Workload & Forecasts: plan coverage
Use historical arrivals to estimate demand and test staffing with Erlang C. Forecasting and coverage calculations use PBX data locally; optional AI is not required to obtain these results.
Separate display history from forecast training
Choose history dates, section and optionally one queue, then update. Arrivals, the load heatmap and SLA summary follow the displayed historical period. Forecast training uses its separate setting of 1–180 completed days. Select 15-, 30- or 60-minute intervals. Click a queue name in the current-pressure table to examine that queue while retaining the analysis settings.
Read demand and validation
Compare current pressure, historical arrivals, weekly time bands and the forecast. The local forecast uses median totals for matching weekdays and needs at least four comparable days; intervals use the observed daily profile. Read the validation panel: MAE is average absolute error in daily arrivals, so lower is better. Campaigns, holidays and service changes are not detected automatically.
Set the coverage assumptions
Open Coverage Simulator and select one queue, or explicitly confirm a shared homogeneous pool. Review expected arrivals, conversation time, after-call work, response-time service target, maximum occupancy and unavailability allowance. Conversation time may be initialized from historical completed calls; after-call work is a separate assumption. Do not insert Global Dashboard AHT: its waiting component is not the simulator’s service time.
Compare scenarios and limits
Compare required net agents, agents to schedule and the planned coverage by interval. Save clearly named scenarios to revisit assumptions. Erlang C assumes random arrivals and stable conditions in a homogeneous pool; it excludes abandonment, differing skills and backlog carried from a previous interval. Adding memberships from several queues is not a count of independent available people.
Read each layer of the workspace
The page combines observations, a forecast and an explicit model. Read their dates and assumptions separately.
| Indicator or control | How to read and use it |
|---|---|
| Current pressure | Waiting/free/busy/paused/DND values come from the latest snapshot. Check its timestamp and missing/stale indication. Click a queue to keep filters and focus its history. |
| Arrivals and historical load | Read arrivals, load heatmap and service summaries over the displayed historical dates. Check the last available source event before treating an empty interval as no demand. |
| Forecast and validation | Read each day’s estimate, range and comparable-day count. Select a forecast day for its intervals. Validation describes historical errors; a range is not a guarantee. |
Run and compare a coverage scenario
Choose the input
In Coverage simulator choose Forecast and an available day, or Manual and the arrival count for the selected 15/30/60-minute interval. Keep the queue scope explicit; a mixed set of unrelated queues does not behave like one homogeneous pool.
Enter the service assumptions
Review talk seconds, after-call seconds, target percentage, answer threshold seconds, maximum occupancy and unavailable percentage. Change one assumption at a time. For example, compare the same arrivals with two after-call values rather than changing arrivals and service time together.
Read the result and save context
Calculate and compare net agents, planned agents and the predicted service per interval. Review unstable/unavailable results instead of replacing them with zero. Save a clearly named scenario and use the comparison control for compatible scenarios; retain the input source and date with the result.
Example: prepare next Monday
Select the support queue, inspect forecast confidence through historical validation and test higher arrivals or longer conversations. Compare staffing alternatives, keeping assumptions visible. Agree on the schedule outside the simulator, then compare actual arrivals and service results after the shift. A saved scenario does not change shifts or PBX queue membership.
Use the controls, understand what changes
Keep the displayed history separate from the forecast basis. The historical heatmap shows average simultaneous waiting calls, not total incoming calls. Open a queue from the pressure table, then check its name before using that queue’s forecast and coverage simulator.
The product’s information button opens on hover or focus. A click keeps the help visible; click again, click outside or press Escape to close it. The examples below reproduce selected product help text; they are teaching controls, not screenshots.
Average number of calls waiting simultaneously in historical samples, by weekday and hour. This is not the total number of calls.
Complete product screenshots
Illustrative data. Open the screenshot to enlarge it and match the controls to the instructions in this chapter.
Open complete screenshot
Read the workspace section by section
Match the numbers and panels below to the complete workspace. These screenshots use illustrative data; the procedures above explain how to work with your own installation.
Choose the scope of the decision
Select the historical period, section and queue, then choose the forecast history and 15-, 30- or 60-minute intervals. The history you inspect and the completed days used to train the forecast each have their own controls.
Open a queue. Focus the whole analysis.
Compare waiting calls and available, busy, paused and DND agents, with the time of the latest sample. Click a queue name to open its waiting trend, historical load, expected arrivals and dedicated coverage simulation with the selected context retained.
Read the demand that actually arrived
Daily queue-entry events show total arrivals, observed days and the average per day. Use the pattern to recognise peaks and quieter days; the date of the latest event helps you assess how current the evidence is.
Look seven days ahead, down to each interval
The local model uses the median of at least four comparable weekdays. Read expected daily arrivals, the minimum and maximum already observed and the comparable-day count, then select a day to inspect its interval profile and prepare the coverage scenario.
Check how the forecast performed on history
Compare the model’s mean absolute error with a simple historical baseline and read how many days were evaluated. This gives you evidence for deciding how much weight to give the forecast when planning.
Find recurring waiting pressure
The day-and-hour heatmap shows average waiting calls per queue sample. Read it alongside sample coverage and peaks to locate recurring pressure and decide which time bands deserve a closer look.
Compare service by queue priority
Compare recorded average service levels, abandonment and observed volumes for queues with a positive weight and those with zero weight. Use the selected period to assess whether priority groups receive the service you expect.
Turn arrivals into an Erlang C scenario
Start from forecast arrivals or enter a manual volume. Set conversation and after-call time, the percentage to answer within a chosen number of seconds, maximum occupancy and unavailability. When combining queues, confirm that the agents can serve them as one shared pool.
Put coverage in the right time bands
Try constant staffing or assign different headcounts to individual time bands. Compare the effect of an earlier start, a stronger peak period or reduced evening coverage; account for breaks and training through the unavailability assumption.
Keep the scenarios worth discussing
Save personal scenarios with parameters, staffing and the reference traffic. Reopen the original snapshot to review a decision on the same evidence, or create a new scenario from the latest forecast.
See the coverage gap before the busy period
The chart and interval table compare scheduled staff, net agents required and agents to schedule. Read the staffing gap, estimated answers within threshold, occupancy and mean wait to locate the specific intervals that need attention.
Compare two choices against the same demand
Compare the current simulation with a saved scenario using the same reference traffic. Read agent-hours, intervals below target and insufficient-capacity intervals together with each scenario’s service and handling assumptions to choose the most suitable coverage.