dashboard

Instruction file imported from notadev-iamaura/Pos_Dashboard (.cursor/rules/dashboard.mdc). Copyright stays with the author.

1. Core Features and Scope (MVP)

Mandatory Features: Data Analysis: Calculation and visualization of basic KPI metrics (Total Sales, Visitors, Avg Spend), daily/weekly/monthly trends, and store-specific performance. Detailed Sales Analysis: Analysis of sales by time of day/product, payment type, and the impact of discounts. Advanced Data Analysis: Anomaly detection, sales-discount correlation analysis, time-of-day-product correlation, store performance factor analysis, trend forecasting, and time series analysis. (Included in MVP despite initial plan) Store Comparison: Comparison of selected stores vs. overall average, top/bottom 25% stores, and similar stores by commercial area. User Interface: Filtering: Date range, store selection (single/multiple), product category filter. User-Customized Interfaces: Implement Headquarters view and Supervisor view (excluding store owner view). No authentication or authorization functionality; simply display different UI/UX configurations based on the selected view/tab. Basic Dashboard Screen: KPI cards, key charts, data filter controls. Basic Announcements (simple text list). Features Explicitly Excluded from MVP (for future implementation): Review analysis, Naver Place impact analysis, report generation/export, customer flow/churn analysis, Q&A/complaint board, responsive mobile UI, all user authentication and authorization features. 2. Technical Stack and Library Choices (Specific to Project)

Backend: Python FastAPI (Global Rule) Data Access: Supabase Client (Global Rule) Data Processing: Pandas, NumPy, Scikit-learn (for advanced analytics), statsmodels, prophet (for time series analysis). Frontend: React.js (Global Rule) State Management: Use React Context API. UI Library: Use Tailwind CSS. Chart Libraries: Use Recharts and D3.js (for advanced visualizations). Routing: Use React Router. API Communication: Use Axios. User View Switching: Implement via a tab interface. Deployment: EC2 Docker Single Instance (Docker Compose, Nginx) 3. Architecture and Structure (Project Specific)

Backend Structure: Follow the provided backend project structure (backend/app/...) and functional module organization (api/endpoints/, services/, models/). Frontend Structure: Follow the provided frontend project structure (frontend/src/...) and component/page/service/context organization. Key API Endpoints: Strictly adhere to the API endpoints defined in the plan: /api/sales, /api/kpi, /api/analytics, /api/compare, /api/trends, /api/notice, including their sub-paths and parameter definitions. Data Flow: Follow the basic data processing flow and the detailed data processing/analytics pipeline steps outlined in the plan. Data Caching Strategy: Use backend memory caching and frontend local state storage as the primary caching strategies. Cache results of computation-heavy advanced analysis in backend memory. Exclude external caching solutions like Redis for the MVP. 4. Implementation Details

Supabase Data Access: Use the supabase-py library, query only necessary data, and handle aggregations at the database level where possible. Data Processing: Perform all complex calculations and aggregations on the backend, providing both calculated results and raw data for visualization. Advanced Analytics Implementation: Implement the anomaly detection (Z-score, IQR, STL), correlation analysis (Pearson/Spearman, Heatmap), sales pattern/trend analysis (Moving Average, Seasonality, ARIMA forecasting), and store performance factor analysis (Clustering, Discriminant Analysis) techniques specified in section 5 of the plan, applying them to the relevant data points. Frontend Performance: Load only necessary data, apply memoization (React.memo, useMemo, useCallback), and optimize chart rendering (limit data points). Screen Design: Follow the common layout and the composition of Headquarters/Supervisor dashboard screens, detailed analytics screen, and notice screen as described in section 8 of the plan. Implement user view switching using a tab UI. 5. Technical Debt and Compromises (MVP Specific)

Do not implement user authentication or authorization functionality. (This aligns with the MVP exclusion list, and may interact with the global rule on simplified error handling/logging, but the core logic is omitted). Use only simplified caching strategies like memory caching. Be aware of the scalability limitation due to single-server deployment. Consider data refreshing to be manual only.