AI inventory forecasting estimates future demand; it does not establish exactly how much stock you will need. Evaluate a forecast using your own products, lead times, and errors before letting it influence purchasing. A forecast that looks accurate in aggregate can still overstock slow sellers and miss a key variant.
Editorial correction, October 12, 2026: removed unsupported accuracy and savings percentages, unverified app-price recommendations, and the recommendation to adopt Stocky. This is a documentation-based planning method, not a report of tests in a merchant store. Original publication dates are preserved.
Start with the current Shopify workflow
Shopify's Stocky transition guidance says Stocky is no longer available as of August 31, 2026. It directs merchants to Shopify's inventory tools and describes Sidekick helping with reorder suggestions and draft purchase orders. Do not start a new process around a retired app or assume historical Stocky records automatically migrated.
The purchase-order overview separates the supplier agreement from receiving through a linked transfer. A demand suggestion is an input to that agreement; it is not a received shipment. Keep approval of quantities, costs, and delivery terms with a responsible person.
Define the decision the forecast will support
Choose a product group, location, forecast period, and review date. “How many blue mugs should we order for the next replenishment cycle?” is specific enough to evaluate. “Improve inventory with AI” is not.
Record the supplier lead time and the time until the next buying decision. If a delivery takes three weeks and you review purchasing weekly, your planning window needs to account for both. Check minimum order quantities, carton sizes, cash limits, and any known promotion separately.
For an illustrative product, suppose the team plans for 80 units of demand during its chosen window, wants a 15-unit buffer, has 35 usable units, and expects 20 usable units to arrive in time. The planning quantity is 80 + 15 - 35 - 20 = 40 units before supplier pack rules. If the incoming 20 arrive too late, that assumption changes. None of these figures is a recommended buffer or a forecast generated by Shopify.
Clean the observations before comparing predictions
A week with no sales might mean no demand, or it might mean the product was unavailable. Record stockout periods, unusual promotions, returns, changes in variants, and interrupted store operation. Ask how a proposed tool handles each situation rather than assuming it can infer every missing sale.
Shopify's inventory-state definitions distinguish Available, Committed, Unavailable, and Incoming stock. Use the appropriate quantities in your planning model. Damaged items on hand are not necessarily usable supply, and an incoming shipment is not available today.
Keep source data and adjustments traceable. If a staff member replaces a demand input, record the reason so the later evaluation compares the forecast that actually informed the order.
Compare against a simple baseline
Before buying a forecasting app, save a simple estimate such as the average of comparable recent weeks. Record its assumptions and use the same products and time periods for the candidate forecast.
Consider three hypothetical weekly outcomes:
| Week | Actual units | Baseline estimate | Candidate estimate |
|---|---|---|---|
| One | 20 | 18 | 21 |
| Two | 30 | 24 | 27 |
| Three | 10 | 18 | 16 |
Absolute errors total 16 units for the baseline and 10 for the candidate. That is a useful arithmetic comparison, not proof that the candidate will perform better next month. Three weeks is a small example, and the cost of underestimating one high-margin item may differ from overestimating another.
Review bias as well as total error. Repeatedly ordering too much can consume cash even if the percentage errors look modest. Include slow-moving products in the evaluation rather than only the successful cases.
Evaluate software on the workflow you can verify
Ask each candidate to explain its supported data, forecast horizon, treatment of stockouts, purchase-order handoff, permissions, and current total charges. Preserve direct app listings and vendor documentation when comparing them. A feature list or demonstration is not evidence of the savings your store will earn.
Start with recommendations that a person approves. Keep an override log and compare actual deliveries and sales with the saved forecast. Do not automatically purchase stock because an assistant produced a confident explanation.
Use the partial receiving guide to reconcile incoming shipments and the draft-order reservation guide for customer stock promises. Forecasting becomes more useful when the surrounding inventory records describe what the business can actually buy, hold, and deliver.