max value

Overview

The Max Value enrichment identifies and extracts the maximum value from a selected event attribute across all events within each case, creating a new case-level attribute with this highest value. This statistical operator is essential for understanding peak values, worst-case scenarios, and maximum thresholds in your process data. Unlike aggregations that might average or sum values, Max Value specifically captures the single highest value encountered during the entire process execution, making it invaluable for identifying extremes and outliers.

This enrichment is particularly powerful in process mining scenarios where understanding maximum values provides critical insights into process boundaries, capacity limits, or peak performance indicators. For instance, you can identify the highest cost incurred at any step of a procurement process, the maximum temperature reached during a manufacturing cycle, or the longest individual wait time experienced by a customer. The enrichment preserves the data type of the source attribute, ensuring that numeric maximums, date maximums, and even text maximums (based on alphabetical ordering) are handled appropriately.

Common Uses

Identify peak resource consumption during any activity in the process
Find the highest cost or price recorded at any process step
Determine maximum wait times or delays experienced in customer service
Track peak inventory levels reached during supply chain processes
Identify maximum temperature, pressure, or quality scores in manufacturing
Find the latest timestamp or date value across all process events
Determine highest approval amounts or transaction values in financial processes

Settings

New Attribute Name: Specify the name for the new case attribute that will store the maximum value. Choose a descriptive name that clearly indicates what maximum is being captured, such as "Max_Transaction_Amount", "Peak_Temperature", or "Highest_Priority_Level". This name must be unique and will be added to your case table for use in further analysis, filtering, and visualization.

Activity Names: Select the event attribute from which you want to extract the maximum value. This dropdown lists all available event attributes except for the standard activity name and timestamp columns. The enrichment will scan through all events in each case and identify the highest value of this selected attribute. Only attributes that contain comparable values (numbers, dates, or text) should be selected. The data type of the selected attribute will determine how the maximum is calculated - numeric values use mathematical comparison, dates use chronological comparison, and text uses alphabetical ordering.

Examples

Example 1: Peak Transaction Amount in Payment Processing

Scenario: In a payment processing system, you need to identify the highest individual transaction amount processed for each customer case to support fraud detection and credit limit management.

Settings:

New Attribute Name: Max_Transaction_Amount
Activity Names: Transaction_Amount

Output: Creates a new case attribute "Max_Transaction_Amount" containing the highest transaction value from all payment events in each case. For a case with transactions of:

Payment 1: $125.50
Payment 2: $450.00
Payment 3: $89.75
Payment 4: $1,250.00
Payment 5: $75.00

The Max_Transaction_Amount would be $1,250.00

Insights: This maximum value helps identify cases with unusually high transactions that may require additional verification, supports credit limit decisions, and enables risk-based processing rules.

Example 2: Maximum Temperature in Manufacturing Process

Scenario: In a chemical manufacturing process, monitoring the peak temperature reached during production is critical for quality control and safety compliance.

Settings:

New Attribute Name: Peak_Process_Temperature
Activity Names: Reactor_Temperature_C

Output: For each production batch, creates "Peak_Process_Temperature" by scanning all temperature readings:

Heating Phase: 85°C
Reaction Phase 1: 120°C
Reaction Phase 2: 145°C
Cooling Phase: 95°C
Stabilization: 75°C

Result: Peak_Process_Temperature = 145°C

Insights: Tracking peak temperatures enables quality assurance teams to identify batches that exceeded temperature thresholds, correlate temperature extremes with product quality issues, and ensure safety protocols were maintained.

Example 3: Longest Individual Wait Time in Healthcare

Scenario: A hospital emergency department wants to identify the maximum wait time experienced at any stage of patient treatment to improve service levels and patient satisfaction.

Settings:

New Attribute Name: Max_Stage_Wait_Minutes
Activity Names: Stage_Wait_Time

Output: For each patient case, identifies the longest wait at any treatment stage:

Triage Wait: 15 minutes
Doctor Consultation Wait: 45 minutes
Lab Test Wait: 30 minutes
Treatment Wait: 85 minutes
Discharge Wait: 20 minutes

Result: Max_Stage_Wait_Minutes = 85 minutes

Insights: Identifying the maximum wait time helps hospital administrators understand worst-case patient experiences, identify specific bottleneck stages, and prioritize process improvements where they will have the most impact.

Example 4: Highest Inventory Level in Supply Chain

Scenario: A retail distribution center needs to track peak inventory levels for each product SKU to optimize warehouse space allocation and prevent stockouts.

Settings:

New Attribute Name: Peak_Inventory_Units
Activity Names: Current_Stock_Level

Output: For each SKU's replenishment cycle, captures the highest inventory level:

Initial Stock: 500 units
After Receiving: 1,800 units
Mid-cycle: 1,200 units
Before Reorder: 350 units
After Emergency Restock: 2,100 units

Result: Peak_Inventory_Units = 2,100 units

Insights: Understanding peak inventory levels supports warehouse capacity planning, helps identify overstocking situations, and enables better inventory optimization strategies.

Example 5: Maximum Approval Authority in Procurement

Scenario: In a procurement process with multiple approval stages, identifying the highest approval authority level involved helps understand process complexity and compliance requirements.

Settings:

New Attribute Name: Max_Approval_Level
Activity Names: Approver_Authority_Level

Output: For each purchase request, identifies the highest approval level involved:

Department Manager: Level 2
Finance Manager: Level 3
Director: Level 4
CFO: Level 5
VP Operations: Level 4

Result: Max_Approval_Level = 5 (CFO level)

Insights: Tracking maximum approval levels helps analyze which purchases required executive involvement, supports delegation optimization, and enables audit trail analysis for compliance reporting.

Output

The Max Value enrichment creates a single new attribute in your case table with the name specified in the settings. This attribute will contain the maximum value found across all events in each case for the selected event attribute. The data type of the new attribute matches the source event attribute - if you're finding the maximum of a numeric field, the output will be numeric; if finding the maximum of a date field, the output will be a date.

Cases where the selected event attribute has no values (all null) will receive a null value in the new maximum attribute. The enrichment handles missing values gracefully by excluding them from the maximum calculation. The new attribute becomes immediately available for use in filters, calculators, and other enrichments, enabling you to build complex analytical logic based on these peak values.

Overview

Common Uses

Settings

Examples

Example 1: Peak Transaction Amount in Payment Processing

Example 2: Maximum Temperature in Manufacturing Process

Example 3: Longest Individual Wait Time in Healthcare

Example 4: Highest Inventory Level in Supply Chain

Example 5: Maximum Approval Authority in Procurement

Output

See Also