Workforce Management Decision Lab

→ Run the interactive model in your browser

No install, no signup. Enter your own call volume, AHT, shrinkage and FTE count; the model runs Erlang C per 30-minute interval across a simulated day, diagnoses which kind of problem you have, and sequences the fix.

When service level drops, the instinct is to hire or to launch an AHT reduction program. Modeled at interval level, neither is reliably the first move.

Reshaping the existing schedule to match interval demand — without adding a single productive agent-hour — outperformed a 10% headcount increase in the default scenario.

The staffing was already there. It just wasn't in the intervals where demand landed.

But the more useful output isn't the lever ranking. It's the diagnosis underneath it.

A service-level miss caused by misplaced hours is a different problem from one caused by insufficient capacity. Different owner, different cadence, different budget consequence. Ranking four levers without saying which problem you have assumes the reader already knows.

The model classifies into one of four regimes before it recommends anything:

The classification runs two explicit tests, both shown in the tool:

1. Slot balance — total productive half-hour slots against the summed interval requirement.
2. Reach — whether the best available reallocation of those same hours actually hits the target.

Test 2 is the binding one, because aggregate service level is call-weighted: an operation can miss the per-interval requirement during quiet intervals and still clear target overall.

Materiality rule. If reallocation cannot reach target but recovers at least 2.0 percentage points, the constraint is classified as Mixed rather than Capacity. That threshold is a configurable decision rule, not an Erlang C result.

This is the output the diagnosis makes possible. Most capacity requests are sized against the current schedule. If that schedule has hours in the wrong intervals, the request inherits the error and asks for capacity that is already funded.

Modeled 70-FTE operation, demand peakiness 1.3:

Reallocating the same 369.5 productive agent-hours lifts modeled service level from 33.7% to 72.7% before a single hire is approved.

1,200 calls · 240s AHT · 34% shrinkage · 19 scheduled FTEs · typical double-hump demand

Baseline delivers 43.5% against an 80/20 target. 21 FTEs are required, 19 are scheduled. 10 of 16 intervals are understaffed, 4 are overstaffed. Diagnosis: Mixed.

Step 1 — reallocate first

Step 2 — capacity options, sized against the corrected schedule

Reported separately, because it is two levers rather than one:

Redistribution holds 100.5 productive agent-hours (201 half-hour slots) exactly constant. It adds no capacity; it moves existing capacity between intervals.

A model that always favors its own headline lever is less useful, not more. Two disclosures:

Flat demand returns exactly +0.0 pp. Set peakiness to zero and redistribution recovers nothing, because a flat day has nothing to redistribute. The regime correctly flips to capacity-constrained.

Redistribution beats a 10% headcount add in 38% of scenarios, not all of them. Across a sweep of scenarios that miss target, it wins 44 of 116. The finding is not "redistribution beats hiring." It is: diagnose the interval problem before assuming the answer is more headcount.

The lever magnitudes are adjustable sliders specifically so the ranking can be broken.

Both use Erlang C at the interval level and demonstrate the same diagnostic principle, but the magnitudes differ because the demand pattern and baseline schedule differ. Neither analyzes an organization's real forecast.

1. Distribute call volume across 30-minute intervals.
2. Convert volume and AHT into offered load (Erlangs).
3. Size required agents per interval using Erlang C, under an occupancy ceiling.
4. Apply a shrinkage build-up to convert on-phone agents into scheduled FTEs.
5. Compare scheduled coverage against the interval requirement.
6. Classify the regime, then model each lever and measure call-weighted service level.

Redistribution reshapes the schedule toward the interval requirement curve while holding total productive agent-hours fixed, and is floored so no interval is stripped to feed the peaks. The baseline schedule is always a candidate allocation, so redistribution can never score worse than leaving the schedule alone.

Full detail in methodology.md.

Poisson call arrivals · exponential handle times · no abandonment (Erlang C, not Erlang A) · a single homogeneous agent skill · no transfers or retries · steady state within each interval · redistributed staffing assumed operationally movable · no shift-length, labor-rule, skill or break-placement constraints.

These assumptions are appropriate for a planning-stage capacity model, but they are not a substitute for a scheduling engine that respects real shift rules.

Abandonment-aware queueing (Erlang A), so caller patience is modeled rather than assumed infinite. Multi-skill and blended workload, since most operations are not a single queue. Shift-constrained optimization, so a recommended redistribution is actually rosterable under labor rules. Intraday re-forecasting. And import of real interval forecasts and schedules in place of the generated demand curve.

WFM-Decision-Lab/
├── README.md
├── index.html                    interactive model (GitHub Pages)
├── decision_lab.py               Python case-study model
├── methodology.md                full methodology
├── requirements.txt
├── sample_interval_data.csv
├── lever_comparison.png
└── wfm-decision-lab-hero.png

pip install -r requirements.txt
python decision_lab.py

Sean Codner — Workforce Management & Operations Analyst

WFM Forecasting · Erlang C · Intraday Staffing · ATM Network Operations · SQL · Python

Part of a workforce-management and operations analytics portfolio: github.com/SEANSKIDATA