KPI and KRI Summary
Last updated
Last updated
Case Count: Total (unique) number of cases
Activity Count: Total number of activities
Link Count: Total number of paths
Variant Count: Total number of variants
Waiting Time (A): Average time cases spent as waiting time (requires end date/time stamp per activity)
Waiting Time (T): Total time cases spent as waiting time (requires end date/time stamp per activity)
Process Time (A): Average time cases spent in the total process (activities plus waiting time)
Process Time (T): Total time cases spent in the total process (activities plus waiting time)
Activities (Mean): Mean number of activities per case
Activities (ST): Standard deviation of number of activities per case
Activities (Higher): Maximum number of activities per case
Activities (Lower): Minimum number of activities per case
Min Paths (Case): Minimum number of paths per case
Max Paths (Case): Maximum number of paths per case
Paths (Avg/Case): Average number of paths per case
Paths (SD): Standard deviation of number of paths per case
Min Time: Minimum time spent in the process (one case)
Max Time: Maximum time spent in the process (one case)
As a designer you decide which KPIs and KRIs to display by using the checkboxes in the customization pane. The appearance of the KPI bar can be customized to fit within the thematic guidelines of the rest of your dashboard and ProcessMining experience. Each KPI or KRI can also be individually styled, or a general style can be applied to all measures at once.
Key Performance Indicators (KPIs) and Key Risk Indicators (KRIs) are essential metrics used in process mining to measure and evaluate process performance and identify potential risks. Here’s a description of each:
Key Performance Indicators (KPIs):
KPIs are quantifiable metrics that provide insights into the performance and efficiency of a process. They help measure how well a process is meeting its objectives and can be used to monitor, analyze, and improve process performance. KPIs are typically aligned with the goals and objectives of an organization or specific process. By tracking and analyzing KPIs, process analysts can identify bottlenecks, inefficiencies, and areas for improvement. KPIs provide a quantitative basis for decision-making and performance management.
Example: In a customer service process, some KPIs might include Average Resolution Time, Customer Satisfaction Score, First-Contact Resolution Rate, or Number of Service Requests Handled per Agent. These KPIs would provide insights into how efficiently and effectively the customer service process is performing, helping identify areas for improvement or resource allocation.
Key Risk Indicators (KRIs):
KRIs are metrics used to identify and monitor potential risks associated with a process. They help organizations proactively identify and mitigate risks that may impact process performance, compliance, or overall business objectives. KRIs are designed to measure and track specific risk factors or indicators that are critical to the organization. By monitoring KRIs, organizations can detect early warning signs, trigger risk mitigation actions, and ensure adherence to compliance regulations.
Example: In a financial transaction process, some KRIs might include Average Transaction Value, Percentage of Transactions Above a Certain Threshold, Number of Suspicious Transactions, or Frequency of Failed or Rejected Transactions. These KRIs would help identify potential risks such as fraud, non-compliance, or operational inefficiencies in the financial transaction process, allowing organizations to take appropriate actions to mitigate those risks.
Both KPIs and KRIs play a crucial role in process mining by providing objective measures and insights into process performance and risk exposure. While KPIs focus on evaluating performance and efficiency, KRIs focus on identifying and managing risks. By monitoring and analyzing these indicators, organizations can make informed decisions, optimize processes, and ensure that performance objectives are met while mitigating potential risks.
Case Count: This KPI represents the total number of unique cases in the event log. It provides an overview of the volume or size of the process dataset.
Example: Let’s say you have an event log of a customer support process. If the Case Count KPI shows 500, it means that there are 500 unique cases (e.g., 500 customer support requests) recorded in the event log.
Activity Count: This KPI indicates the total number of activities recorded in the event log. It provides insights into the complexity or granularity of the process.
Example: Suppose you are analyzing a procurement process. If the Activity Count KPI displays 100, it means that there have been 100 unique activities involved in the procurement process, such as “Create Purchase Order,” “Receive Goods,” or “Approve Payment.”
Link Count: This KPI represents the total number of paths or transitions between activities in the process. It helps identify the complexity of the process flow and the presence of alternative or parallel paths.
Example: Consider a loan approval process. If the Link Count KPI shows 200, it means that there have been 200 transitions or links between activities in the loan approval process, representing the various paths taken by loan applications through different stages.
Variant Count: This KPI indicates the total number of distinct process variants or sequences of activities observed in the event log. It helps understand the process’s flexibility and the existence of different process paths.
Example: Let’s say you are analyzing an order fulfillment process. If the Variant Count KPI displays 10, it means that there are 10 different process variants observed in the event log, representing various ways orders have been fulfilled based on different conditions or decisions made during the process.
Waiting Time (A): This KPI represents the average time cases spend in a waiting state between activities. It requires end date/time stamps per activity to calculate waiting times accurately.
