Finding slow moving stock by store, and where it sells instead
The short answer
A line with more than 16 weeks of cover in one store and under 6 in others is slow moving stock by store, and often a placement problem more than a product problem. Rank stores by weeks of cover for each line, then compare who buys the line where it sells with the customers in the slow store's trade area.
Why it matters
Stock that sits in the wrong store ends up marked down while other stores run short at full price. Matching slow stock to the stores where its buyers shop recovers margin without a new buy.
The question to ask
Copy this into Lexi as written.
What the answer looks like
Illustrative output on sample data
Illustrative example: how Lexi answers this for a footwear retailer. It compares stock and weekly sales of a premium trail running shoe across three stores, then looks at who buys it.
- Store A, downtown: 180 pairs, 6 a week, 30 weeks of cover
- Store B, near trails: 40 pairs, 14 a week, about 3 weeks of cover
- Store C, mall: 90 pairs, 10 a week, 9 weeks of cover
What the data shows
Store A holds over half the stock but sells the fewest pairs.
What it means
Store A's customers mostly buy road and lifestyle shoes. Trail shoe buyers live closer to Store B and buy two or more pairs of running shoes a year.
What to do
Move 100 pairs from A to B, taking Store A to about 13 weeks of cover and Store B to 10.
Would you like the same check on every slow-moving line?
Frequently asked questions
What counts as slow moving inventory?
Slow moving inventory sells below the pace needed to clear it at full price within its season or planned life. Weeks of cover is the simplest test: units on hand divided by average weekly sales. Set the threshold by category, since basics can carry more cover than seasonal lines.
Why is a store-level view of slow stock more useful than a network view?
A network total can show healthy sell through while individual stores hold months of stock and others run out. Markdowns then hit the whole network when the real problem is placement. Checking each store separates lines that need moving from lines that need clearing.
How do customers help decide where stock should go?
Past sales show where a line sold, which is limited by where it was stocked. Customer data shows who buys the line and where similar customers shop, including stores that never carried it. That points transfers and future allocation toward demand that sales history alone would miss.