Overview
The Add enrichment performs addition operations on numeric attribute values and stores the result in a new attribute. This fundamental arithmetic operator allows you to sum multiple case attributes together, providing essential capabilities for aggregating metrics, calculating totals, and deriving new insights from your process data. Unlike simple aggregations that work at the event level, the Add enrichment operates on case-level attributes, making it ideal for combining different numeric measures that characterize each process instance.
The Add enrichment is particularly valuable in process mining scenarios where you need to understand total impacts, combined effects, or aggregate measures. For instance, you can add together different cost components to calculate total process costs, sum various time durations to understand cumulative delays, or combine multiple quality scores to derive an overall quality metric. The enrichment automatically handles different numeric data types and ensures proper type conversion in the output.
Common Uses
- Calculate total costs by adding different cost components (material cost + labor cost + overhead)
- Sum multiple duration attributes to find total processing time
- Combine different delay types to understand total waiting time
- Add quantity changes across different product categories for total inventory impact
- Sum multiple score attributes to calculate composite performance metrics
- Calculate total resource consumption by adding different resource usage metrics
- Aggregate financial impacts by combining revenue and expense attributes
Settings
Filter (Optional): Apply filters to limit which cases receive the new calculated attribute. When filters are applied, only cases matching the filter criteria will have the sum calculated and stored. This is useful when you want to perform calculations only on specific subsets of your data, such as high-value orders or cases from specific regions.
New Attribute Name: Specify the name for the new attribute that will store the sum result. Choose a descriptive name that clearly indicates what values are being added together. For example, use "Total_Cost" when adding cost components, or "Combined_Duration" when summing time attributes. The name must be unique and cannot conflict with existing attributes.
Attribute Names: Select at least two numeric attributes that you want to add together. The enrichment will sum all selected attributes for each case. Only numeric attributes (integer or floating-point) are available for selection. The attributes must already exist in your dataset - you can use attributes from the original data or those created by other enrichments. All selected attributes will be added together to produce the final sum.
Examples
Example 1: Total Order Processing Cost
Scenario: In a procurement process, you need to calculate the total cost for each purchase order by adding together material costs, shipping costs, and handling fees to understand the complete financial impact.
Settings:
- Filter: None (calculate for all orders)
- New Attribute Name: Total_Order_Cost
- Attribute Names: Material_Cost, Shipping_Cost, Handling_Fee
Output: Creates a new case attribute "Total_Order_Cost" containing the sum of all three cost components. For a case with:
- Material_Cost: 1500.00
- Shipping_Cost: 75.50
- Handling_Fee: 25.00
The Total_Order_Cost would be 1600.50
Insights: This combined cost metric enables analysis of total procurement expenses, identification of high-cost orders, and comparison of cost structures across different suppliers or regions.
Example 2: Cumulative Processing Time
Scenario: In a manufacturing process, you want to calculate the total time spent in different processing stages to identify bottlenecks and optimize the production line.
Settings:
- Filter: Product_Type = "Complex Assembly"
- New Attribute Name: Total_Processing_Hours
- Attribute Names: Cutting_Time, Assembly_Time, Quality_Check_Time, Packaging_Time
Output: For complex assembly products only, creates "Total_Processing_Hours" by summing:
- Cutting_Time: 2.5 hours
- Assembly_Time: 8.0 hours
- Quality_Check_Time: 1.5 hours
- Packaging_Time: 0.5 hours
Result: Total_Processing_Hours = 12.5 hours
Insights: Understanding total processing time helps identify products that consume the most production resources and reveals opportunities for process optimization.
Example 3: Patient Care Quality Score
Scenario: In a healthcare setting, multiple quality indicators need to be combined to create an overall patient care score for each treatment case.
Settings:
- Filter: Treatment_Complete = "Yes"
- New Attribute Name: Overall_Quality_Score
- Attribute Names: Clinical_Outcome_Score, Patient_Satisfaction_Score, Safety_Protocol_Score, Documentation_Score
Output: For completed treatments, creates "Overall_Quality_Score":
- Clinical_Outcome_Score: 85
- Patient_Satisfaction_Score: 92
- Safety_Protocol_Score: 88
- Documentation_Score: 90
Result: Overall_Quality_Score = 355 (out of 400 possible)
Insights: The composite score enables hospital administrators to assess overall care quality, compare performance across departments, and identify cases requiring quality review.
Example 4: Inventory Impact Assessment
Scenario: In a warehouse management system, you need to track total inventory changes across multiple product categories to understand daily stock movements.
Settings:
- Filter: Transaction_Date = Today()
- New Attribute Name: Total_Inventory_Change
- Attribute Names: Electronics_Change, Clothing_Change, Food_Change, Hardware_Change
Output: For today's transactions, calculates total inventory movement:
- Electronics_Change: +45 units
- Clothing_Change: -23 units
- Food_Change: +67 units
- Hardware_Change: -12 units
Result: Total_Inventory_Change = +77 units (net increase)
Insights: This aggregated view helps warehouse managers understand overall inventory flow patterns and make informed restocking decisions.
Example 5: Financial Period Closing Adjustments
Scenario: In financial period closing processes, various adjustment amounts need to be summed to calculate the total impact on account balances.
Settings:
- Filter: Period = "Q4-2024" AND Account_Type = "Revenue"
- New Attribute Name: Total_Revenue_Adjustments
- Attribute Names: Accrual_Adjustment, Deferral_Adjustment, Correction_Adjustment, Reclass_Adjustment
Output: For Q4 revenue accounts, sums all adjustments:
- Accrual_Adjustment: 125,000
- Deferral_Adjustment: -45,000
- Correction_Adjustment: 8,500
- Reclass_Adjustment: -12,000
Result: Total_Revenue_Adjustments = 76,500
Insights: Finance teams can quickly assess the net impact of all adjustments on revenue recognition and ensure accurate financial reporting.
Output
The Add enrichment creates a new numeric case attribute with the name specified in the "New Attribute Name" setting. The data type of the output attribute is automatically determined based on the input attributes - if any input contains floating-point values, the result will be a floating-point number; otherwise, it will be an integer.
Calculation Formula: Result = Attribute1 + Attribute2 + ... + AttributeN
Null Value Handling: If any of the selected attributes contains a null value for a particular case, that null value is treated as zero in the addition. This ensures that the calculation can proceed even when some attributes are missing values. For example, if adding three attributes where one is null, only the two non-null values are summed.
Data Type Considerations: The enrichment automatically handles mixed numeric types. When adding integers and floating-point numbers together, the result will be stored as a floating-point number to preserve precision. Large sum values are supported, but ensure your visualization and analysis tools can handle the magnitude of the results.
Integration with Other Features: The new calculated attribute can be used immediately in filters, other calculators, and additional enrichments. It appears in attribute lists throughout mindzieStudio and can be exported with your enriched dataset. The attribute is also available for use in dashboards, process maps, and custom analyses.
This documentation is part of the mindzie Studio process mining platform.