| --- |
| title: TalentFlow |
| emoji: ✨ |
| colorFrom: purple |
| colorTo: indigo |
| sdk: static |
| app_file: index.html |
| pinned: false |
| license: mit |
| short_description: Workforce planning and staffing simulation. |
| --- |
| |
| # TalentFlow |
|
|
| ### Workforce Intelligence Studio |
|
|
| **A professional, fully client-side workforce planning laboratory for examining capacity, staffing, skill readiness, employee turnover, and operating cost under synthetic scenarios.** |
|
|
| TalentFlow is an original static Hugging Face Spaces application. It gives users an inspectable, editable demonstration environment for understanding staffing tradeoffs, with transparent calculations and no external services. |
|
|
| **Short description:** Workforce planning and staffing simulation. |
|
|
| > **Demonstration only.** The eight example teams, workloads, labor costs, readiness scores, and attrition rates are fictional. They are not verified workforce data or validated forecasts. The application does **not** contain a trained machine learning model. |
|
|
| ## Features |
|
|
| | Workspace | Included capabilities | |
| | --- | --- | |
| | **Command center** | Executive metrics, monthly capacity versus demand, staffing-shortfall signals, and an at-a-glance comparison of scenario coverage. | |
| | **Workforce forecast** | 12-month projections for headcount equivalents, team-level and aggregate capacity, demand, labor cost, and unserved hours, with a CSV export. | |
| | **Scenario studio** | Four editable presets: steady state, growth acceleration, retention pressure, and productivity boost. Sliders control demand, annual attrition, quarterly hires, and productivity. Includes a deterministic 300-run sensitivity band and comparison CSV export. | |
| | **Team registry** | Create, edit, archive, restore, and search workforce teams. Customize workload, monthly growth, FTE, annual attrition, quarterly hires, per-FTE cost, and skill readiness. Export as CSV. | |
| | **Methodology** | In-app explanations of simulation equations, limitations, data handling, and portability. | |
|
|
| Additional features: light/dark mode, responsive desktop and mobile design, browser-local persistence, versioned JSON backup and import, and form validation. |
|
|
| ## Deploy on Hugging Face Spaces |
|
|
| 1. Open [Create a new Space](https://huggingface.co/new-space). |
| 2. Select owner `Hugdrigo`. |
| 3. Name the Space `talentflow-workforce-intelligence`. |
| 4. Choose **Static** SDK, **Public** visibility, and **MIT** license. |
| 5. Extract the ZIP and upload **all three files** (`README.md`, `index.html`, `LICENSE`) directly to the Space repository root. **Do not upload the ZIP itself.** |
| 6. Open the Space to check that it loads and test its settings and exports in the public deployment. |
|
|
| This `README.md` provides the Hugging Face Spaces configuration front matter. No build step, `requirements.txt`, package installation, API token, backend, GPU, or paid service is required. |
|
|
| ## Local operation |
|
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| Open `index.html` in a current browser. For normal browser-local persistence, a standard HTTPS or localhost origin is preferable. Storage access can vary for local `file://` pages. All calculations run in JavaScript without network requests. |
|
|
| ## Model definition |
|
|
| Each team has eight numeric attributes: current FTE, monthly workload hours, monthly demand growth %, annual attrition %, quarterly hires, cost per FTE per month in USD, and skill readiness %. Archived teams are excluded from simulations while remaining editable and restorable. |
|
|
| ### 1. Effective capacity |
|
|
| The model assumes 140 productive base hours per FTE per month and a simplified readiness adjustment: |
|
|
| ```text |
| readiness_factor = 0.65 + 0.35 * readiness_percent / 100 |
| monthly_capacity_hours = expected_FTE * 140 * readiness_factor |
| * (1 + productivity_uplift_percent / 100) |
| ``` |
|
|
| Skill readiness is an illustrative planning coefficient, **not** a directly measured employee performance multiplier. |
|
|
| ### 2. Expected workforce evolution |
|
|
| The first month begins with the team's input FTE. For months 2 to 12: |
|
|
| ```text |
| annual_attrition_effective = clamp(team_attrition + scenario_attrition, 0%, 85%) |
| next_month_FTE = previous_month_FTE * (1 - annual_attrition_effective / 12) |
| |
| For months 3, 6, 9, 12 only: |
| next_month_FTE += max(0, team_hires_per_quarter + scenario_hiring_adjustment) |
| ``` |
|
|
| FTE is a continuously valued expected headcount, so it can be fractional. This does not mean fractional employees are literally hired or dismissed. Quarterly hires are modeled as instantly productive when added. |
|
|
| ### 3. Demand and coverage |
|
|
| ```text |
| month_m_demand = team_starting_demand * (1 + monthly_growth_rate)^(m - 1) |
