Returns analysis: by reason, item and supplier

With the growth of online trade, Telegram shops and marketplaces, returned goods have become a permanent stream in the warehouse. Many treat them as a "side effect" of sales and count only the total. But returns analysis answers three questions: why goods come back, which item and supplier they come from, and how far it depends on the warehouse's own work. The answers give different departments different tasks.

Calculating the rate correctly

Return rate = units returned ÷ units sold. The main mistake is dividing this month's returns by this month's sales. A return usually arrives a few days or weeks after the sale, so part of February's returns belongs to goods sold in January. The right way is to tie them to the sales period: record how many came back, and within what time, from the goods sold in January. Once the return window has closed, the rate stops changing and can be compared with confidence.

For example, 1 000 units were sold in January. In January itself 30 came back, in February another 15: 45 units in all, or 4.5 percent. Look at the February report and January's returns will "stick" to February's sales, giving a wrong rate.

Example: by reason

In a month 3 000 units were sold and 120 came back: the rate is 120 ÷ 3 000 = 4.0 percent. By reason:

• Defective or damaged: 36 units, 30 percent.
• Customer chose the wrong size or model: 30, 25 percent.
• Not as described: 24, 20 percent.
• Changed their mind: 18, 15 percent.
• Warehouse error (a different item was sent): 12, 10 percent.

Reasons differ by category, so build the table by category too. The reason gives each department its own task. A defect is a matter for the supplier. Size and description mean correcting the listing and the size chart. A change of mind is the result of the returns policy. A warehouse error is only 12 units, but this is the part you can reduce yourself: confirming picking with a scanner is enough.

By supplier

Look at the same data by supplier:

• Supplier A: 1 000 units sold, 70 returned, 7.0 percent.
• Supplier B: 1 200 sold, 30 returned, 2.5 percent.
• Supplier C: 800 sold, 20 returned, 2.5 percent.

A makes up a third of sales (33 percent) but accounts for 70 ÷ 120 ≈ 58 percent of returns. Suppose 30 of the 36 defective units came from A. With these numbers you can now file a claim with the supplier, press for a lower price or ask for a quality guarantee. One month is too little; look at at least three months together, especially if sales volume is small.

The cost of a return

A return is not only a lost sale. Transport, receiving, inspection and repacking are added to it. Suppose one return costs 35 000 so'm on average: 120 × 35 000 = 4.2 million so'm a month, not counting the loss on goods that cannot be sold again. If you split this sum by reason, it becomes clear which direction to fix first. One more part belongs in the cost: while goods sit under inspection they are not sold, and the money stays tied up.

Where returned goods go in the warehouse

Returned goods need their own status and place: "under inspection". After inspection there are four ways forward: back to sale, repair or repack, write off as defective, return to the supplier. If a defective item is put straight on the shelf, it is sold again and returned again. If a return is not documented at all, a surplus or shortage appears at the count: the item physically exists but not in the records. Do not keep goods in the "under inspection" status for more than three days: otherwise money and space are tied up again.

Conclusion

Returns analysis is a move from "how many came back" to "why, whose task it is and what it cost". Through stock counts and discrepancy analysis, CountLab separates return-related record errors from count results, and in the management report provides the breakdown by reason and supplier.