The Predict Cycle Time - Key Factors Analysis dashboard provides detailed insights about the top 8 Key Factors that could potentially contribute to the average cycle time of completed Work Items. These insights enable you to perform detailed analysis and identify areas that you can focus on to improve the cycle time of In Progress or upcoming Work Items and ensure that there are no impediments to the project or iteration schedule.
Key factors correspond to predictor variables used by the ML model to predict time to complete a Work Item. These factors help you in identifying areas associated with a Work Item that could potentially impact the schedule and enable you to take appropriate measures to reduce the cycle time.
The dashboard displays top 8 key factors that are dynamically configured depending on the deployed Cycle Time Prediction ML model. These key factors are categorized into buckets, which are used to represent the key factor values.
As a Delivery Manager, you can find answers for some of the key business scenarios such as:
The data displayed in this dashboard considers only primary Work Items that are in the Completed status and are by default configured to be an Epic or a Story, however, the primary Work Item type can be modified based on your organization needs.
You can use the filters in the dashboard to analyze information related to projects associated with specific Engineering Managers, and the dashboard supports up to three levels of hierarchy in case of filtering results based on Managers.
The Predict Cycle Time Key Factors Analysis dashboard consists of the following pages:
The Key Factor Analysis page provides information about completed Work Items and the top Key factors that have impacted the cycle time of these Work Items. The insights provided in this page enable you to identify Key Factor values (categorized as buckets) that are associated with the most Work Items and have a poor average cycle time. You can then be better prepared to concentrate on these factors to ensure that they do not affect the cycle times of In-progress or upcoming Work Items.
The dashboard page consists of the following sections:
Displays a combination chart illustrating a monthly trend of completed primary Work Items along with the average cycle time for all Work Items in each month. The bars represent the number of completed Work Items and the line graph indicates the average cycle time. You can hover over each bar or node of the line graph to view details.
Note: The information shown in this graph considers data as of the current day. For example, if a Work Item was completed in Jan 2021, reopened in Feb 2021, and was again closed in April 2021; the Work Item is only considered and displayed against April 2021 and not for Jan 2021.
You can click a particular month in the bar chart to drive the other sections of the dashboard and view key factor details specific to the associated Work Items.
Displays the top 8 Key Factors that are determined by the Cycle Time Prediction ML model. Key Factors help you identify areas associated with a Work Item that could have had an impact on the cycle time and therefore affecting a Project schedule. For example, Work Items with more idle time could result in an increased cycle time, Work Items with a blocked flag indicate blockers that affects the schedule.
The Key Factor values are categorized into preconfigured buckets and display the primary Work Items associated with each bucket along with the average cycle time of these Work Items. The average cycle time values are color coded to indicate best to worst cycle time values.
The threshold colors for the average cycle time values are preconfigured as follows:
The threshold values for Average Cycle Time Best Threshold, Worst Threshold, and Average Training Cycle Time are preconfigured with the following default values:
You can click a particular Key Factor value bucket to drive the data in the subsequent Key Factors panel and view details only for the Work Items associated with the selected Key Factor value bucket.
Note: The out-of-the-box Key Factor value buckets, default threshold color, and threshold values are preconfigured and can be modified based on your requirement. To modify these configurations, contact the Digital.ai support team.
To view detailed information about the Work Items and Key Factor values for each bucket, click the link provided at the bottom of this dashboard page.
The Key Factor Analysis - Primary Work Item Details page provides information about all the completed primary Work Items for each Key Factor whose values are categorized as preconfigured buckets. You can also view the key factor details for individual Work Items.
The month over month trend chart and the KPIs are carried over from the previous dashboard page and provide information about the completed primary Work Items and their average cycle time for each month.
Tip: You can click the Return link in the page header to go back to the previous dashboard page.
The dashboard page consists of the following sections:
Displays the top 8 Key Factors displayed in the previous dashboard page that are determined by the Cycle Time Prediction ML model. The Key Factors are provided as drop-down lists with the values categorized as preconfigured buckets. For example, Child Work Item Count key factor values are categorized as the following buckets: 0, 1, 2-5, >=6.
You can select a bucket value from any of the Key Factors to drive the remaining sections of the page and view detailed information about primary Work Items whose Key Factor values are associated with the selected Key Factor bucket.
The Key Factor buckets do not drive the other Key Factors drop-down. For example, if you select the value '0-24' under Comment Keywords Key Factor and select '>=6' under the Child Work Item Count Key Factor, the Work Item Details panel displays all Work Items whose Key Factor values meet the selected criteria ('0-24' and '>=6').
Note: The out-of-the-box Key Factor value buckets are preconfigured based on the deployed ML model and can be modified based on your requirement. To modify these bucket values, contact the Digital.ai support team.
Displays a grid with detailed information about the primary Work Items whose status is Completed and which meet the Key Factors bucket filters specified in the previous section.
The section displays information, such as Work Item Number, date when the Work Item first moved to an In Progress date, completion date, and the actual cycle time of the Work Item calculated as the difference between the first In Progress date and the latest Completed date. The grid also displays the Source URL icon that can launch the corresponding source system to view more details.
You can select a specific Work Item in this grid to drive the next section and view details about the top 8 Key Factors and values corresponding to the selected Work Item.
This section is driven by primary Work Item selection made in the previous section and displays the top 8 Key Factors as determined by the Cycle Time Prediction ML model. You can view the actual values for all the 8 Key Factors that correspond to the selected primary Work Item.
| Metric Name | Description |
|---|---|
| Primary Work Items | Total number of primary Work Items with status as 'Completed' |
| Avg Cycle Time (Days) | Average duration between the first In Progress date and the latest Completed date of primary Work Items whose current status is Completed |
| Projects | Total number of distinct projects that are associated with completed primary Work Items |
| Avg Cycle Time Best Threshold |
Configurable threshold value within which the average Cycle Time of a Work Item is considered as good. |
| Avg Cycle Time Worst Threshold | Configurable threshold value beyond which the average Cycle Time of a Work Item is considered as poor. |
| Average Training Cycle Time | Average duration between the first In Progress date and latest Completed date of Work Items considered in the training dataset of the Cycle Time Prediction Machine Learning model. |
© 2022 Digital.ai Inc. All rights reserved.