Stock risk, days of cover and low-stock alerts
StockSense helps translate a quantity into a time-based decision. Daily in-stock sales velocity and on-hand inventory are used to show days of cover and surface products at risk of running out.
Dashboard prioritisation
See critical, low and healthy stock levels together so the team can start with the most urgent products.
Persistent alert decisions
Dismiss a condition when it has been reviewed; it stays dismissed until the underlying risk resolves or changes meaningfully.
Explainable SKU-level Stock Projection
Choose a tracked SKU and review the inputs behind its projected run-out and reorder context. The forecast is deliberately transparent rather than a single unexplained score.
Normal demand baseline
Uses the product’s in-stock Shopify sales velocity, avoiding the mistake of treating out-of-stock days as weak demand.
Historical and planned demand
Shows stronger comparable historical seasonality separately from a planned Sale Period uplift, when each is relevant.
Reviewable reorder points, not silent automation
StockSense can calculate a recommended reorder point from forecast daily demand during supplier lead time plus safety stock. The merchant sees the reason before applying it.
Lead-time aware
Supplier and product lead-time context changes the recommended timing when supply takes longer to arrive.
Merchant-controlled
The app does not silently overwrite a deliberate reorder buffer. A team member approves the suggested setting.
Purchase orders from draft to arrival
Create purchase orders, connect them to suppliers, record expected delivery dates and track their status from draft through to received. Incoming stock remains part of the planning picture.
Supply context in the forecast
On-order quantities and expected arrivals appear alongside on-hand stock to help avoid duplicate reordering.
Operational details included
Capture custom non-stock lines, shipping details and supplier information where the order needs more than a simple SKU list.
Plan for promotions and seasonal demand
Sale Periods let you set a demand uplift for an upcoming event. StockSense can also use stronger comparable prior-year demand, keeping historical evidence separate from a current promotion assumption.
Visible forecast components
See baseline demand, historical seasonal uplift and planned sale uplift as distinct drivers of the forecast.
Use when it matters
Apply the context to a particular product and sale period instead of changing ordinary demand for every SKU.
Barcode-supported stocktakes with a review step
Compatible USB and Bluetooth barcode scanners can enter Shopify barcode values or SKUs into a draft Stocktake. Each scan increments the saved count; a refresh does not reset it.
Draft before adjustment
Scan and review physical counts as a draft. Shopify inventory is not changed by scanning itself.
Submit when ready
Review variances, then choose whether to submit the stocktake and whether to sync the confirmed adjustment to Shopify.
Inventory value and lost-revenue context
Look beyond a total stock figure. Product-level retail and cost-value context helps show where inventory value sits, while lost-revenue reporting helps quantify an otherwise selling product’s stockout risk.
Product-level context
Review the value of inventory by product without inflating totals through bundled products.
Stockout consequence
Use prior in-stock sales to estimate the units and revenue that may have been missed during an unavailable period.
Keep large catalogues manageable
Choose which products should be tracked for planning, retain that choice through Shopify product syncs, and focus Stock Projection on the SKUs your team actually manages.
Tracked-product workflow
Keep long-tail or intentionally excluded products out of active planning views without losing the wider catalogue sync.
AI Brief as a review aid
Use the AI Brief to review risk and inbound-stock context. It supports a decision; it does not place orders or make unreviewed changes.
How StockSense approaches forecasting: it uses the merchant’s own Shopify sales, stock, incoming purchase orders, supplier timing and explicit sale assumptions. It does not claim to use cross-merchant data or an opaque model to predict consumer trends.
