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Count Activities

Enrichments

Overview

The Count Activities enrichment is a powerful statistical tool that counts how many times specific activities occur within each case in your process. This enrichment creates a new integer attribute that tallies the total number of executions for selected activities, providing quantitative insights into activity frequency and process patterns. It's particularly valuable for identifying cases with unusual activity patterns, measuring compliance with expected process steps, and understanding workload distribution across different process paths.

Unlike simple event counting, Count Activities allows you to focus on specific activities of interest rather than all events in a case. This targeted approach helps you analyze critical process steps, measure the frequency of rework activities, count approval cycles, or track how often specific exceptions occur. The enrichment also supports filtering, allowing you to count activities only within specific segments of your cases based on defined criteria.

Common Uses

  • Track the number of approval cycles in procurement or finance processes to identify bottlenecks
  • Count rework activities in manufacturing processes to measure quality issues and process efficiency
  • Monitor the frequency of escalations in customer service cases to assess service quality
  • Measure how often manual interventions occur in automated processes to identify automation opportunities
  • Count validation or verification steps in compliance-critical processes to ensure proper controls
  • Analyze the number of follow-up activities in sales processes to understand customer engagement patterns
  • Track exception handling activities to identify process variations and improvement opportunities

Settings

Filter: An optional filter that allows you to limit the counting to specific segments of your cases. When a filter is applied, only activities within events that meet the filter criteria will be counted. This is useful for counting activities within specific time periods, for certain case types, or under particular conditions. If no filter is specified, all occurrences of the selected activities across the entire case will be counted.

New Attribute Name: The name for the new integer attribute that will store the activity count for each case. This attribute will be added to your case table and will contain the total number of times the selected activities occurred. Choose a descriptive name that clearly indicates what is being counted, such as "ApprovalCount", "ReworkActivities", or "EscalationFrequency". This field is required.

Activity Names: A multi-select dropdown that allows you to choose which activities to count. You can select one or more activities from the list of all activities present in your dataset. The enrichment will count the total occurrences of all selected activities combined. For example, if you select "Review" and "Approve", the count will include both Review and Approve activities. This field is required and must include at least one activity.

Examples

Example 1: Counting Approval Cycles in Purchase Orders

Scenario: A procurement team needs to identify purchase orders that required multiple approval cycles, as these cases often indicate complex requirements or incomplete documentation that slow down the procurement process.

Settings:

  • New Attribute Name: "Total Approval Steps"
  • Activity Names: ["Manager Approval", "Director Approval", "VP Approval", "CFO Approval"]
  • Filter: None

Output: The enrichment creates a new case attribute "Total Approval Steps" with integer values:

  • Case PO-2024-001: Total Approval Steps = 2 (Manager and Director approval)
  • Case PO-2024-002: Total Approval Steps = 4 (All four approval levels)
  • Case PO-2024-003: Total Approval Steps = 1 (Only Manager approval)
  • Case PO-2024-004: Total Approval Steps = 3 (Manager, Director, and VP approval)

Insights: Cases with 3 or more approval steps can be analyzed to understand why they required elevated approvals. This data helps identify threshold violations, policy exceptions, or opportunities to streamline the approval matrix for routine purchases.

Example 2: Measuring Rework in Manufacturing Quality Control

Scenario: A manufacturing plant wants to measure how often products require rework or re-inspection, as these activities directly impact production efficiency and delivery timelines.

Settings:

  • New Attribute Name: "Rework Count"
  • Activity Names: ["Quality Rejection", "Rework Process", "Re-inspection", "Repair"]
  • Filter: Department = "Production Line A"

Output: The enrichment creates a "Rework Count" attribute showing rework frequency:

  • Case BATCH-5001: Rework Count = 0 (First-time quality pass)
  • Case BATCH-5002: Rework Count = 3 (Quality Rejection, Rework Process, Re-inspection)
  • Case BATCH-5003: Rework Count = 1 (Single Repair activity)
  • Case BATCH-5004: Rework Count = 5 (Multiple quality issues requiring several rework cycles)

Insights: Batches with high rework counts indicate quality issues that need investigation. The plant can correlate rework frequency with factors like shift timing, operator training, or raw material suppliers to identify root causes.

