company project · completed
Excel Data Automation Platform
Frontend contribution on a retail Excel forecasting platform — built React/Vite UI to display Store, COM, and Omni forecast variables, calculation breakdowns, and detailed metrics per product within the larger Django forecasting system.
Problem
Forecast outputs from the backend (store-level, COM-level, and omni-channel calculations) were difficult for planners to review — raw API data needed clear, structured UI with visible calculation steps and per-variable detail metrics.
Solution
Developed the product-detail forecasting UI — StoreVariables, COMVariables, OmniVariables, rolling forecast tables, and monthly forecast views — surfacing backend-calculated metrics with readable breakdowns, labels, and drill-down detail for each forecast dimension.
Architecture
React + Vite frontend consuming Django REST API forecast endpoints. Redux (forecastSlice) for state. Component hierarchy: ProductDetailsView → forecast/rolling modules → Store/COM/Omni variable panels with calculation detail modals. Backend forecasting pipeline (Django, pandas) built by other team members.
Business Impact
Made complex store/COM/omni forecast outputs readable and actionable for retail planners reviewing product-level calculations.
Technical Decisions
- Separate StoreVariables, COMVariables, and OmniVariables components per forecast dimension
- Redux forecastSlice for shared product forecast state across detail views
- Modal-based calculation detail views for verbose algorithm breakdowns
Feature Breakdown
Responsibilities
- Built Store, COM, and Omni forecast variable UI components with structured metric display
- Implemented calculation breakdown views so planners can see how each forecast value is derived
- Developed rolling forecast and monthly forecast table UI for product-level detail
- Integrated forecast API responses into Redux state and product detail views
- Designed detail metrics layout for per-variable labels, values, and explanatory drill-downs
Challenges
- Presenting dense forecast variables (store, COM, omni) without overwhelming the product detail view
- Mapping backend calculation fields to clear UI labels and drill-down detail metrics
Performance Considerations
- Structured rendering of large forecast variable sets per product
- Redux selectors to avoid unnecessary re-renders on detail metric updates
Lessons Learned
- Forecast UIs need progressive disclosure — summary tables first, calculation breakdown on drill-down
- Consistent metric cards across Store/COM/Omni reduce cognitive load for planners switching contexts