Overview
The Time Between Selected Events calculator measures the time elapsed between two specific events in your process. This targeted calculator helps you analyze durations for critical process segments and understand timing between key milestones.
Common Uses
- Measure the time between two specific activities
- Analyze time between order placement and order fulfillment
- Calculate duration from invoice posted to payment received
- Measure lead times between critical milestones
- Identify delays in specific process segments
Settings
Event Attribute: Select the event attribute to use for filtering events (e.g., 'Activity Name' to measure between activities, or 'Resource' to measure between events performed by specific resources).
From Value: Select the first event that will represent the start time.
First or Last: Specify whether to use the first or last occurrence of the start event in the case.
To Value: Select the second event that will represent the end time.
First or Last: Specify whether to use the first or last occurrence of the end event in the case.
Examples
Example 1: Time from Invoice Entry to Payment
Scenario: You want to calculate the time between when an invoice is entered and when it's finally paid.
Settings:
- Event Attribute: Activity Name
- From Value: Enter Invoice
- First or Last: First
- To Value: Payment
- First or Last: Last (to capture final payment)
Output:
The calculator displays key metrics:
- Mean: Average time between the two events
- Median: Middle value (useful when outliers exist)
- Max: Longest time observed
- Additional statistical measures
Insights: This reveals invoice payment lead times and helps identify slow-paying scenarios.
Example 2: Time from Due Date Miss to Payment
Scenario: You want to calculate how long overdue invoices remain unpaid after missing their due date.
Settings:
- Event Attribute: Activity Name
- From Value: Due Date Missed
- First or Last: First
- To Value: Invoice Paid
- First or Last: First
Output:
The calculator shows metrics including a maximum time of 118 years, clearly indicating a data entry error.
Important Note on Outliers:
As seen in this example, data quality issues can create extreme outliers (like 118 years). In such cases:
- The mean value is heavily affected by outliers
- The median is more informative, representing the middle value unaffected by extreme outliers
- Always review maximum values to identify data quality issues
Insights: This analysis reveals:
- Typical overdue payment duration (use median)
- Data quality issues requiring correction
- Real payment behavior patterns after due dates pass
This documentation is part of the mindzie Studio process mining platform.