Compare oversell-exposure scenarios
Enter your own stock, average order rate, sync interval and buffer assumptions. Compare formula outputs for multi-channel selling scenarios on Amazon, eBay, Temu and TikTok Shop.
Your setup
Enter one SKU’s assumptions. Results compare scenarios using a simplified formula; they are not observed or predicted outcomes.
Physical units available
How often stock syncs between channels
Stock held back from channels
Illustrative presets
Modelled collision exposure
Modelled events / month
0.0
Formula output using 150 entered orders/month.
Gross sales exposure / month
£0.26
Modelled events × your entered average sale price; no other costs.
Stockout projection
18.0 days
90 units buyable (after 10% buffer).
Race window exposure
0.1%
Of stock cycle at collision-prone levels (W = 0.10 orders/window).
Inputs to test
- Compare the 30-minute input with a shorter interval, then verify whether each marketplace and connector can actually support the alternative.
- Compare the entered 10% buffer with alternatives and account for stock withheld from sale as well as collision exposure.
- Confirm that all 3 entered channels share the same operational stock and document how each API handles updates, retries and failures.
- Compare the 5 orders/day assumption with observed SKU-level data, including peaks; this model assumes a constant average rate.
Method: a simplified Poisson-style comparison using only your entered stock, average order rate, channel count, sync interval and buffer. It is not calibrated against MaxInvent customers or UK seller outcomes and does not predict marketplace action.
Why oversell risk matters for UK multi-channel sellers
Overselling can create cancellations, customer-service work and marketplace-account consequences. Check the current official policies for each marketplace; this tool does not predict warnings, visibility changes or suspension.
The three levers that control oversell risk
- Sync cadence. How often the platform pushes stock updates to each channel. The formula uses the entered interval as a scenario variable; it does not model marketplace processing or retry behaviour.
- Safety buffer. Stock held back from channels. A 10% buffer means Amazon sees 90 when you have 100. Higher buffer = fewer oversells but more stranded sales.
- Architecture. How your systems reserve stock, publish availability, retry failed updates and reconcile incidents. The simplified formula does not model those controls.
How the math works
The calculator uses a Poisson approximation for concurrent arrivals. Orders arrive at rate r per minute across all channels. In a sync window of S minutes, the expected number of orders is W = r × S. A “race window” exists at the bottom of each stock cycle where stock level is comparable to W — any new order has a non-trivial chance of colliding with another on a different channel. The collision probability during this race window is 1 − e−W × (channels − 1) / channels. The overall oversell rate is the product of the race fraction and the collision probability.
This is an illustrative comparison, not a forecast. It uses only user-entered assumptions and is not calibrated against customer outcomes. Use the oversell-prevention playbook for the full operational response, including Amazon A-to-Z protection and eBay defect-rate recovery.