add activity from case attribute

Overview

The Add Activity From Case Attribute enrichment creates new activities in your event log based on timestamp values stored in case attributes. This powerful transformation enrichment allows you to convert milestone dates, deadline timestamps, or any other date-based case attribute into visible activities that appear in your process maps, variants, and timeline visualizations.

This enrichment is essential when you have important process milestones captured as case-level attributes (like "Expected Delivery Date", "Contract Start Date", or "Warranty Expiration") that you want to analyze alongside your actual process activities. By converting these timestamps into activities, you can measure deviations between planned and actual timelines, identify delays relative to deadlines, and gain deeper insights into time-based process behavior.

The enrichment intelligently places the new activity at the exact timestamp specified in the case attribute, automatically integrating it into the chronological sequence of existing activities. This makes it possible to calculate durations between attribute-based milestones and actual activities, identify cases where activities occurred before or after expected dates, and visualize the relationship between planned and actual process execution.

Common Uses

Convert expected delivery dates into activities to measure on-time delivery performance
Transform contract start dates or SLA deadlines into visible milestones in process maps
Create activities from planned completion dates to compare planned vs. actual timelines
Convert appointment times or scheduled dates into activities for appointment adherence analysis
Inject warranty expiration dates into process flows to identify post-warranty service activities
Transform check-in times or registration timestamps into process activities for attendance tracking
Create milestone activities from project phase deadlines to track project schedule adherence
Convert promised customer delivery dates into activities to measure promise fulfillment

Settings

Date Attribute Column Name: Select the case attribute containing the timestamp you want to convert into an activity. This attribute must be a DateTime type attribute. The enrichment will use the timestamp value from this attribute as the time when the new activity occurs. If a case does not have a value for this attribute (null value), no activity will be created for that case.

New Activity Name: Enter the name for the new activity that will be created. This name will appear in your process map, variant analysis, and activity lists. Choose a descriptive name that clearly indicates what the activity represents, such as "Expected Delivery Date", "SLA Deadline", or "Contract Start Date". The activity name should be distinct from existing activities to avoid confusion.

New Activity Display Name: Optionally specify a user-friendly display name for the activity if you want it to appear differently in reports and visualizations. If not specified, the Activity Name will be used as the display name.

Expected Order: Specify the expected sequence position for this activity in your process model. This numeric value helps the system understand where this activity logically belongs in the process flow, which is useful for conformance checking and variant comparison. For example, if this represents a deadline that should occur after certain activities, assign an appropriate order number based on your process model.

Examples

Example 1: On-Time Delivery Analysis

Scenario: An e-commerce company tracks promised delivery dates for customer orders as a case attribute. They want to visualize these promised dates as activities in their process map to identify when deliveries occur before or after the promised date, enabling them to measure delivery performance and identify problematic fulfillment patterns.

Settings:

Date Attribute Column Name: "Promised_Delivery_Date"
New Activity Name: "Promised Delivery Deadline"
New Activity Display Name: "Expected Delivery"
Expected Order: 150

Output: A new activity called "Promised Delivery Deadline" is created for each order at the timestamp specified in the Promised_Delivery_Date attribute. In the process map, this activity appears alongside actual delivery activities like "Package Shipped" and "Delivery Complete".

Sample case data before enrichment:

Case ID: ORD-5423, Promised_Delivery_Date: 2024-03-15 17:00:00
Activities: Order Placed (March 10), Payment Confirmed (March 10), Package Shipped (March 12), Delivery Complete (March 16)

After enrichment:

Activities: Order Placed (March 10), Payment Confirmed (March 10), Package Shipped (March 12), Promised Delivery Deadline (March 15 5:00 PM), Delivery Complete (March 16)

Insights: The company can now use duration calculators to measure the time between "Promised Delivery Deadline" and "Delivery Complete", identifying late deliveries as cases where this duration is positive. Process maps reveal that 23% of deliveries occur after the promised deadline, primarily for orders fulfilled from the West Coast warehouse. This enables targeted improvement initiatives for problematic fulfillment centers.

