Overview
The Duration - Case above threshold grouped by attribute calculator identifies and analyzes cases that exceed a specified duration threshold, with results grouped by a categorical attribute. This calculator helps you understand which categories of cases are experiencing delays and measure the extent of those delays beyond acceptable timeframes.
Unlike the basic Case Duration calculator that shows overall duration statistics, this calculator focuses specifically on problematic cases that breach your performance thresholds, allowing you to segment the analysis by business-relevant categories such as customer, product type, region, or resource.
Common Uses
- Identify which customers or vendors have the most cases exceeding SLA targets
- Analyze delayed orders by product category to find systematic issues
- Measure the extent of delays by regional office or processing center
- Compare SLA breach rates across different case types or priority levels
- Track improvement initiatives by monitoring threshold breaches in specific categories over time
- Identify resource or team performance issues by grouping delayed cases by assigned resource
Settings
Duration Threshold: Specify the maximum acceptable case duration. Cases exceeding this threshold will be included in the analysis. Enter the threshold in the appropriate time unit (hours, days, weeks, etc.).
Grouping Attribute: Select the categorical attribute to group the results by. This allows you to see which categories have the most cases exceeding the threshold. Common choices include Customer, Vendor, Product Type, Region, Resource, or any custom case attribute.
Statistics to Display: Choose which metrics to calculate for each category:
| Statistic | Description |
|---|---|
| Count | Number of cases exceeding the threshold in each category |
| Average Excess Duration | Mean amount of time cases exceed the threshold |
| Total Excess Duration | Sum of all excess durations for cases in the category |
| Maximum Excess Duration | Largest duration breach in the category |
| Percentage of Total | Proportion of all threshold-breaching cases in each category |
Maximum Categories to Display: Limit the output to show only the top N categories with the most threshold breaches. This helps focus on the most problematic areas.
Sort Order: Choose whether to sort categories by:
- Count (most frequent threshold breaches first)
- Average excess duration (longest average delays first)
- Total excess duration (greatest cumulative delay first)
Examples
Example 1: Identifying Customers with Chronic Order Delays
Scenario: Your company has an SLA requiring order completion within 5 days. You want to identify which customers are experiencing the most delayed orders and understand the severity of these delays.
Settings:
- Duration Threshold: 5 days
- Grouping Attribute: Customer Name
- Statistics to Display: Count, Average Excess Duration, Total Excess Duration
- Maximum Categories to Display: 20
- Sort Order: Count (descending)
Output:
The calculator displays a table showing your top 20 customers by number of delayed orders:
| Customer | Cases Over Threshold | Avg Excess Duration | Total Excess Duration |
|---|---|---|---|
| Acme Corp | 47 cases | 3.2 days | 150.4 days |
| Global Industries | 38 cases | 2.1 days | 79.8 days |
| TechStart Inc | 31 cases | 5.7 days | 176.7 days |
Insights: While Acme Corp has the most delayed orders (47), TechStart Inc actually has more severe delays averaging 5.7 days beyond the threshold. This suggests different root causes - Acme may have volume or priority issues, while TechStart may have complex requirements or processing problems. The total excess duration helps quantify the cumulative impact on customer experience.
Example 2: Regional Performance Analysis for Invoice Processing
Scenario: Your accounts payable process operates across four regional processing centers. You've set a 10-day target for invoice processing, and management wants to understand which regions are struggling most with timely processing.
Settings:
- Duration Threshold: 10 days
- Grouping Attribute: Processing Region
- Statistics to Display: Count, Average Excess Duration, Percentage of Total, Maximum Excess Duration
- Maximum Categories to Display: 10
- Sort Order: Average Excess Duration (descending)
Output:
| Region | Cases Over Threshold | Avg Excess | % of Total Breaches | Max Excess |
|---|---|---|---|---|
| APAC | 127 cases | 8.4 days | 35% | 45 days |
| EMEA | 89 cases | 6.2 days | 24% | 38 days |
| Americas East | 78 cases | 4.1 days | 21% | 29 days |
| Americas West | 71 cases | 3.8 days | 20% | 22 days |
Insights: The APAC region shows both the highest volume of delayed invoices and the longest average delays (8.4 days beyond the 10-day threshold). This accounts for 35% of all delayed invoices company-wide. The maximum excess of 45 days suggests serious outliers that need immediate attention. This analysis indicates APAC may need additional resources, process improvements, or investigation into systemic issues.
Example 3: Product Type Analysis for Manufacturing Lead Times
Scenario: Your manufacturing facility produces multiple product types with a standard 14-day production lead time. You want to identify which product types consistently exceed this target and by how much.
Settings:
- Duration Threshold: 14 days
- Grouping Attribute: Product Type
- Statistics to Display: Count, Average Excess Duration, Total Excess Duration
- Maximum Categories to Display: 15
- Sort Order: Total Excess Duration (descending)
Output:
| Product Type | Cases Over Threshold | Avg Excess | Total Excess |
|---|---|---|---|
| Custom Assembly A | 23 cases | 12.3 days | 282.9 days |
| Standard Widget B | 64 cases | 3.1 days | 198.4 days |
| Premium Unit C | 18 cases | 9.7 days | 174.6 days |
Insights: While Standard Widget B has the most cases exceeding the threshold (64), Custom Assembly A has the most severe individual delays (averaging 12.3 days over target). The total excess duration metric reveals that Custom Assembly A represents the greatest cumulative production delay impact (282.9 days). This suggests that custom products may need revised time estimates, additional resources, or process redesign to meet customer expectations.
Example 4: Resource Workload and Performance Analysis
Scenario: Your customer service team handles support cases with a 2-day resolution target. You want to identify which team members have the most cases exceeding this threshold and whether they're experiencing workload issues or performance challenges.
Settings:
- Duration Threshold: 2 days
- Grouping Attribute: Assigned Resource
- Statistics to Display: Count, Average Excess Duration, Maximum Excess Duration
- Maximum Categories to Display: 25
- Sort Order: Count (descending)
Output:
| Resource | Cases Over Threshold | Avg Excess | Max Excess |
|---|---|---|---|
| Sarah Chen | 34 cases | 1.8 days | 12 days |
| Mike Patel | 31 cases | 2.4 days | 18 days |
| Lisa Wong | 28 cases | 1.2 days | 6 days |
| John Smith | 12 cases | 8.7 days | 45 days |
Insights: Sarah Chen has the most delayed cases but relatively modest average delays (1.8 days), suggesting possible workload issues. John Smith has far fewer delayed cases (12) but much higher average delays (8.7 days) with an extreme outlier at 45 days - this pattern suggests individual performance issues or assignment of particularly complex cases. Lisa Wong shows the best performance among high-volume handlers with only 1.2 days average excess, making her a potential model for best practices.
Output
The calculator provides results in a tabular format showing:
Category Column: The values of the selected grouping attribute (e.g., customer names, regions, product types).
Statistical Columns: One or more columns based on your selected statistics, showing metrics like count of delayed cases, average excess duration, total excess duration, and percentages.
Visualization Options: Results can be viewed as:
- Grid view (default) - Detailed tabular data with sorting capabilities
- Bar charts - Visual comparison of categories by selected metric
- Pie charts - Proportional view showing each category's contribution to total delays
Interactive Features:
- Click on any category to drill down into the specific cases that exceeded the threshold
- Sort by any column to reorder the analysis
- Export data for further analysis or reporting
This documentation is part of the mindzie Studio process mining platform.