Review Scheduled and Forecast counts and daily summaries

If you generate a forecast-based schedule, the schedule editor provides scheduled, forecast, and difference counts to help you analyze and plan schedules. In the chart at the bottom of the schedule editor, select Scheduled and Forecast from the list.

Note: The Scheduled and Forecast view changes, depending on your view. The example below illustrates information shown in View by Day and View by Date Range.

Scheduled and Forecast

Available in View by Day and Date Range modes, the Scheduled and Forecast information includes the following rows:

  • The Scheduled row shows how many agents have an activity type during that time period (15-minute interval).
  • The Forecast (without shrinkage) row shows the number of full time-equivalent agents that were forecast during that time period. This value is the total of all forecast values for the time period, and does not include shrinkage.
  • The Difference (without shrinkage) row shows difference between the Scheduled row and the Forecast (with shrinkage) row, and does not include shrinkage.
  • The Forecast (with shrinkage) row shows the number of full time-equivalent agents that were forecast during that time period. This value is the total of all forecast values for the time period, and includes shrinkage.
  • The Difference (with shrinkage) row shows difference between the Scheduled row and the Forecast (with shrinkage) row, and includes shrinkage.

Available in the View by Week mode, daily summaries provide totals for:

  • The number of agents that have shifts for the day.

  • The agent's total number of paid hours for the currently displayed week.

  • The agent's total number of paid hours for the currently displayed day.

Because multi-contact type and multi-staff type environments are so complex, the forecast staffing requirement is not a simple calculation (for example, base staffing requirements) and does not use a simple formula such as Erlang-C.

Instead, the system uses a proprietary process that utilizes mathematical modeling, mathematical optimization, linear programming, discrete event simulation, and heuristics. The scheduling engine attempts to produce a realistic, minimal set of agents based on actual agents that could handle the given load–the number shown is how many agents, including shrinkage, were needed to meet service performance goals.

The agents used are similar to existing agents (for example, with the same queue associations, media type handling abilities, and skills), but with some flexibility that allows it to additionally include up to one clone of each of the agents.