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Inventory Platform That Cut Stock-Outs by 45% Across 127 Retail Locations

From 'Where is that product?' guesswork to real-time visibility across every shelf, warehouse, and supplier — with AI predicting demand before customers walk in the door.

Client
StockIQ
Industry
Retail & Logistics
Engagement
Inventory Platform
Duration
5 months
  • React
  • React Native
  • TypeScript
  • Node.jsNode.js
  • PostgreSQLPostgreSQL
  • +7 more
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Inventory Platform That Cut Stock-Outs by 45% Across 127 Retail Locations — case study visual

Operational outcome

Real-time inventory platform

A connected inventory platform unified store, warehouse, and supplier workflows across 127 locations.

Measured result

99.7% inventory accuracy, 45% fewer stock-outs, and $890K recovered in year one.

Overview

The project at a glance

RetailMax Holdings was bleeding money — $2.3M annually in lost sales from stock-outs, excess carrying costs from overstocking, and store managers spending 12+ hours weekly on manual inventory counts that were still only 78% accurate. We built StockIQ: a real-time inventory platform connecting RFID scanners, barcode systems, and POS data across 127 locations. AI-powered demand forecasting predicts reorder points before shelves empty. Automated purchase orders flow directly to suppliers. Five months after launch: 99.7% inventory accuracy, 45% reduction in stock-outs, and $890K in recovered revenue.

What the engagement had to achieve

  1. Eliminate manual inventory counts consuming 12+ hours per store manager per week
  2. Raise inventory accuracy from 78% to 99%+ across all 127 locations
  3. Reduce stock-outs causing $2.3M in annual lost sales by at least 40%
  4. Cut excess carrying costs from chronic overstocking by 25%+
  5. Deploy AI demand forecasting that predicts reorder needs 2+ weeks ahead

The story

The challenge, and how we solved it

What was at risk

The Challenge

RetailMax was operating blind. Store managers spent 12+ hours every week on manual inventory counts — clipboards, spreadsheets, and best guesses. Even then, accuracy hovered at 78%. Stock-outs were costing $2.3M annually in lost sales, while warehouses overflowed with dead inventory from chronic overstocking. When a customer asked 'Do you have this in the back?', the honest answer was usually 'We have no idea.' Corporate couldn't see real-time inventory across 127 locations. Reorder decisions were reactive, not predictive. And suppliers received faxed POs that arrived days after stock ran out.

How we responded

The Solution

We built StockIQ to turn inventory chaos into precision operations. RFID tags and barcode scanners feed real-time stock levels into a central platform — no more manual counts. Every shelf, every warehouse, every incoming shipment appears on a unified dashboard within seconds. AI demand forecasting analyzes 3 years of sales history, seasonal patterns, and external signals to predict reorder points 2-3 weeks ahead. When stock hits thresholds, automated purchase orders fire directly to suppliers via EDI — no faxes, no delays. Store managers now know exactly what's on every shelf without leaving the floor.

Deliverables

What we built

The concrete capabilities designed, built, and shipped in this engagement — each one targeting a specific problem identified above.

Core deliverable

Real-Time Multi-Location Tracking: Every Unit, Every Shelf, Every Second

RFID tags and barcode scanners capture every inventory movement in real-time — receiving, transfers, sales, shrinkage. A unified dashboard shows live stock levels across all 127 locations. Store managers see what's on every shelf without walking the floor. Corporate sees aggregate and location-level views with 30-second refresh rates. The system processes 2.4M+ inventory events daily with 99.97% capture accuracy.

  • Inventory accuracy: 78% → 99.7%
  • 2.4M+ daily inventory events processed
  • 30-second refresh across 127 locations

AI Demand Forecasting: Predict Stock-Outs 2-3 Weeks Before They Happen

Machine learning models trained on 3 years of sales history, seasonal patterns, local events, and weather data predict demand at the SKU-location level. The system identifies items trending toward stock-out 2-3 weeks before shelves empty — early enough for suppliers to deliver. Forecast accuracy improved from 62% (gut feel) to 89% (ML-powered) within 3 months of training.

