Case Study: Retail Industry

AI-Powered Inventory Optimisation

How Predictive AI Reduced Costs by 23% While Improving Product Availability

By Lana Korzhuk, Founder & CEO

Executive Summary

A retail business with multiple locations was struggling with inventory management, resulting in overstocking of slow-moving items and stockouts of popular products. By implementing a predictive inventory management system using historical sales data, seasonal trends, and external factors, the client reduced overall inventory costs by 23% while decreasing stockouts by 64%.

Challenge: Inefficient Inventory Management

Client Persona: Multi-location retail business managing thousands of SKUs across diverse product categories with varying demand patterns.

The business was relying on intuition-based ordering and manual inventory tracking, leading to significant capital tied up in slow-moving stock while simultaneously losing sales due to stockouts of popular items.

Before and After Inventory Management Metrics
Metric	Before Automation	After Automation
Inventory Costs	High carrying costs	Reduced by 23%
Stockout Rate	8-10% of SKUs	3% of SKUs
Ordering Process	Manual, intuition-based	Automated, data-driven
Inventory Turnover	Low for many items	Optimised across range

Capital Waste:

Significant capital tied up in slow-moving inventory with poor turnover rates.

Lost Sales:

Frequent stockouts of popular products resulted in lost revenue and dissatisfied customers.

Guesswork Ordering:

Purchasing decisions were based on gut instinct rather than data, leading to consistently suboptimal stock levels.

Solution: Predictive AI Inventory Management

The solution leveraged machine learning models trained on historical sales data, seasonal patterns, and external factors to predict optimal stock levels for each product at each location.

Data Integration & Pattern Analysis

Historical sales data, seasonal trends, promotional calendars, and external factors (weather, local events) were integrated into a unified data model to identify demand patterns for each SKU.

Predictive Demand Forecasting

Machine learning models generate rolling demand forecasts for each product and location, accounting for seasonality, trend shifts, and promotional effects to recommend optimal reorder points and quantities.

Automated Replenishment & Alerts

The system generates automated purchase orders when stock approaches reorder points, while alerting managers to unusual demand spikes or emerging trends that may require human judgement.

Results: Lower Costs, Better Availability

Key Outcomes

23% Cost Reduction
Overall inventory carrying costs reduced through optimised stock levels.
64% Fewer Stockouts
Popular products consistently available, reducing lost sales.
Improved Cash Flow
Less capital tied up in slow-moving stock, improving working capital.
Better Customer Experience
Higher product availability led to increased customer satisfaction and loyalty.

Impact Metrics

Inventory Cost Reduction23%

Stockout Reduction64%

Automated Ordering85%

Client Impact Statement:

"The AI inventory system pays for itself many times over. We're carrying less stock but have better availability — it's transformed our retail operations."

Frequently Asked Questions

How can inventory costs fall while availability improves?▾

They are two sides of the same problem. Intuition-based ordering simultaneously overstocks slow movers and understocks popular lines. Forecasting per SKU per location released capital from the slow movers — a 23% cut in carrying costs — while raising cover on the fast ones, taking stockouts from 8–10% of SKUs down to 3%.

What data does AI inventory forecasting need?▾

Historical sales, seasonal patterns, and promotional calendars are the core inputs, with external factors such as weather and local events layered on. If your sales history lives in a till or ERP system, you almost certainly have enough to start.

Does the system order stock automatically?▾

It generates purchase orders when stock approaches a reorder point, and it alerts managers to unusual demand spikes or emerging trends that warrant human judgement. The intent is to automate the routine reorders, not to remove buyer oversight from the unusual ones.

How many SKUs does this approach need to be worthwhile?▾

This client managed thousands of SKUs across multiple locations, which is where per-SKU forecasting pays for itself. With a small, stable product range, a simple reorder-point rule will get you most of the benefit for a fraction of the cost.

What happens with new products that have no sales history?▾

Forecasts for new lines lean on comparable products and category-level seasonality until the SKU accumulates its own history. These are the items that most warrant a buyer's judgement, which is why the system flags rather than silently orders them.

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