--
OPS HEALTH
INITIALIZING
Composite Operational Score
PROJECTED SL ?
--%
AGENTS REQ ?
--
EOD PROJECTION ?
--
RUN RATE/HR ?
--
WORKLOAD DELTA ?
-- h
FTE GAP ?
--
AI OPERATIONS INSIGHTS
◈Load the example scenario or enter your parameters and click RUN ANALYSIS.
INTRADAY PERFORMANCE
Hours Elapsed
--
Hours Remaining
--
Forecast Vol
--
Actual Vol
--
Variance %
--
Calls in Queue
--
Abandon Rate
--
AHT Drift
--
ERLANG-C STAFFING ENGINE
ERLANG-C
Required Agents ?
--
Occupancy % ?
--
Avg Speed of Answer ?
--
Prob. of Delay ?
--
Traffic Intensity ?
--
Scheduled Needed ?
--
SERVICE LEVEL PROJECTION
0%25%50%75%100%
Projected SL
--
Target SL
--
SL Gap
--
END-OF-DAY PROJECTION
Projected Vol
--
Run Rate/hr
--
Remaining
--
CAPACITY vs WORKLOAD ?
Required Hours
--
Available Hours
--
Capacity Delta
--
FTE Gap
--
ARRIVAL VARIABILITY ?
Coefficient of Variation
--
Burstiness Index
--
Pattern Shift %
--
Forecast Confidence
--
PROJECTED PEAK:--:--
FORECAST HEALTH
MAPE
--
Bias
--
Accuracy
--
PRIMARY VARIANCE DRIVER
—
INTRADAY REFORECAST
Forecast Wt
--
Run-Rate Wt
--
Revised Vol
--
MONTE CARLO SIMULATION ENGINE
READY
⚙
Click RUN SIMULATION to model thousands of outcome scenarios and estimate the service level probability distribution.
AI FORECASTING BRAIN
Volume Spike Risk
Queue Buildup Risk
SL Collapse Risk
AHT Drift Risk
REAL-TIME STAFFING ACTION ENGINE
STANDBY
Run analysis to generate staffing recommendations.
INTRADAY INTERVALS TABLESimulated intervals based on current inputs
| INTERVAL | FORECAST | ACTUAL / EST | AHT (sec) | AGENTS REQ | SERVICE LEVEL | STATUS |
|---|---|---|---|---|---|---|
| Run analysis to populate interval data. | ||||||
HOW THIS WORKFORCE INTELLIGENCE ENGINE WORKS
01
Arrival Estimation
The system estimates call arrival patterns using forecast volume, current run-rate, and arrival variability. Reforecast weights shift dynamically as the day progresses (70/30 → 50/50 → 30/70).
02
Erlang-C Staffing
Erlang-C queueing models calculate the minimum agents required to meet your service level target, accounting for AHT, traffic intensity (Erlangs), and shrinkage.
03
Capacity Analysis
Workload demand (Volume × AHT ÷ 3600) is compared against available productive hours (Agents × Shift Hours × (1 − Shrinkage%)). Surplus or deficit is flagged immediately.
04
Operational Insights
The AI layer auto-identifies the primary risk driver — volume spike, AHT drift, forecast error, or arrival pattern shift — and generates plain-language recommendations for RTA teams.
05
Monte Carlo Simulation
Thousands of randomised scenarios model service level probability distributions. Outputs include best-case (P90), median (P50), and worst-case (P10) service level outcomes.