Stockouts reduced by 89%
How an e-commerce retailer replaced gut-feel buying with AI-powered demand forecasting.
The Situation Before Sync4Tech
An online retailer with 4,000 SKUs across three product categories was experiencing stockout rates of 12–18% during peak periods, directly costing an estimated £340K per year in lost revenue. The buying team was working from last year's data and intuition.
Simultaneously, overstock on slow-moving lines was tying up £200K+ in working capital. The business needed to get smarter about what to buy, when to buy it, and in what quantities without adding buying headcount.
The challenge was compounded by a highly seasonal product mix, a supplier base with 8–14 week lead times, and no existing data infrastructure to build on.
How We Solved It
Data Infrastructure
We built a Snowflake data warehouse connecting Shopify, the supplier portal, Google Analytics, and the finance system creating the data foundation for forecasting.
Demand Signal Engineering
We engineered demand signals from 18 months of sales history, seasonality patterns, promotional calendars, and external trend data.
Forecasting Model
We trained and validated an ensemble forecasting model achieving 94% accuracy on held-out data, with SKU-level and category-level predictions.
Buying Workflow Integration
We integrated model outputs into a buying dashboard and automated purchase order suggestions, reviewed weekly by the buying team.
Measurable Impact
From 15% average stockout rate to under 2% within three months of deployment.
Validated on held-out data across all major product categories and seasonal peaks.
Lost revenue from stockouts eliminated, with working capital also freed from overstock.
"Our buying team now works from AI recommendations, not gut feel. Stockouts are almost gone. The ROI paid for the project in the first quarter."— Head of Merchandising, E-Commerce Retailer
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