A complete working set for contact-centre workforce management — forecasting, Erlang calculators, multi-skill sizing, outbound dialler maths, shift planning, what-if scenarios, capacity and hiring plans, shrinkage diagnostics, and deal sizing. Built on the same classical methods used by Verint, NICE and other enterprise WFM platforms.
Six classical time-series methods run in parallel against your historical contact volume — seasonal naïve, Holt-Winters (additive and multiplicative), auto-SARIMA, classical decomposition, and a Fourier-decomposable model. Holdout backtesting picks the best fit; you can override. Long-term forecast up to 24 months ahead, short-term 13 weeks with day-of-week pattern from daily history.
Paste or upload your volume history. Minimum: 18 months of monthly totals. Ideal: 36 months. Optional: daily data — unlocks the day-of-week pattern for short-term forecast. Auto-detects monthly vs daily, accepts CSV or Excel.
Active holidays affect daily-pattern modelling when daily data is provided. Toggle to include or exclude.
For inbound queues where blocked callers wait rather than being turned away. Use these for staffing decisions in voice, chat and any queued channel with finite handle time.
For lossy systems where blocked callers receive a busy tone rather than queuing — telephone trunks, SIP channels, line sizing. Includes the Extended Erlang B (which accounts for retries by blocked callers) and the Engset model for finite caller populations.
How to read these tools. Erlang C assumes calls that arrive and cannot be answered immediately are held in queue and answered in arrival order. It is the right model for inbound voice and chat queues. Erlang B assumes blocked calls are turned away (busy tone, dropped connection) — the right model for trunk and SIP-channel sizing.
Inputs are per-interval. The classic Erlang formulas assume a stationary arrival rate. For real intraday work, run the calculator interval by interval (typically 15 or 30 minutes) and feed the per-interval call count and the interval-specific AHT.
Limits of the model. The Erlang models do not account for abandonment beyond the simple patience approximation shown here; they assume callers wait until answered. They also assume agents are homogeneous and fully skilled — multi-skill operations need simulation, not Erlang.
Where the formulas come from. A.K. Erlang, the Danish mathematician, derived these formulas at the Copenhagen Telephone Company between 1909 and 1917. They have been the operational backbone of telephony and contact centres ever since. A plain-language explainer of how each formula actually works — written for a curious fifteen-year-old, with worked examples — is available as a complimentary PDF download.
Multi-queue contact-centre sizing with skill-blending leverage. Computes the independent Erlang C requirement per queue, the full-pooling baseline, and a blended figure that captures the realistic leverage from multi-skill agents. Shows where the savings come from and how sensitive they are to the assumed pooling fraction.
Configure 2 to 6 queues. Each queue has its own volume, AHT and SLA target.
| Queue Name | Calls / Hour | AHT (sec) | SLA Target (%) | SLA Time (sec) |
|---|
Pacing maths for predictive and progressive outbound. Sizes the agents needed to hit a target connected-call (or RPC) rate, the dial volume that must be sourced, the pacing ratio, and the implied abandon-rate behaviour. Built for collections, sales outbound and survey operations.
Compute required staff per 15-minute interval across each day of the week (Erlang C), assign shifts greedily to cover the requirement, and export the full plan as an Excel workbook. The Excel output mirrors the structure of a working WFM spreadsheet — seven day sheets, each with Required / Provided / Variance per interval, plus a summary.
Percentages need not sum exactly to 100; the tool normalises.
Each cell is a percentage of that day's total volume. Pre-filled from your currently-selected preset. Columns are auto-normalised on save (so column totals will become exactly 100% even if your edits don't sum to it). Cells you leave at zero remain zero — useful for keeping the operating window tight.
Define a baseline plus up to five scenarios and see the side-by-side impact on required staffing, monthly cost and predicted SLA. Useful for board conversations, sensitivity testing, and stress-testing forecasts.
The reference scenario. All other scenarios apply changes on top of this baseline.
Specify deltas (relative changes) to apply to the baseline. Leave blank or 0 for "no change".
| Scenario Name | Δ Volume (%) | Δ AHT (%) | SLA Override (%) | Δ Shrinkage (pts) | Δ FTE Cost (%) |
|---|
Given an FTE trajectory across the horizon, attrition rate, hire-class size and training time, builds the monthly hiring schedule needed to hit headcount targets while replacing churn. Outputs hire dates, training load, on-floor vs paid roster, and total cost including training overhead.
Required on-floor FTE for each month. Paste a comma-separated list or use the baseline + growth model below.
Decompose historical shrinkage by category and period. Identify planned vs unplanned, spot trends and outliers, surface the largest opportunities for governance intervention. The kind of view no major WFM vendor offers as a standalone tool.
Set the denominator for each period. Shrinkage % is computed as category hours ÷ total paid hours.
| Period Label | Total Paid Hours |
|---|
Enter hours consumed by each category in each period. Planned categories are scheduled events (breaks, training, leave). Unplanned categories are deviations from schedule (sick, late, no-show).
Multi-month commercial sizing for a contact-centre engagement. Takes a volume forecast with growth, runs the pure Erlang C requirement for the SLA target on each month's peak, converts to rostered FTE via shrinkage, multiplies by your per-FTE monthly cost, and rolls up to total cost and cost-per-contact across the horizon.