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Business Systems

Retail Stock-Audit & Financial Management Platform

A platform built to digitize retail stock audits and financial workflows. It centralizes inventory capture, validation, reconciliation, financial tracking, and reporting, replacing spreadsheets and manual calculations.

Project Scope

Inventory tracking and stock records. Bulk Excel inventory uploads and barcode-based workflows. Automated stock reconciliation between expected and actual quantities. Financial and operational reporting. Role-based access using Supabase Auth and PostgreSQL Row Level Security. Database functions and triggers for automated business calculations.

Technology Stack

Next.js App RouterServer ActionsSupabasePostgreSQLRLSFunctions & TriggersRecharts

Architecture

Users interact with the Next.js frontend. Server Actions validate requests and execute workflows through Supabase. PostgreSQL stores relational data while Supabase Auth and RLS enforce access. Database functions and triggers perform calculations, keeping derived values consistent across Excel and barcode inputs.

Users
👤
Admin
(Full Access)
👤
Manager
(Inventory & Financials)
👤
Staff
(Stock Entry & Audit)
➔
Frontend
Next.js (React)
Web Application
(Desktop)
Mobile View
(Responsive)
UI Components
(Tailwind, shadcn/ui)
⟷
External Integrations
(Barcode Scanner / Excel Upload)
⬇
Backend
Next.js API Routes / Server Actions
Authentication Middleware
Business Logic
(Inventory, Audit, Financials)
Validation & Error Handling
API Endpoints
Deployment
(Vercel)
⟷
PostgreSQL
⟷
⟷
⟷
Database Layer (Supabase)
Authentication
(Supabase Auth)
Row Level Security (RLS)
(Role-based Access)
Functions & Triggers
(Stock Reconciliation, Financials)
⬇
📊 Dashboard & Reports
Real-time Analytics (Recharts)
Inventory Status
Financial Summary
Audit Reports
⟷
Data Storage (Tables)
Products
Inventory (7 channels)
Financials (Dues, Losses, Fulfillment)
Audit Logs
Users & Roles
Reports Data

Results & Validation

Reported project results were approximately 70–75% faster stock-audit cycles, reconciliation accuracy improving from roughly 87% to 99%, daily reporting falling from about 2–3 hours to around 10 minutes, and monthly reporting falling from about one day to under two minutes.