Example 3: Tracking Customer Service Escalations

Scenario: A customer service center needs to monitor how often support tickets are escalated to higher tiers, as excessive escalations indicate either complex issues or insufficient first-tier training.

Settings:

  • New Attribute Name: "Escalation Count"
  • Activity Names: ["Escalate to Tier 2", "Escalate to Tier 3", "Escalate to Supervisor", "Transfer to Specialist"]
  • Filter: Case Type = "Technical Support"

Output: The enrichment produces an "Escalation Count" for technical support cases:

  • Case TICKET-8901: Escalation Count = 0 (Resolved at Tier 1)
  • Case TICKET-8902: Escalation Count = 2 (Escalated to Tier 2, then to Specialist)
  • Case TICKET-8903: Escalation Count = 1 (Single escalation to Tier 2)
  • Case TICKET-8904: Escalation Count = 4 (Complex issue requiring multiple escalations)

Insights: High escalation counts correlate with longer resolution times and lower customer satisfaction. The center can use this data to identify training needs, improve knowledge base articles, or route specific issue types directly to appropriate tiers.

Example 4: Monitoring Manual Interventions in Automated Processes

Scenario: A bank has automated its loan application process but needs to track how often manual interventions are required, as these indicate either system limitations or exceptional cases requiring human judgment.

Settings:

  • New Attribute Name: "Manual Intervention Count"
  • Activity Names: ["Manual Review", "Override Decision", "Exception Handling", "Manual Verification"]
  • Filter: Process Type = "Auto Loan" AND Application Date >= "2024-01-01"

Output: The enrichment creates a "Manual Intervention Count" for recent auto loan applications:

  • Case LOAN-2024-001: Manual Intervention Count = 0 (Fully automated processing)
  • Case LOAN-2024-002: Manual Intervention Count = 2 (Manual Review and Manual Verification)
  • Case LOAN-2024-003: Manual Intervention Count = 1 (Override Decision for special terms)
  • Case LOAN-2024-004: Manual Intervention Count = 3 (Multiple manual steps required)

Insights: Applications with zero manual interventions demonstrate successful automation, while those with multiple interventions highlight opportunities for system enhancement or identify edge cases that require special handling procedures.

Example 5: Analyzing Follow-up Activities in Sales Processes

Scenario: A sales team wants to measure how many follow-up activities occur in their opportunity management process, as this indicates the level of effort required to close deals and helps predict resource needs.

Settings:

  • New Attribute Name: "Follow Up Activities"
  • Activity Names: ["Follow-up Call", "Follow-up Email", "Schedule Meeting", "Send Reminder", "Check-in Contact"]
  • Filter: Opportunity Stage != "Closed Lost"

Output: The enrichment generates a "Follow Up Activities" count for active opportunities:

  • Case OPP-2024-101: Follow Up Activities = 3 (Two emails and one call)
  • Case OPP-2024-102: Follow Up Activities = 7 (High-touch enterprise deal)
  • Case OPP-2024-103: Follow Up Activities = 1 (Quick conversion)
  • Case OPP-2024-104: Follow Up Activities = 5 (Multiple meetings and reminders)

Insights: Opportunities requiring many follow-ups may indicate customer hesitation or complex decision-making processes. The sales team can adjust their approach for high-touch deals and identify which opportunities might benefit from executive involvement or different engagement strategies.

Output

The Count Activities enrichment creates a single new integer attribute in your case table with the name specified in the "New Attribute Name" setting. This attribute contains the total count of how many times the selected activities occurred within each case, subject to any applied filters.

The output attribute characteristics include:

  • Data Type: Integer (Int32)
  • Value Range: 0 to the maximum number of events in any case
  • Column Type: Derived attribute
  • Display Format: Number

The new attribute can be immediately used in:

  • Filters to identify cases with specific activity count ranges (e.g., "Rework Count > 3")
  • Calculators to compute averages, distributions, or correlations with other metrics
  • Dashboards to visualize activity frequency patterns across your process
  • Other enrichments that depend on quantitative activity measurements
  • Process conformance checks to verify expected activity occurrences

Cases where none of the selected activities occur will have a count value of 0, making it easy to identify cases that completely avoided certain process steps. The enrichment preserves the original event data while adding this analytical layer, allowing you to maintain full process transparency while gaining statistical insights.

This documentation is part of the mindzie Studio process mining platform.

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