliable inbound supply chain.

Located in purchasing documents and goods receipt records within Kinaxis RapidResponse, typically named 'Supplier ID' or 'Vendor Number'.

This attribute specifies the storage facility involved in fulfillment activities. It is key to understanding the logistics and inventory management parts of the supply chain.

By analyzing activities per warehouse, businesses can compare the efficiency of their distribution network. This is useful for dashboards related to inventory bottlenecks and fulfillment throughput, helping to identify slow-moving inventory or inefficient warehouse operations.

Pinpoints the specific warehouse or distribution center, allowing for analysis of inventory movement, picking efficiency, and fulfillment speed by location.

Found in inventory management and shipment data, often associated with goods picking and shipping events.