sort log on start time

Overview

The Sort the log on start time enrichment changes how events are ordered in your process mining dataset when activities have both start and end timestamps. By default, mindzieStudio sorts events by their completion time (end timestamp), which shows the sequence in which activities finished. This enrichment switches the sorting to use start timestamps instead, revealing the actual sequence in which activities began. This distinction is crucial for understanding true process flow, identifying parallel activities, and analyzing resource allocation patterns.

This enrichment is particularly valuable when analyzing processes where the initiation order differs significantly from the completion order, such as manufacturing processes with varying activity durations, healthcare treatments where procedures start and end at different rates, or project management scenarios where tasks overlap extensively. By sorting on start time, you gain insights into when work actually begins, how resources are allocated at the start of activities, and the true dependencies between process steps. This perspective is essential for capacity planning, bottleneck identification at activity initiation points, and understanding the real sequence of work initialization.

Common Uses

Analyze the true initiation sequence of activities in manufacturing processes
Understand resource allocation patterns based on when work actually starts
Identify bottlenecks at activity start points rather than completion points
Detect parallel processing patterns that are obscured by end-time sorting
Analyze queue formation and work initialization patterns
Understand true process dependencies based on activity start sequences
Optimize resource scheduling by seeing when activities actually begin

Settings

This enrichment has no configurable settings. It applies a global change to how the entire event log is sorted, switching from end-time to start-time ordering. The enrichment will only have an effect when your dataset contains activities with both start and end timestamps. If your activities only have single timestamps, this enrichment will not change the log ordering.

Examples

Example 1: Manufacturing Process Analysis

Scenario: In a production line, multiple workstations process items with varying durations. You need to understand the actual sequence of work initiation to optimize resource allocation and identify where queues form at the start of processes.

Settings:

No configuration required

Output: Before enrichment, the event log sorted by end time might show:

Station A completes at 10:30 (started at 09:00, duration: 1.5 hours)
Station B completes at 10:15 (started at 10:00, duration: 15 minutes)
Station C completes at 11:00 (started at 08:30, duration: 2.5 hours)

After enrichment, sorted by start time:

Station C starts at 08:30
Station A starts at 09:00
Station B starts at 10:00

Insights: The start-time view reveals that Station C actually begins processing first despite finishing last, indicating it's a long-duration activity that might need additional resources. Station B, though completing quickly, starts last, suggesting it depends on outputs from other stations.

Example 2: Healthcare Treatment Sequencing

Scenario: In a hospital emergency department, various treatments and procedures have different durations. Understanding when treatments actually begin is crucial for patient flow management and resource planning.

Settings:

No configuration required

Output: End-time sorted view:

Blood test results: 14:30 (started 13:00)
X-ray completed: 14:15 (started 14:00)
Initial assessment: 13:30 (started 13:00)
Treatment administered: 15:00 (started 14:45)

Start-time sorted view:

Initial assessment: 13:00
Blood test: 13:00 (parallel with assessment)
X-ray: 14:00
Treatment: 14:45

Insights: The start-time sorting reveals that blood tests and initial assessments begin simultaneously, indicating efficient parallel processing. The gap between X-ray start and treatment start suggests a waiting period for results, highlighting a potential optimization opportunity.

Example 3: Project Task Management

Scenario: In a software development project with overlapping tasks, you need to understand when work actually begins on different components to better manage developer allocation and identify true task dependencies.

Settings:

No configuration required

Output: Before enrichment (end-time view):

Database design: Day 5 completion
API development: Day 8 completion
Frontend development: Day 10 completion
Testing: Day 12 completion

After enrichment (start-time view):

Database design: Day 1 start
Frontend development: Day 2 start (parallel work)
API development: Day 4 start
Testing: Day 7 start (begins before all development completes)

Insights: Start-time sorting reveals that frontend development begins early in parallel with database design, and testing starts before all development completes, indicating an agile approach with continuous testing rather than a waterfall model.

Example 4: Insurance Claim Processing

Scenario: Insurance claims go through various validation and approval steps with different processing times. Understanding when each step begins helps identify where claims queue up and where parallel processing occurs.

Settings:

No configuration required

Output: End-time sorted events for a claim:

Document verification complete: Day 3, 14:00
Risk assessment complete: Day 2, 16:00
Initial review complete: Day 1, 11:00
Final approval: Day 4, 10:00

Start-time sorted events:

Initial review starts: Day 1, 09:00
Risk assessment starts: Day 1, 10:00 (parallel processing)
Document verification starts: Day 2, 08:00
Final approval starts: Day 4, 09:00

Insights: The start-time view shows that risk assessment begins while initial review is still in progress, indicating parallel processing capabilities. Document verification doesn't start until Day 2, suggesting it depends on outputs from the earlier steps.

Example 5: Warehouse Order Fulfillment

Scenario: In a distribution center, orders go through picking, packing, and shipping stages with varying durations. Understanding start sequences helps optimize worker assignment and identify where orders begin queueing.

Settings:

No configuration required

Output: Standard end-time view:

Order A shipping: 15:00 (started picking at 09:00)
Order B shipping: 14:30 (started picking at 11:00)
Order C shipping: 14:45 (started picking at 08:00)

Start-time sorted view:

Order C picking starts: 08:00
Order A picking starts: 09:00
Order B picking starts: 11:00

Insights: Despite Order B shipping before Order C, Order C actually started processing much earlier, indicating it's a complex order requiring more time. This start-time perspective helps warehouse managers understand true FIFO compliance and identify orders that spend excessive time in the fulfillment process.

Output

The Sort the log on start time enrichment modifies the fundamental ordering of events in your process log without creating new attributes or changing existing data values. The enrichment sets an internal flag (SortLogOnStartTime = true) that affects how all process mining visualizations and analyses interpret the sequence of events.

Impact on Process Mining: After applying this enrichment, all process maps, variant analyses, and sequence-dependent calculations will reflect the start-time ordering. This affects:

Process flow visualizations showing the sequence of activity initiations
Variant detection based on start-time sequences
Throughput time calculations from the start of the first activity
Bottleneck analyses focusing on where activities begin rather than end
Resource utilization views based on when resources begin work

Requirements: This enrichment only affects datasets where activities have both start and end timestamps. For datasets with single timestamps per activity, the enrichment will have no effect. The enrichment is particularly useful for processes imported from systems that track both activity initiation and completion, such as manufacturing execution systems, project management tools, or healthcare information systems.

Reversibility: The sorting change persists for the current analysis session. To return to end-time sorting, you would need to remove this enrichment and reprocess the dataset. Consider saving different versions of your analysis if you need to switch between start-time and end-time perspectives frequently.

Combination with Other Enrichments: This enrichment works seamlessly with all other enrichments and doesn't interfere with calculations or filters. However, be aware that any duration or sequence-based enrichments applied after this will use the start-time ordering, which may produce different results than when using end-time ordering.

Overview

Common Uses

Settings

Examples

Example 1: Manufacturing Process Analysis

Example 2: Healthcare Treatment Sequencing

Example 3: Project Task Management

Example 4: Insurance Claim Processing

Example 5: Warehouse Order Fulfillment

Output

See Also