Tulip automatically collects data about time elapsed on each step and the total process cycle time.

You can slice the data in multiple ways, but let's start with a single number analysis to show Average Cycle Time under certain parameters. 

Here's how to view data about each app completion. In this case, an app encapsulates the work done at one station on the shop floor.

1- After selecting "Add Analysis" under the Analytics tab in the App Summary View, select "Single Number": 

2- It will automatically populate the following: 

This shows the total number of times that an app has been completed over a certain time period. In this case, the date range is "All Time".

3. From here, you need to change your "Number" field to "Process Cycle Time". Click on "Number" and then click on "change". Then start typing "Process Cycle Time" in the box that appears.

4. By default, it will populate the average "Process Cycle Time". This means that the analysis will look at the average amount of time that an operator spent in the app between the first step of the app and pressing the "Complete" button at the end of the app.

You can change that, by clicking on "Average" or "Count of Completions" in the menu and choosing a different option. 

5. Remember to press "Save & Close" before exiting the analysis!

You can also change the analysis type by clicking on the 'Single Number' option (right above the analysis title) and selecting a new analysis type from the template menu.

Here's a look into translating the information to a 'One Operation' analysis for a view of the Process Cycle Time that week. 

This chart examines the average amount of time to complete the app on a daily basis. In this case, the timeframe is one week.

Here is the Context Pane for that chart:

In this case, the chart will show you target cycle time. You can get rid of it by deselecting the display option. 

Click on the "Display" dropdown and click on "Hide Target Cycle Time". This is just one example, there are multiple ways to customize your data. 

Your analysis should now look like the following graph: 

Did this answer your question?