Example 2: SLA Monitoring in IT Support

Scenario: An IT support department has service level agreements with different response time commitments based on ticket priority. The SLA deadline for each ticket is calculated and stored as a case attribute. The team wants to inject these deadlines as activities to monitor SLA compliance and identify at-risk tickets before they breach.

Settings:

Date Attribute Column Name: "SLA_Deadline"
New Activity Name: "SLA Response Deadline"
New Activity Display Name: "SLA Deadline"
Expected Order: 50

Output: For each support ticket, an "SLA Response Deadline" activity is created at the timestamp specified in the SLA_Deadline attribute. This deadline activity appears chronologically among the actual support activities.

Sample case data:

Ticket ID: TKT-8821, Priority: High, SLA_Deadline: 2024-06-20 14:30:00
Activities: Ticket Created (June 20 10:00), Auto-Assigned (June 20 10:05), SLA Response Deadline (June 20 14:30), First Response (June 20 15:15), Ticket Resolved (June 21 09:00)

Insights: The team can now easily identify SLA breaches by filtering for cases where "First Response" occurs after "SLA Response Deadline". Analysis reveals that 18% of high-priority tickets breach the SLA, with most breaches occurring during peak hours (12-2 PM) when the team is understaffed. This data supports a request for additional staffing during peak periods.

Example 3: Contract Milestone Tracking

Scenario: A professional services firm manages long-term client contracts with multiple milestone dates stored as case attributes (contract start, first deliverable due, second deliverable due, contract end). They want to visualize these milestone dates as activities to compare planned contract timelines with actual work performed.

Settings (run this enrichment 4 times for different milestones):

Configuration 1:

Date Attribute Column Name: "Contract_Start_Date"
New Activity Name: "Contract Start Milestone"
Expected Order: 10

Configuration 2:

Date Attribute Column Name: "Deliverable_1_Due_Date"
New Activity Name: "Deliverable 1 Deadline"
Expected Order: 50

Configuration 3:

Date Attribute Column Name: "Deliverable_2_Due_Date"
New Activity Name: "Deliverable 2 Deadline"
Expected Order: 100

Configuration 4:

Date Attribute Column Name: "Contract_End_Date"
New Activity Name: "Contract End Milestone"
Expected Order: 200

Output: The enrichment creates four new milestone activities for each contract based on the stored deadline dates. These milestone activities appear in the process timeline alongside actual work activities like "Requirements Gathering", "Design Review", "Deliverable 1 Submitted", etc.

Sample case data for Contract C-445:

Before enrichment: Requirements Gathering (Jan 5), Design Review (Jan 20), Deliverable 1 Submitted (Feb 10), Testing Complete (Feb 25), Deliverable 2 Submitted (Mar 8)
After enrichment: Contract Start Milestone (Jan 1), Requirements Gathering (Jan 5), Design Review (Jan 20), Deliverable 1 Deadline (Feb 5), Deliverable 1 Submitted (Feb 10), Testing Complete (Feb 25), Deliverable 2 Deadline (Mar 1), Deliverable 2 Submitted (Mar 8), Contract End Milestone (Mar 15)

Insights: The firm can now visualize how actual work aligns with contractual deadlines. Analysis reveals that Deliverable 1 was submitted 5 days late, but Deliverable 2 was submitted 7 days early, resulting in overall on-time contract completion. This pattern analysis helps identify which types of deliverables consistently run late and require better estimation.

Example 4: Appointment Adherence in Healthcare

Scenario: A medical clinic schedules patient appointments and stores the scheduled appointment time as a case attribute. They want to create an activity for the scheduled time to measure how long patients wait before being seen and identify patterns of appointment delays.

