1c3f6fe78f
- Efficiency preview: toggle 10/25/50/75/90% to see adjusted FTE live - Programme roles NOT reduced (they don't scale with AI) - Excel export: select multiple efficiency levels, each gets its own tab showing original vs adjusted hours/FTE/headcount with hours saved - Export buttons on both Ratecard and Team Shape tabs - team_shape service accepts efficiency_pct parameter Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
91 lines
3 KiB
Python
91 lines
3 KiB
Python
"""Calculate team shape (FTE headcount) from ratecard data."""
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import logging
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from collections import defaultdict
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from sqlalchemy import select
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from sqlalchemy.ext.asyncio import AsyncSession
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from app.models.gmal import Role
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from app.models.project import Project, RatecardLine
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logger = logging.getLogger(__name__)
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HOURS_PER_FTE = 1800
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async def calculate_team_shape(
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db: AsyncSession,
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project: Project,
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efficiency_pct: float = 0,
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) -> list[dict]:
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"""Calculate FTE headcount per role from the ratecard.
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FTE = total_hours_per_role / 1800
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If efficiency_pct > 0, delivery role hours are reduced by that percentage.
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Programme roles are NOT reduced (they don't scale with AI efficiency).
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Returns list of dicts sorted by discipline then role order.
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"""
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# Load ratecard lines
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lines_result = await db.execute(
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select(RatecardLine).where(RatecardLine.project_id == project.id)
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)
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lines = lines_result.scalars().all()
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if not lines:
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return []
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# Aggregate hours per role
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role_hours: dict[int, float] = defaultdict(float)
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for line in lines:
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effective = float(line.manual_override) if line.manual_override is not None else float(line.total_hours or 0)
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role_hours[line.role_id] += effective
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# Load role details
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role_ids = list(role_hours.keys())
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roles_result = await db.execute(select(Role).where(Role.id.in_(role_ids)))
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roles = {r.id: r for r in roles_result.scalars().all()}
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# Build team shape
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efficiency_multiplier = 1 - (efficiency_pct / 100) if efficiency_pct > 0 else 1
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team = []
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for role_id, total_hours in role_hours.items():
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role = roles.get(role_id)
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if not role or total_hours == 0:
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continue
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fte = round(total_hours / HOURS_PER_FTE, 4)
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# Apply efficiency - programme roles are NOT reduced
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if efficiency_pct > 0 and not role.is_programme_role:
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adjusted_hours = round(total_hours * efficiency_multiplier, 2)
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adjusted_fte = round(adjusted_hours / HOURS_PER_FTE, 4)
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else:
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adjusted_hours = round(total_hours, 2)
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adjusted_fte = fte
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team.append({
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"role_id": role_id,
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"role_title": role.role_title,
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"discipline": role.discipline,
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"is_programme_role": role.is_programme_role,
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"sort_order": role.sort_order or 0,
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"total_hours": round(total_hours, 2),
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"fte": fte,
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"adjusted_hours": adjusted_hours,
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"adjusted_fte": adjusted_fte,
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"hours_saved": round(total_hours - adjusted_hours, 2),
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"fte_saved": round(fte - adjusted_fte, 4),
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})
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# Sort by discipline then sort_order
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team.sort(key=lambda t: (t["discipline"], t["sort_order"]))
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total_hours = sum(t["total_hours"] for t in team)
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total_fte = sum(t["fte"] for t in team)
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logger.info(f"Team shape: {len(team)} roles, {total_hours:.0f} hours, {total_fte:.2f} FTE (efficiency: {efficiency_pct}%)")
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return team
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