| * (1 + scenario_demand_adjustment) |
| |
| served_hours = min(month_m_demand, monthly_capacity_hours) |
| unserved_hours_by_team = max(0, month_m_demand - monthly_capacity_hours) |
| |
| month_coverage = sum(served_hours across teams) / sum(demand across teams) |
| annual_coverage = sum(served_hours across all months and teams) |
| / sum(demand across all months and teams) |
| ``` |
|
|
| If total demand is zero, coverage defaults to 100% by convention. Excess capacity in one team **does not** offset shortfall in a different team. Therefore, the sum of team-level unserved hours may exceed an aggregate `max(total demand − total capacity, 0)` calculation. |
|
|
| ### 4. Labor spend and indicative staffing gap |
|
|
| ```text |
| monthly_labor_spend = sum(expected_FTE * assumed_monthly_cost_per_FTE) |
| |
| first_month_FTE_gap_by_team = max(0, demand - capacity) |
| / (140 * readiness_factor * productivity_factor) |
| ``` |
|
|
| Cost totals are illustrative, exclude recruiting and overhead expenses, and assume constant per-FTE wages. Gap estimates presume immediate availability and constant productivity; they are *not* operational hiring recommendations. |
|
|
| ### 5. Four scenario presets |
|
|
| | Preset | Demand adjustment | Attrition adjustment | Quarterly hires adjustment | Productivity uplift | |
| | --- | ---: | ---: | ---: | ---: | |
| | Steady state | 0% | 0 pp | 0 | 0% | |
| | Growth acceleration | +30% | +2 pp | +1 per team | 0% | |
| | Retention pressure | +8% | +16 pp | 0 | 0% | |
| | Productivity boost | +12% | -2 pp | 0 | +22% | |
|
|
| Scenario adjustments are assumption changes, not probability-based forecasts. Slider overrides can be reset to the chosen preset. |
|
|
| ### 6. Sensitivity illustration |
|
|
| The scenario studio performs **300 deterministic seeded calculations** using the selected parameter values plus hypothetical, bounded perturbations in demand and attrition. The displayed P10, median and P90 of annual modeled coverage are **not calibrated prediction intervals**. Repeating the same dataset and parameters produces identical values. |
|
|
| ## Validation |
|
|
| The packaged application was tested locally using headless Chromium and independent Python calculations: |
|
|
| - **14,221 numerical assertions**, including 220 randomized model comparisons of 12 months and five aggregate quantities against an independent Python implementation, annual coverage comparisons, parameter monotonicity, and deterministic sensitivity reproducibility. |
| - **47 functional checks**, covering navigation, model calculations, team creation/editing/archiving/restoration, scenarios, slider overrides, CSV and JSON downloads, JSON backup validation and restoration, HTML escaping, CSV formula escaping, browser-local state writes, reset, theme toggling, and six viewport widths. |
| - **35 additional responsive checks**, covering five views at seven viewport widths ranging from 320 to 1512 pixels, with no document-level horizontal overflow detected. |
| - No uncaught JavaScript errors detected in the tested workflows; JavaScript syntax check passed. |
|
|
| **Scope:** These are local software checks, not an external security assessment, a statistical validation of assumptions, a benchmark against human-resource outcomes, or a successful public Spaces deployment. |
|
|
| ## Data, safety, and limitations |
|
|
| - All app computations run in the browser. No external fonts, packages, data APIs, telemetry, or tracking are used. |
| - Per-browser persistence uses `localStorage`, where supported; clearing browser data may erase unsaved edits. |
| - Version 1 JSON import is limited to 300 KB and 1–40 teams. Values have type and range checks, teams must have unique IDs, and imports require user confirmation before replacement. |
| - Names and categories are escaped during HTML display. CSV output quotes cells and prefixes formulas or suspicious spreadsheet formula triggers with an apostrophe. This is a limited defensive measure, not a complete security audit. |
| - The application does not access live workforce systems, salaries, HR records, skills assessments, staffing vendors, payroll APIs, or employee-specific personal data. |
| - It does not model labor-law constraints, hiring pipelines, recruiting lead times, employee absence, part-time scheduling, holidays, time-zone constraints, service-level routing, staff ramp-up, or performance outcomes. |
| - For real organizational use, validate model assumptions, anonymize or aggregate data, and review decisions with qualified professionals. |
|
|
| ## Files |
|
|
| ```text |
| README.md Documentation and Hugging Face Space metadata |
| index.html Complete offline application, interface and simulation engine |
| LICENSE MIT License |
| ``` |
|
|
| **License:** MIT. See `LICENSE`. |
|
|
| **Portfolio:** [Hugdrigo](https://huggingface.co/Hugdrigo) |
| **Project:** 95 · TalentFlow |
|
|