Prevented $890K in lost sales from predicted stock-outs in year one

  • Forecast accuracy: 62% → 89%
  • 2-3 week advance warning on stock-outs
  • SKU-level predictions for 47,000+ products

Automated Reorder Engine: Purchase Orders That Write Themselves

When stock crosses dynamic thresholds (adjusted for lead time, demand velocity, and safety stock), the system generates purchase orders automatically. Orders flow to suppliers via EDI/AS2 integration — no manual entry, no faxes, no phone calls. The engine placed 23,000+ automated POs in the first year with 99.4% accuracy, requiring human review on only 1.2% of orders.

Reduced purchasing team workload by 65%

  • 23,000+ automated POs in year one
  • 99.4% order accuracy
  • Only 1.2% required human review

Supplier Integration Hub: EDI Connections to 48 Distributors

Direct EDI/AS2 connections to 48 major suppliers and distributors enable automated order transmission, shipment tracking, and invoice reconciliation. The hub processes advance shipping notices (ASNs) so receiving teams know exactly what's arriving before trucks dock. Integration reduced order-to-delivery cycle time by an average of 2.3 days.

Eliminated 94% of manual order entry and invoice matching

  • 48 suppliers connected via EDI
  • Automated ASN processing
  • 2.3-day reduction in order-to-delivery

Analytics Dashboard: From Data Chaos to Actionable Intelligence

Real-time dashboards surface the metrics that matter: inventory turnover, days-on-hand, dead stock identification, shrinkage patterns, and category performance. Custom alerts notify managers of anomalies — unexpected shrinkage spikes, slow-moving SKUs, and emerging stock-out risks. Monthly executive reports auto-generate with one click, eliminating 8+ hours of manual report building.

Identified $340K in dead stock for liquidation in first quarter

  • Real-time visibility across 127 locations
  • Custom alerts for anomalies and risks
  • One-click executive report generation

Technology

The stack

The tools behind the build, and the role each one played.

Frontend

React

Web dashboard and admin interface

React Native

Mobile app for floor staff

TypeScript

Type-safe application code

Backend

Node.js

Node.js

API services and event processing

PostgreSQL

PostgreSQL

Transactional inventory database

Redis

Redis

Real-time cache and pub/sub

AI/ML

Python/scikit-learn

Demand forecasting models

TensorFlow

Deep learning for pattern detection

Apache Airflow

ML pipeline orchestration

Infrastructure

AWS

Cloud platform (EC2, RDS, S3)

Docker/Kubernetes

Container orchestration

Apache Kafka

Event streaming for real-time sync

Outcome

What changed

Within 12 months of launch, StockIQ transformed RetailMax from inventory chaos to precision retail operations — and the financial impact was immediate.

Inventory Accuracy

0.0%

From 78% with manual counts to 99.7% with RFID/barcode automation

Real-time accuracy across all locations

Stock-Out Reduction

0%

From 847 monthly stock-out incidents to 466

Fewer empty shelves and lost sales

Carrying Cost Reduction

0%

From $4.2M to $2.9M in annual carrying costs

Lower excess inventory costs

Manager Time Saved

0+ hrs

From 12+ hours on manual counts to near-zero

Weekly time savings per store manager

Beyond the launch

Lasting improvements

The changes that keep paying off after the engagement ended.

  1. 99.7% inventory accuracy — up from 78% with manual counts
  2. $890K in recovered revenue from prevented stock-outs in year one
  3. $1.3M in freed working capital from reduced overstock
  4. 45% reduction in stock-out incidents across 127 locations
  5. 65% reduction in purchasing team workload through automation
  6. 23,000+ automated purchase orders with 99.4% accuracy
We went from 'I think we have that in the back' to knowing exactly what's on every shelf in real-time. Stock-outs dropped 45% in the first year, and my managers stopped spending half their week on inventory counts. The AI forecasting is almost creepy — it predicted our Easter demand spike two weeks before our best buyer did.
Marcus Chen — VP of Operations, RetailMax Holdings
The automated reorder system processed 23,000 POs last year with 99.4% accuracy. My purchasing team went from drowning in spreadsheets to focusing on supplier negotiations and strategic sourcing. We cut our order-to-delivery time by 2.3 days just by eliminating manual data entry.
Jennifer Walsh — Director of Supply Chain, RetailMax Holdings

How Much Is Inventory Chaos Costing You Right Now?

RetailMax was losing $2.3M annually to stock-outs and overstock before StockIQ. Within 12 months, they recovered $890K in lost sales, freed $1.3M in working capital, and raised inventory accuracy from 78% to 99.7%. What would those numbers look like for your operation?

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