Settings:

Date Attribute Column Name: "Scheduled_Appointment_Time"
New Activity Name: "Scheduled Appointment"
New Activity Display Name: "Appointment Time"
Expected Order: 20

Output: A "Scheduled Appointment" activity is created at the exact time the patient was scheduled to be seen, allowing comparison with the actual "Patient Called to Exam Room" activity.

Sample case data:

Patient Visit PV-9923, Scheduled_Appointment_Time: 2024-09-12 10:00:00
Activities: Patient Check-In (9:45 AM), Scheduled Appointment (10:00 AM), Patient Called to Exam Room (10:23 AM), Doctor Enters Room (10:30 AM), Visit Complete (10:52 AM)

Insights: The clinic can calculate the duration between "Scheduled Appointment" and "Patient Called to Exam Room" to measure appointment delays. Analysis shows an average wait time of 18 minutes beyond scheduled time, with morning appointments (8-10 AM) being more punctual than afternoon appointments. This indicates the clinic falls behind schedule as the day progresses, supporting the need for schedule buffer adjustments.

Example 5: Manufacturing Production Planning

Scenario: A manufacturing company plans production runs and stores the planned start date for each job as a case attribute. They want to inject this planned start date as an activity to compare planned vs. actual production schedules and identify jobs that start late.

Settings:

Date Attribute Column Name: "Planned_Production_Start"
New Activity Name: "Planned Start Date"
New Activity Display Name: "Scheduled Start"
Expected Order: 15

Output: The enrichment creates a "Planned Start Date" activity for each production job, positioned at the timestamp specified in the Planned_Production_Start attribute.

Sample case data:

Job ID: JOB-3391, Planned_Production_Start: 2024-11-05 06:00:00
Activities: Materials Requisitioned (Nov 1), Materials Received (Nov 3), Planned Start Date (Nov 5 6:00 AM), Production Setup (Nov 6 8:00 AM), Production Started (Nov 6 9:30 AM), Quality Check (Nov 7), Production Complete (Nov 7)

Insights: By measuring the time between "Planned Start Date" and "Production Started", the company identifies that 34% of jobs start more than 24 hours late. Root cause analysis reveals that late material deliveries account for 60% of delayed starts, while equipment availability issues account for 25%. This data drives improvements in material planning and equipment maintenance scheduling.

Output

The Add Activity From Case Attribute enrichment creates new event rows in your event log:

New Activity Records: For each case that has a non-null value in the specified date attribute, a new event row is created with:

Activity Name: The name you specified in the "New Activity Name" setting
Timestamp: The DateTime value from the selected case attribute
Case Association: Linked to the same case as the source attribute
Expected Order: The order value you specified for conformance and variant analysis

Data Type: The new activities are standard event log activities that appear in all activity-based analyses, including:

Process maps and variants (showing the new activity in chronological sequence)
Activity frequency tables and statistics
Timeline visualizations
Duration calculations (as start or end points for duration enrichments)
Conformance checking (using the expected order value)

Null Handling: Cases where the specified date attribute is null or empty will not have the new activity created. This means the number of occurrences of the new activity may be less than the total number of cases if some cases lack the source attribute value.

Chronological Integration: The new activities are automatically positioned in the correct chronological order based on their timestamp, appearing before activities that occur later and after activities that occur earlier. This ensures accurate duration calculations and process flow visualization.

Multiple Enrichments: You can run this enrichment multiple times with different source attributes to create multiple milestone activities from various date attributes in your dataset, as demonstrated in the Contract Milestone Tracking example above.

Integration with Other Enrichments: Once created, the new activities can be used in:

Duration enrichments to calculate time between milestone and actual activities
Conformance enrichments to check if activities occur in the expected order relative to milestones
Activity filters to segment cases based on milestone-related patterns
Calculators to measure deviation between planned and actual timelines

Overview

Common Uses

Settings

Examples

Example 1: On-Time Delivery Analysis

Example 2: SLA Monitoring in IT Support

Example 3: Contract Milestone Tracking

Example 4: Appointment Adherence in Healthcare

Example 5: Manufacturing Production Planning

Output

See Also