Overview
The Cases with Identical Event Dates filter identifies cases where multiple activities occurred on the same calendar day, regardless of the specific time. This filter helps you analyze temporal clustering patterns in your processes, identify intensive work periods, or distinguish between cases with concentrated activity versus those spread across multiple days. Unlike the timestamp filter, this filter compares only the calendar date, ignoring the time of day.
Common Uses
- Identify cases with concentrated daily activity indicating intensive processing
- Find batch processing patterns where multiple steps occur on the same day
- Analyze work intensity by identifying cases with same-day activity clustering
- Separate rush-processed cases from normal multi-day cases
- Detect cases where multiple events were completed in a single work session
- Compare processing patterns between concentrated and distributed workflows
Settings
Include or Exclude Cases: Choose whether to include cases that have same-day activities or exclude them.
- Include cases with same-day activities: Returns only cases where at least two events occurred on the same calendar day
- Exclude cases with same-day activities: Returns only cases where all events occurred on different calendar days
Examples
Example 1: Identifying Express Orders
Scenario: Your order fulfillment process normally spans multiple days (Order Received on Day 1, Processing on Day 2, Shipped on Day 3). However, express orders are rushed through the entire workflow in a single day. You want to identify these express cases for performance analysis.
Settings:
- Include cases with same-day activities
Result:
The filter returns all cases where multiple fulfillment steps occurred on the same calendar day. For example, Case #EXP-1234 shows "Order Received" at 9:00 AM, "Payment Processed" at 9:15 AM, "Picked" at 10:30 AM, and "Shipped" at 2:00 PM, all on October 15, 2024. If 300 out of 5,000 orders were processed same-day, those 300 cases are returned.
Insights: These cases represent your express or rush processing workflow, which operates differently from standard multi-day fulfillment. By analyzing these separately, you can measure express service performance, identify bottlenecks in rush processing, and calculate the true capacity of same-day fulfillment.
Example 2: Analyzing Normal Multi-Day Workflows
Scenario: You want to analyze your standard loan approval process, which typically spans several days with proper review periods. You need to exclude rush cases where multiple steps were completed on the same day, focusing only on cases with proper daily distribution.
Settings:
- Exclude cases with same-day activities
Result:
The filter returns only cases where all activities occurred on different calendar days. For example, Case #LOAN-5678 shows "Application Submitted" on Oct 10, "Document Review" on Oct 11, "Credit Check" on Oct 12, and "Final Approval" on Oct 13. If 4,500 out of 5,000 loans followed the normal multi-day pattern, those 4,500 cases are returned.
Insights: By excluding same-day cases, you can analyze your standard workflow without noise from expedited processing. This provides accurate insights into normal processing times, proper review periods, and typical bottlenecks when cases progress through your intended multi-day workflow.
Example 3: Detecting Bulk Processing Days
Scenario: Your invoice processing system normally handles invoices individually across multiple days. However, at month-end, accounting staff often bulk-process multiple steps for many invoices on the same day. You want to identify cases processed during these intensive bulk sessions.
Settings:
- Include cases with same-day activities
Result:
The filter identifies cases where multiple processing steps (Invoice Received, Validation, Approval, Payment Scheduled) occurred on the same calendar day. For example, during month-end on October 31, 150 invoices show all steps completed on that single day, while throughout the rest of October, only 20 invoices had same-day processing. Those 170 cases with same-day activity are returned.
Insights: This reveals your bulk processing patterns and helps distinguish between normal daily processing and intensive batch sessions. You can analyze these patterns separately, optimize bulk processing workflows, and understand the impact of concentrated processing on quality and accuracy.
Example 4: Measuring Patient Journey Duration
Scenario: Your healthcare process tracks patient journeys through Emergency Department visits. You want to identify cases where the entire visit (Triage, Examination, Treatment, Discharge) occurred within a single calendar day versus cases requiring overnight stays or multi-day care.
Settings:
- Include cases with same-day activities
Result:
The filter returns all ED visits where all activities happened on the same calendar day. For example, Patient #12345 was triaged at 2:00 PM, examined at 2:30 PM, treated at 3:15 PM, and discharged at 4:45 PM, all on October 15. If 2,800 out of 3,000 ED visits were same-day, those 2,800 cases are returned.
Insights: Most ED visits should be same-day cases, so this helps you identify the 200 cases that required multi-day care or overnight observation. By analyzing each group separately, you can understand the characteristics of same-day versus extended care cases and optimize resource allocation accordingly.
Output
This filter operates at the case level and filters entire cases based on calendar date analysis:
- Include mode: Returns only cases containing at least two events on the same calendar day
- Exclude mode: Returns only cases where all events occurred on different calendar days
- Comparison uses calendar dates only (ignores time of day)
- Case and event attributes are preserved
- Event sequences and all other properties remain unchanged
- More lenient than timestamp comparison (events at different times on the same day count as same-day)
Use this filter to analyze temporal clustering patterns, identify concentrated versus distributed workflows, and separate rush processing from normal multi-day cases.
This documentation is part of the mindzie Studio process mining platform.