SoftwareJuly 2025 - November 2025

Inventory Management System

Full-Stack Solution with AI-Powered Sales Forecasting

Stock Analytics
Days left, reorder points and status for every SKU
AI Forecasting
Prophet-based demand prediction and optimization
Two Partner Companies
Built with Pajara and Toogtons on their sales data

System Overview

This desktop system turns uploaded apparel sales and stock files into SKU status, restock alerts, analytics, and Prophet forecasts.

It connects a Next.js/React interface to FastAPI and Supabase PostgreSQL, packaged with Electron, bringing stock information and restocking decisions into one interface for two apparel companies.

How it actually works

How StockFlow decides what to reorder

The app's own reorder, status and forecasting code, run in your browser with its defaults and checked against the report's screenshots of 5,209 real SKUs.

Things to try

Pick TTG140-M and check its 179 days against the report.
In 4 Status and alerts, change the Low-stock threshold and watch which rule fires first.
Open 6 Prophet forecast and compare MAPE across categories.

Built from the project's own code and documents.from code: A constant or rule copied from the project's repositoryfrom report: A value from the project's report or presentationsimulated: Computed in your browser by running the project's logic

Real rows from the report's stock table

Stock on hand13

Units sold in the last 180 days73

Sales rate window

Lead time

Low-stock threshold

Defaults are the app's: threshold 10, lead time 14 days, sales window 180 days. Preset buttons match the settings panel (180 is the default window, not a preset there). For the report rows, units sold are back-solved from the screenshot's rate and days left.

daily_sales_rate

0.41

days left (floor)

32 days

reorder point (ceil)

reorder qty, 30 days

Reorder needed: stock 13 is at or below the reorder point 16.

solid: projected stockdashed: reorder point = threshold + lead time x ratedotted: low-stock thresholdx axis: 37 days

Report table for TTF131-M: rate 0.41/day, 32d, Normal. The ported code gives 32 days. from report: inventory-report.pdf p.18, Figure 3.3

your inputfrom code

backend/src/api/routes/products.py, daily sales rateView on GitHub

for item in items:
rid = item.get('raw_id')
sr = sales_rows.get(rid)
if sr:
sum_qty = sr.get('sum_qty') or 0
# average per day over the window
daily = float(sum_qty) / float(sales_window_days)
item['daily_sales_rate'] = round(daily, 6)

frontend/components/stock/stock-management.tsxView on GitHub

const stockLevelForCalc = getStockLevel(p)
if (typeof (p as any).daily_sales_rate === 'number') {
dailySalesRate = Number((p as any).daily_sales_rate) || 0
daysUntilStockout = dailySalesRate > 0 ? Math.floor(stockLevelForCalc / dailySalesRate) : 999
} else {
// No backend rate available: leave dailySalesRate as 0 and daysUntilStockout as large number
dailySalesRate = 0
daysUntilStockout = 999
}
// If there's zero stock, days until stockout should be zero regardless of sales rate availability
if (stockLevelForCalc === 0) {
daysUntilStockout = 0
}
const stockLevel = getStockLevel(p)
const leadTime = (typeof p.lead_time_days === 'number') ? p.lead_time_days : (typeof defaultLeadTime === 'number' ? defaultLeadTime : 14)
const baselineSafety = p.low_stock_threshold ?? 0
// Reorder point = baseline safety stock + expected consumption during lead time
const reorderPoint = Math.ceil(baselineSafety + (leadTime * dailySalesRate))
const recommendedReorderQty = Math.ceil(dailySalesRate * 30)

Clothing Inventory page: 5,209 SKUs, cards for out of stock 4,157, active 743, dead 4,248, reorder needed 4,530, and a table with stock level, sales rate, days left and status — Figure 3.3, inventory management pageReport p. 18

The calculator runs the app's own functions in your browser, with its defaults and rounding. Catalogue counts and forecast errors are read from the report's screenshots of the partner's data; no raw sales data is shown. The report has no overall accuracy figure, and its category MAPEs run from 45% to 96%.

How it works

Turn separate business files into inventory decisions.

1
Upload business data
Users provide sales, stock, and SKU files in the supported CSV or Excel formats to refresh the application data.
2
Clean and store records
The ETL pipeline cleans and validates uploads, then loads structured records into the sales, stock, and SKU tables.
3
Identify stock requiring attention
Calculate sales rates, remaining inventory days, and reorder status; display searchable SKU records, summaries, and low-stock notifications.
4
Explore demand forecasts
Select a SKU, category, or sales channel and forecast period to view Prophet predictions alongside inventory and sales analytics.

Project Demo

The project's own demonstration footage.

Inventory Management System video preview

Watch Inventory Management System on YouTube

Engineering features

Developed a full-stack inventory management and sales forecasting system for two partner companies.
Implemented ETL pipelines, low-stock alerting, and advanced SKU/sales analytics.
Built REST APIs, PostgreSQL schemas for sales, stock and SKUs, and Prophet forecasting with a 30-day held-out error check.
Delivered the system as a standalone desktop app and provided user onboarding.

Inside the build

The control logic, integration and design decisions, one note at a time.

A database-backed desktop architecture

Sales, stock, and SKU records are stored separately in Supabase PostgreSQL after upload validation and normalization. FastAPI retrieves these records for analytics and forecasting. The Next.js/React interface presents inventory, product details, and prediction pages, with Electron packaging the interface as a desktop application.

Explainable reorder and stock-status rules

The reorder point combines a baseline threshold with lead time multiplied by daily sales rate. Remaining days are derived from stock divided by sales rate, while recent sales determine active or dead stock. Users can configure thresholds, lead time, and the sales windows used by these calculations.

Forecasting at the user's chosen level

Prophet processes historical sales prepared by the ETL pipeline, and the prediction page lets users choose SKU, category, channel, and horizon. Inventory summaries separately expose stock coverage, category trends, and SKU performance, giving forecasts context alongside the current stock position rather than presenting predictions in isolation.

Build artifacts

Figures taken from the project's own report or presentation.

Inventory dashboard screenshot with stock summary cards, file upload controls, filters, and SKU statuses — Inventory Management Page, Figure 3.3, extracted from PDF page 18; displayed counts are the screenshot's data, not impact metrics.

Evidence & limitations

What the project documents show, and where each claim comes from.

Implemented screens and company feedback

The report shows inventory, settings, analytics, product-detail, and prediction screens. It records upload processing, stock alerts, and positive feedback from both companies during testing and demonstrations. Its discussion also reports variable forecast accuracy, including high error in some cases, without supplying numerical evaluation results.

Inventory management project report
PDF pages 10-15: problem and architecture; 18-25: implementation; page 18 (printed page 9), section 3.3: Lucky's database, frontend, and stock notification attribution; 26-28: results and limitations.
Stockflow source repository
Project demonstration
Linked from the supplied portfolio.

Project Resources

The original documents behind this case study.

Project Report
PDF file
View Download
Presentation Slides
PDF file
View Download

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