AI demand forecasting case study: from stockout panic to planned purchase orders

The starting problem

A Shopify accessories store sells about 540 units of a tote SKU every 30 days, has 420 units on hand, expects 120 inbound units, and needs 14 days for supplier lead time. A promotion is planned next week, so a simple moving average would understate demand.

The practical question is not "what is next quarter revenue?" The buyer needs to know whether to place a purchase order now, how many units to buy, and which SKU deserves attention before cash is trapped in slow inventory.

• Input sales from the last 180 days so seasonality and promotion spikes can be separated.
• Calculate 30, 60, and 90 day demand at SKU level instead of category level.
• Convert the forecast into a reorder point, safety stock, and purchase date.

The operating result

A good AI demand forecasting workflow gives the buyer a decision-ready replenishment list. It should flag the stockout window, recommend a reorder quantity, and later compare actual sales against the prediction so the model gets more trustworthy over time.

The win is behavioral: the team stops opening spreadsheets only after a SKU is already red. They review exceptions weekly and place purchase orders before the risk becomes expensive.

Quick answers