Case Study: Retail Industry

AI-Powered Inventory Optimisation

Replacing intuition-only ordering with clearer, earlier stock decisions

By Lana Korzhuk, Founder & CEO

At a Glance

The business was carrying too much of the wrong stock while popular lines still went unavailable. We used its sales history, seasonal patterns, and existing buying rules to support more consistent reorder decisions across locations.

Challenge: Inefficient Inventory Management

The team: a multi-location retailer managing thousands of products with very different 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	Lower, not publicly quantified
Stockout Rate	Frequent on popular lines	Improved, not publicly quantified
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

More Consistent Stock Decisions
Ordering used sales history and seasonal evidence rather than individual intuition alone.
Better Exception Visibility
Managers could focus on unusual demand and products without reliable history.
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.

Evidence Boundary

The client remains anonymous and has not approved numerical results for publication. This page therefore explains the workflow and qualitative outcome without presenting invented or unapproved percentages.

Privacy note

The implementation is described without naming the former client or attributing a quotation that has not been approved for publication.

Explore the service behind this work: workflow automation for UK SMEs.

Frequently Asked Questions

How can inventory costs fall while availability improves?▾

They are two sides of the same problem. Intuition-based ordering can overstock slow movers while understocking popular lines. Forecasting per SKU and location helped the team make more consistent ordering decisions, but client-specific figures are not published without approval.

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.

Ready to Optimise Your Inventory?

Send us your current inventory-planning process. We will review it and reply personally with an honest view of whether automation is justified.

Request Workflow Review