July 1, 2026 · Automation · 5 min read

Automatic Reorder Suggestions: Never Run Out of Stock Again

Every online retailer knows the dilemma: ordering too early unnecessarily ties up capital in slow-moving inventory. Ordering too late loses revenue through delivery delays and risks marketplace ranking penalties as items temporarily appear unavailable.

The solution lies not in spreadsheets and gut feeling – but in algorithms that calculate reorder points based on actual sales data.

The Two Critical Metrics

For each item, there are two central values: the reorder point and the optimal order quantity (Economic Order Quantity, EOQ).

The reorder point is the inventory level at which an order should be triggered. It results from average daily sales multiplied by lead time – plus a safety buffer that cushions demand fluctuations and delivery delays.

The optimal order quantity balances ordering costs and holding costs: large orders reduce order frequency but increase capital tied up. Small orders keep inventory low but generate frequent transactions.

Accounting for Seasonality

Static reorder points don't work for seasonal products. A garden furniture retailer who calculates reorder points based on annual averages will be understocked in March and overstocked in October.

Advanced systems dynamically adjust reorder points to historical seasonal curves. Instead of a fixed daily average, a weighted average is used that weights recent sales trends more heavily than older data.

Configuration in Colly.io

In Colly.io, individual parameters can be stored per item: minimum stock, lead time per supplier, safety buffer, and whether seasonal adjustments are active. The system automatically calculates reorder suggestions daily and sends a summary by email or displays them on the dashboard.

Supplier orders can be created directly from the suggestion and exported as PDF or sent by email.

Conclusion

Automatic reorder suggestions solve a problem that previously required daily manual attention. Those who configure their reorder points cleanly once can then rely on the system – and invest their time in product curation rather than inventory monitoring.