Intelligent Inventory & Fmcg

Intelligent Inventory Forecasting For A Multi-store Retail Brand

The Background

A regional retail chain operating 12 outlets across two cities struggled with inventory chaos. Fast-moving items ran out frequently, while slow-moving goods filled up the warehouse.

Store managers relied on intuition, leading to massive revenue leakage.

The Problem

Frequent Stockouts

Overstocking Slow Items

No Centralized Demand Intelligence

Promotions Impact Not Measured

The Turning Point

The owner saw 7 cartons of expired FMCG products discarded in a month.
It was the wake-up call to modernize with AI-driven forecasting.

Cor Advance Data Solution

We built a dual-engine AI platform for the SaaS company

POS sales history

  • Web analytics
  • Email engagement
  • Trial activity
  • Company size & industry fit
  • Behavioral patterns of past converters

     

Store demographics

  • Usage depth
  • Feature adoption
  • Login frequency
  • Support queries
  • Account configuration behavior

Seasonal patterns

Suggests optimal overbooking, sends automated WhatsApp/SMS reminders, and alerts staff of high-risk patients.

Weather trends

  • Web analytics
  • Email engagement
  • Trial activity
  • Company size & industry fit
  • Behavioral patterns of past converters

     

Festival impact

  • Usage depth
  • Feature adoption
  • Login frequency
  • Support queries
  • Account configuration behavior

Promotion performance

Suggests optimal overbooking, sends automated WhatsApp/SMS reminders, and alerts staff of high-risk patients.

Implementation Journey

Weeks 1-2

Discovery & Requirement Analysis

Stockouts reduced by 22%

Week 4

Blockchain Strategy & Planning

Overstock reduced by 15%

Week 6

Smart Contract & DApp Development

Working capital freed significantly

Week 8

Smart Contract & DApp Development

Stores operated with predictable replenishment cycles

Transformation & Results

No-Shows (Down from 28%)
0 %
Faster Patient Wait Time
0 %
Reduction in Doctor Idle Hours
0 %
Less Front Desk Workload
0 %

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