wrong activity order

Overview

The Wrong Activity Order enrichment identifies cases where two specific activities occur in an incorrect sequence, marking them as conformance violations in your process. This powerful conformance checking tool helps organizations ensure that critical process steps follow the prescribed order, detecting when activities that should occur in a particular sequence are executed out of order. By creating conformance attributes and marking affected cases, this enrichment enables you to quantify process compliance, identify training needs, and discover systemic issues that lead to incorrect activity sequencing.

This enrichment goes beyond simple sequence checking by providing flexible severity levels and rule grouping capabilities, allowing you to categorize different types of ordering violations based on their business impact. Whether you're ensuring regulatory compliance, maintaining quality standards, or optimizing process efficiency, the Wrong Activity Order enrichment helps you identify and quantify cases where the expected activity flow is not followed. The enrichment creates both individual rule attributes and group-level attributes, making it easy to analyze conformance at different levels of granularity.

Common Uses

Detect when approval activities occur after execution activities in procurement processes
Identify cases where quality checks happen after product shipment in manufacturing
Monitor compliance violations where verification steps are skipped or performed out of order
Track instances where payment is processed before order confirmation in e-commerce
Identify medical procedures performed before required diagnostic tests in healthcare
Detect regulatory violations where required reviews occur after document submission
Monitor training compliance where certification occurs before completion of required modules

Settings

Activity 1: Select the first activity in the expected sequence from the dropdown list of all activities in your dataset. This is the activity that should occur first in the correct process flow. For example, in a purchase order process, this might be "PO Approval" which should happen before "PO Released". The dropdown shows all unique activities found in your event log.

Activity 2: Select the second activity that should follow Activity 1 in the correct sequence. This dropdown also contains all activities from your dataset. The enrichment will flag cases where Activity 2 occurs but Activity 1 either doesn't occur at all or occurs after Activity 2. For instance, if Activity 1 is "PO Approval" and Activity 2 is "PO Released", cases where "PO Released" happens before "PO Approval" will be marked as violations.

Rule Name: Specify a unique name for this specific conformance rule. This becomes a new boolean case attribute that will be set to true for cases violating this specific ordering rule. Use descriptive names that clearly indicate the violation being detected, such as "Approval After Release" or "QC After Shipment". If left empty, only the Rule Group Name attribute will be created. Each rule name represents a specific ordering violation you want to track separately.

Rule Group Name: Define a category name for grouping related conformance rules. This creates another boolean case attribute that will be true for any case violating rules in this group. Default value is "Activity Order Issue". Use this to group related ordering violations together, such as "Approval Violations" for all approval-related sequence issues or "Quality Process Violations" for quality control ordering problems. This allows for both detailed and aggregated conformance analysis.

Severity: Choose the severity level for this conformance violation from the dropdown:

Low: Minor deviations with minimal business impact
Medium: Moderate violations requiring attention but not critical
High: Serious violations with significant business or compliance impact (default)
Critical: Severe violations requiring immediate correction

The severity level affects how violations are displayed in process maps and conformance dashboards, helping prioritize remediation efforts.

Examples

Example 1: Purchase Order Approval Compliance

Scenario: A procurement department needs to ensure that all purchase orders are approved before they are released to vendors, as releasing unapproved orders violates company policy and can lead to unauthorized spending.

Settings:

Activity 1: PO Approved
Activity 2: PO Released to Vendor
Rule Name: Unapproved PO Release
Rule Group Name: Procurement Compliance
Severity: High

Output: The enrichment creates two new boolean case attributes:

"Unapproved PO Release": true for cases where PO was released without prior approval
"Procurement Compliance": true for any procurement-related conformance violation

Sample data shows:

Case PO-2024-001: Both attributes false (compliant - approved then released)
Case PO-2024-002: Both attributes true (violation - released without approval)
Case PO-2024-003: Both attributes true (violation - released before approval)

Insights: Analysis reveals that 8% of purchase orders are released without proper approval, primarily occurring during month-end rush periods. This insight leads to implementing automated approval reminders and blocking releases for unapproved orders in the system.

Example 2: Manufacturing Quality Control

Scenario: A manufacturing facility must ensure that quality inspection occurs before products are packaged, as packaging uninspected items can lead to customer complaints and recalls.

Settings:

Activity 1: Quality Inspection Completed
Activity 2: Product Packaged
Rule Name: Package Before Inspection
Rule Group Name: Quality Process Violations
Severity: Critical

Output: Creates conformance attributes marking violations:

"Package Before Inspection": Identifies specific QC sequence violations
"Quality Process Violations": Aggregates all quality-related conformance issues

Production batch results:

Batch A-500: Compliant (inspected at 09:00, packaged at 10:30)
Batch A-501: Violation (packaged at 08:45, inspected at 11:00)
Batch A-502: Violation (packaged without any inspection)

Insights: The enrichment reveals that 3% of batches are packaged before inspection, typically during shift changes. This leads to implementing packaging system locks that require inspection confirmation before allowing packaging operations.

