The Efficiency Dashboard provides a summary of the efficiency for Ready Mix operations (not Aggregate) over the course of the day. The dashboard can be used to help with identifying parts of a usual ready mix operation where too much time is taken for specific operations at the aggregate level across the organization.
The basis for all data on this page starts with what tickets are included in the calculations. All tickets within the requested date range and filtered by the following are included
- Ticket must be assigned to a truck the user has visibility into and is in the tag selection they currently have set. This can be further filtered by Business Entity selection on the page, if Business Entities are enabled and configured to apply to trucks.
- Ticket must have a unit that is considered a cubic measurement (effectively cubic yards or cubic meters). This is a UOM of CY, YDQ, or M3 or an explicitly identified UOM of Cubic Yards or Cubic Meters.
- Ticket must not have been voided
- Ticket must have a shipping point that is either unknown or included in the visible points to the user, including in their tag selection.
Filtering of the tickets included in the displayed statistics can be done either through Tags, through Business Entities, through trucks and drivers, or a combination. Filtering is done using the filter slideout in the page’s control bar. Tags also apply to the page, and will limit the included tickets when selected. If you filter by Business Entities then that applies to a ticket’s shipping location. Tag and business entity filtering is applied only to a ticket’s shipping location, and not to its assigned truck. However, tickets that don’t directly associate a shipping point that is known in our system will continue to be included in the displayed summary results.
- When filtering by tags, if any tag selected is in a tag group that applies to trucks, then only tickets that were assigned to the trucks within the selected tags will be included. The only exception is that tickets that are not mapped to a known permanent point in our system will remain included.
When filtering by tags, if any tag selected is in a tag group that applies to trucks, this will not change the selection of tickets, even if those tickets are assigned to trucks not in the tag selection.
- When filtering by business entities, the filter applies only to trucks. So only tickets assigned to trucks that are included in both the company/division and region/location filter (if both are selected) will be included.
If the customer does not use business entities then instead of Company/Division and Region/Location filter options, they will see a Shipping Location filter. This lets the user filter directly by points.
Date Selection and Bookmarking Filters
The currently active filter and date selection are saved in the page URL. This means that reloading the page will not clear the filters, but the filters will clear when leaving and returning to the dashboard or opening it in another tab. It also means that once a certain set of filters is applied, that page can be bookmarked and loading the bookmark will re-apply those filters.
The default date selection on the dashboard is “today”. If the date picker in the control bar is used to select a particular date range, then that date range will be included in the bookmarkable URL state, and will continue to apply when the dashboard is reloaded. If you wish to clear the date selection and reset to the “today” behavior, you can click on the calendar icon next to the date picker.
The Efficiency Dashboard includes an export feature, which lets you drill down into the specific data which the displayed statistics are based on. There are two export options available:
Export Ticket Details - contains the full list of tickets selected by the dashboard based on the active tags and filters. Each ticket includes data on its truck, driver, shipping and return locations, status durations, and other relevant data feeding the stats.
Export Driver Login Details - contains the full list of driver logins used by the dashboard based on the active tags and filters. Each row includes data on the driver’s truck and login and logout times, plus summarized ticket and truck status data used to calculate the stats. This list includes both logged out and actively logged-in drivers. The log out time for currently active drivers is always the current time. These logins can be identified by referring to the “Currently Logged In” column.
Truck Trip Time Distribution
Each status evaluation is based on the average time within a status for each ticket that has time in that status. This means that if a ticket does not have time within a particular status, it doesn’t affect that particular statuses minutes per trip. This can have a significant effect when all tickets have a to job time, but only very few could have a pouring time for a client that does not have DF+ and relies on manual driver selection of the status.
When it says there is only "59.3%" tickets reporting (hover on a status) it means that of all the completed tickets in the timeframe, 59.3% of them reported this status, such as the pouring time example above.
Completed to Ticketed “status” is special, as it is not associated with a ticket and is an aggregate of the time between tickets. However, the max time to include a value in the calculation is 2 hours. This helps avoid including large ranges that would exist overnight.
The 3 percentages in the top right (% travel time, % in yard, and % on job) are all comparisons for the multiple statuses that are in each group compared to the total time associated with the ticket. They should always add up to 100%.
- travel time = to job + to yard
- in yard = time until next ticket + time between ticket activation and to job
- on job = time between at job and returning events
This same logic as described for truck trip time distribution and evaluation of status times applies to workload distribution values “avg load ordered per ticket,” “avg distance driven from yard to jobsite”, “avg yards delivered per miles driven while on ticket”, “gallons of fuel per ticket”, and “miles per gallon of fuel consumed while on ticket”.
The truck and driver lifecycle values for the day include the trucks that were assigned any tickets which come back in the original ticket list. This set of trucks is used to determine the login times to be considered in the aggregate evaluations. However, it is important to note that the larger the number of days included, the more likely it is to include driver login times in the aggregate that isn’t specifically related to ticket work. For example, if you were to query over the course of a week and every truck that has a ticket also has a day of yard work then that time logged in and doing yard work will affect the “average cubic yards delivered per logged in driver hour.”
- Ticketed Time - this is calculated as the ratio of total time (assigned to completed) of all selected tickets, divided by the total hours drivers were logged in during the selected time. Both the driver’s login times and the ticket assigned times are strictly limited by the current date selection (midnight to midnight). For example, if a driver logs in before midnight on the selected dashboard start date, only the time between midnight and their log out will be counted towards total driver hours. Similarly, if a truck is ticketed before midnight on the selected dashboard end date, only the time between ticket assignment and midnight will be included in the total ticket hours. Note that this midnight limit only applies to this stat; the other stats include full ticket and driver hours even if they overlap the date selection.
Startup and End of Day Distributions
Login to First Ticket is the difference between login time and ticket activation time, not ticket creation time. (Although these are often the same).
Arrive Yard to Last Logout is the difference between the last ticket status change and the logout time.
Additionally, if there are multiple logins and logouts throughout the day then each login period will be part of the calculations. For example, if drivers logout in the middle of the day for lunch, then the period between login and the first ticket and the day and the period between login after lunch and the first ticket after lunch will both count towards evaluating login to first ticket and similarly arrive yard to last logout.