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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.

  • React
  • Vite
  • Redux
  • JavaScript
  • REST APIs

01

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.

02

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.

03

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.

04

Business impact

Made complex store/COM/omni forecast outputs readable and actionable for retail planners reviewing product-level calculations.

Decisions

Technical decisions

  1. 01Separate StoreVariables, COMVariables, and OmniVariables components per forecast dimension
  2. 02Redux forecastSlice for shared product forecast state across detail views
  3. 03Modal-based calculation detail views for verbose algorithm breakdowns

Features

Feature breakdown

  • Store Forecast Metrics UI
  • COM Forecast Metrics UI
  • Omni Forecast Metrics UI
  • Calculation Breakdown Display
  • Rolling Forecast Tables
  • Monthly Forecast Views
  • Product Detail Metrics

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
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