Example: Suppose you are analyzing a patient care process. The Waiting Time (A) KPI might show an average waiting time of 2 hours, indicating that, on average, patients spend 2 hours waiting between different medical procedures or consultations.
Waiting Time (T): This KPI represents the total time cases spend in a waiting state between activities. It requires end date/time stamps per activity to calculate waiting times accurately.
Example: Imagine you are analyzing an IT support process. The Waiting Time (T) KPI might display a total waiting time of 50 hours, indicating the cumulative waiting time across all cases in the event log between the submission of support tickets and their resolution.
Process Time (A): This KPI represents the average time cases spend in the entire process, including activities and waiting times. It provides insights into the overall process duration per case.
Example: Consider a travel booking process. The Process Time (A) KPI might display an average process time of 5 days, indicating that, on average, it takes 5 days for a travel booking case to go through all the necessary activities and waiting times before completion.
Process Time (T): This KPI represents the total time cases spend in the entire process, including activities and waiting times. It provides insights into the cumulative process duration across all cases.
Example: Let’s say you are analyzing a manufacturing process. The Process Time (T) KPI might show a total process time of 1,000 hours, indicating the combined time spent by all cases in the event log from the start of the process until completion.
These KPIs help quantify and analyze different aspects of the process, such as its size, complexity, time durations, waiting times, and variations. By monitoring and analyzing these KPIs, process analysts can gain valuable insights into process performance, identify bottlenecks, and make data-driven decisions for process improvement.
Activities (Mean): This KPI represents the mean or average number of activities per case in the event log. It provides insights into the typical number of activities executed per case.
Example: Suppose you are analyzing an employee onboarding process. The Activities (Mean) KPI might display an average of 8 activities per case, indicating that, on average, employees go through 8 different activities during the onboarding process.
Activities (ST): This KPI represents the standard deviation of the number of activities per case in the event log. It measures the variation or dispersion in the number of activities across cases.
Example: Consider a project management process. The Activities (ST) KPI might show a standard deviation of 2, indicating that the number of activities per case varies by approximately 2 activities on average.
Activities (Higher): This KPI represents the maximum number of activities observed in a single case within the event log. It indicates the highest number of activities executed in a single instance of the process.
Example: Let’s say you are analyzing a software development process. The Activities (Higher) KPI might display 15, indicating that one particular case in the event log had the highest number of activities observed, involving 15 different tasks.
Activities (Lower): This KPI represents the minimum number of activities observed in a single case within the event log. It indicates the lowest number of activities executed in a single instance of the process.
Example: Imagine you are analyzing an order processing process. The Activities (Lower) KPI might show 3, indicating that there is at least one case in the event log with the minimum number of activities observed, involving only 3 tasks.
Min Paths (Case): This KPI represents the minimum number of paths or transitions observed in a single case within the event log. It indicates the lowest number of paths taken by a case.
Example: Suppose you are analyzing a customer complaint resolution process. The Min Paths (Case) KPI might display 1, indicating that there is at least one case in the event log with the minimum number of paths observed, representing a straightforward resolution without any deviations or alternative paths.
Max Paths (Case): This KPI represents the maximum number of paths or transitions observed in a single case within the event log. It indicates the highest number of paths taken by a case.
Example: Consider a supply chain process. The Max Paths (Case) KPI might show 5, indicating that there is at least one case in the event log with the maximum number of paths observed, representing a complex supply chain scenario involving multiple decision points and alternative paths.
Paths (Avg/Case): This KPI represents the average number of paths or transitions per case in the event log. It provides insights into the typical number of paths followed by cases.
Example: Let’s say you are analyzing a customer support process. The Paths (Avg/Case) KPI might display an average of 3 paths per case, indicating that, on average, customer support cases follow 3 different paths based on various conditions or decisions.
Paths (SD): This KPI represents the standard deviation of the number of paths per case in the event log. It measures the variation or dispersion in the number of paths across cases.
Example: Suppose you are analyzing a logistics process. The Paths (SD) KPI might show a standard deviation of 1.5, indicating that the number of paths per case varies by approximately 1.5 paths on average.
Min Time: This KPI represents the minimum time spent in the process by a single case within the event log. It indicates the shortest duration from the process start to completion for a specific case.
Example: Imagine you are analyzing an insurance claims process. The Min Time KPI might show 2 days, indicating that there is at least one case in the event log where the claim was processed and resolved within 2 days.
Max Time: This KPI represents the maximum time spent in the process by a single case within the event log. It indicates the longest duration from the process start to completion for a specific case.
Example: Consider a patient treatment process. The Max Time KPI might display 30 days, indicating that there is at least one case in the event log where the patient treatment process took a maximum of 30 days from admission to discharge.
These additional KPIs provide further insights into the characteristics, variations, and durations of activities and paths within the process. By analyzing these KPIs, process analysts can identify outliers, measure process efficiency, and uncover potential areas for improvement.