Example 3: Healthcare Treatment Protocol

Scenario: A hospital needs to ensure that informed consent is obtained before surgical procedures begin, as performing surgery without consent violates medical ethics and legal requirements.

Settings:

Activity 1: Informed Consent Signed
Activity 2: Surgery Started
Rule Name: Surgery Without Consent
Rule Group Name: Medical Protocol Violations
Severity: Critical

Output: Generates conformance tracking attributes:

"Surgery Without Consent": Flags cases with consent sequence violations
"Medical Protocol Violations": Tracks all medical protocol breaches

Patient case analysis:

Patient 1001: Compliant (consent at 07:30, surgery at 09:00)
Patient 1002: Violation (emergency surgery at 14:00, consent obtained post-operation at 16:00)
Patient 1003: Compliant (consent at previous visit, surgery as scheduled)

Insights: While most surgeries follow proper consent protocols, emergency procedures sometimes bypass standard consent processes. This leads to implementing specific emergency consent procedures and documentation requirements.

Example 4: Financial Services Loan Processing

Scenario: A bank must ensure that credit checks are completed before loan approvals are granted, as approving loans without proper credit assessment increases default risk and violates regulatory requirements.

Settings:

Activity 1: Credit Check Completed
Activity 2: Loan Approved
Rule Name: Approval Without Credit Check
Rule Group Name: Lending Compliance
Severity: High

Output: Creates compliance tracking attributes:

"Approval Without Credit Check": Identifies loans approved without credit verification
"Lending Compliance": Aggregates all lending-related compliance issues

Loan application results:

Loan 2024-0101: Compliant (credit check completed, then approved)
Loan 2024-0102: Violation (approved before credit check was run)
Loan 2024-0103: Violation (approved with no credit check activity)

Insights: The analysis uncovers that 2% of loans are approved without proper credit checks, primarily for existing customers with assumed good standing. This finding prompts policy updates requiring credit checks for all loans regardless of customer history.

Example 5: IT Change Management

Scenario: An IT department needs to ensure that change approval occurs before implementation in production systems, as unauthorized changes can cause system instability and security vulnerabilities.

Settings:

Activity 1: Change Approved by CAB
Activity 2: Deployed to Production
Rule Name: Unauthorized Deployment
Rule Group Name: Change Management Violations
Severity: Medium

Output: Produces change management conformance attributes:

"Unauthorized Deployment": Flags changes deployed without CAB approval
"Change Management Violations": Groups all change process violations

Change ticket analysis:

CHG-0001: Compliant (CAB approval Monday, deployment Wednesday)
CHG-0002: Violation (emergency deployment Saturday, CAB approval Monday)
CHG-0003: Violation (deployed without any CAB review)

Insights: The enrichment reveals that 5% of changes bypass the approval process, mostly during weekend emergency fixes. This leads to establishing an emergency change approval process with expedited CAB review procedures.

Output

The Wrong Activity Order enrichment creates new boolean case attributes that mark conformance violations in your dataset:

Individual Rule Attributes: If a Rule Name is specified, a new boolean case attribute with that name is created. This attribute is set to true for all cases where the specified activity ordering violation occurs (Activity 2 happens without Activity 1 occurring first). The attribute uses a Yes/No display format for easy interpretation in dashboards and reports.

Rule Group Attributes: The Rule Group Name creates another boolean case attribute that aggregates violations across multiple related rules. This attribute is true for any case that violates any rule within the group, enabling both detailed and summary conformance analysis.

Conformance Issue Registration: The enrichment registers the violation in the system's conformance issue list with the specified severity level. This integration ensures that violations appear in conformance dashboards, process maps with violation highlighting, and conformance reports.

Edge Information Updates: The enrichment updates edge information between the two activities, marking the edge as non-conformant with the specified severity. This affects how the process flow is visualized in process maps, with violation edges typically shown in red or with warning indicators.

These attributes can be used in subsequent filters to isolate non-conformant cases, in calculators to compute conformance rates and trends, and in other enrichments that depend on conformance status. The boolean nature of the attributes makes them ideal for creating conformance KPIs, such as calculating the percentage of cases with ordering violations or tracking conformance improvement over time.

Overview

Common Uses

Settings

Examples

Example 1: Purchase Order Approval Compliance

Example 2: Manufacturing Quality Control

Example 3: Healthcare Treatment Protocol

Example 4: Financial Services Loan Processing

Example 5: IT Change Management

Output

See Also