Food Pantry Distribution Demand Analysis Builder

You are a nonprofit data analyst helping a food pantry understand distribution demand and capacity. Build an analysis plan for:

Pantry model: [walk-up pantry, appointment pantry, mobile pantry, school pantry, faith-based pantry, multi-site nonprofit]
Data sources: [client visits, household size, zip code, item categories, inventory logs, volunteer shifts, appointment slots, weather, holidays]
Time period: [last 8 weeks, quarter, year-over-year, seasonal campaign]
Distribution schedule: [weekly windows, mobile routes, evening hours, emergency pickup, holiday boxes]
Client segments: [household size, seniors, families with children, new visitors, repeat visitors, language needs, delivery needs]
Inventory categories: [produce, shelf-stable goods, protein, diapers, hygiene, culturally preferred foods, special diets]
Capacity constraints: [food supply, cold storage, volunteer coverage, parking, intake desks, delivery drivers]
Key questions: [peak demand, stockout causes, no-show patterns, route needs, volunteer gaps, equity concerns]
Data quality issues: [duplicate households, missing zip codes, inconsistent item names, manual sign-ins, privacy limits]
Output needs: [dashboard, board report, grant narrative, operations recommendations, donor update]

Create:
1. Data cleaning checklist and privacy-safe assumptions.
2. Core metrics for visits, households served, pounds or units distributed, stockouts, wait time, and repeat demand.
3. Segmentation plan by household, geography, distribution window, and item category.
4. Demand trend analysis with seasonality, holidays, weather, and benefit-cycle notes if data exists.
5. Capacity analysis for volunteers, cold storage, appointment slots, and mobile routes.
6. Dashboard layout with charts, filters, and alert thresholds.
7. Operational recommendations for ordering, staffing, scheduling, and donor priorities.
8. Grant-ready narrative summarizing need, reach, gaps, and planned improvements.
9. Caveats for incomplete data, privacy, and avoiding stigmatizing language.

Do not infer sensitive attributes that are not in the data. Use respectful language and explain uncertainty clearly.