Make capacity your advantage.
Connect staffing, skill readiness, workload and cost in one transparent planning workspace.
12-month workforce outlook
Modeled monthly demand and effective service capacity, hours
Coverage by team
First-month demand covered by effective workforce capacity
Staffing signals
Where the baseline plan needs attention
Scenario snapshot
How outcomes change under four assumptions
Headcounts, costs, productivity and workload are fictional. Replace demo inputs before using the tool for any real planning decisions.
Model the next 12 months.
Examine capacity, workload, staffing gaps and labor spend over time. Results update as assumptions change.
Capacity versus demand
Expected supply and modeled workload by month
Monthly operating plan
Expected staffing uses fractional full-time equivalents after attrition
Workforce coverage by month
Coverage is capped at 100% for reporting
Planning assumptions
Applies to all teams in the current scenario
Pressure-test every plan.
Choose a fictional scenario and tune hiring, demand and retention to explore tradeoffs between cost and service capacity.
Scenario presets
Each preset is editable without changing underlying team data
Scenario controls
Customize shock parameters for the selected profile
Deterministic sensitivity band
300 seeded perturbations of demand and attrition, illustrative only
Selected scenario
Annual modeled coverage and labor cost
Side-by-side comparison
All four scenario profiles applied to the same teams
Build your workforce model.
Maintain team headcounts, workloads, skill readiness, annual attrition, quarterly hiring and labor costs.
Team registry
All team data is local and editable; archive teams without deleting their history
Know the assumptions.
Every calculation is designed to be explainable, reproducible and suitable for classroom demonstrations.
Workforce calculation model
Transparent, deterministic monthly mechanics
1. Effective workforce capacity
capacity = FTE × 140 hours × (0.65 + 0.35 × readiness/100) × (1 + productivity uplift). This assumes 140 productive base hours per FTE per month and uses skill readiness as a simplified productivity multiplier. This coefficient is hypothetical.
2. Workforce progression
FTE(next) = FTE(current) × (1 − annual attrition/12) + quarterly hires. Hires arrive in months 3, 6, 9 and 12. FTE may be fractional because it is an expected-value projection, not a literal people count.
3. Demand & gaps
demand(month) = starting hours × (1 + monthly growth)^(month−1) × (1 + scenario demand shock). Unmet demand is max(0, demand − capacity). Reported coverage is the sum of min(demand,capacity) divided by summed demand, not a prediction of customer service quality.
4. Costs & staffing recommendations
Modeled cost is FTE × monthly cost per FTE. Shortfall FTE assumes immediate availability at the current productivity level. It does not model hiring lead times, overhead, learning curves, leave, legal constraints or payroll taxes.
What this tool does not do
No AI model, labor-market data, live HR integration, employee tracking, predictive hiring engine, employment advice or calibrated financial forecast. It cannot establish the causal impact of headcount on business performance. All sample teams are fictional and user-configured values remain unverified.
Do not upload personal employee records to a public Space. This browser-only demo is intended for aggregate, non-sensitive planning inputs.
Data & portability
Simple, privacy-conscious demonstration architecture
Everything executes in your browser. No server, third-party scripts, paid models, API keys or outbound requests are required.
Optional localStorage keeps your inputs on the current browser; clearing browser data can erase them. Export a versioned JSON backup before important changes.
JSON imports are limited to 300 KB, 1 to 40 teams, validated numerical ranges and unique IDs. Text values are escaped before HTML rendering. CSV fields are quoted and spreadsheet-formula-like prefixes are escaped.
Built as a self-contained Static Space: README.md, index.html, LICENSE. MIT licensed. No external dependencies.