For example, jeans are a common donation item. Jeans have these attributes:
|Type:||4 types (e.g. Womens, Mens, Girls, Boys)|
|Brand:||6 brands (e.g. Levi’s, Lucky Brand, Calvin Klein, Guess, True Religion, Other)|
|Color:||4 colors (e.g. Blue, Black, White, Other)|
|Size:||12 sizes (e.g. 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36)|
|Condition:||3 conditions (e.g. Good, Better, Best)|
Thus, a single clothing item ‘jeans’ expands into 4 x 6 x 4 x 12 x 3 = 3456 SKUs.
Assuming 5 discount colors and 40 donation locations, the SKUs grow to 691,200.
Assuming 50 clothing items and 300 pricers, the clothing inventory grows to 10,368,000,000 SKUs.
Hard lines inventory is typically 3 times larger than the clothing inventory. Even the world’s largest super computer would find 40 billion part numbers hard to deal with. It is impossible for a standard PC (cash register) to have this many items in its inventory.
Clearly, for the thrift industry, using SKUs to keep track of inventory isn’t the right solution.
One way traditional retail solutions are configured to combat this issue is to limit the attributes to keep track of. This will reduce the inventory size. However, it will also eliminate the ability to measure certain attributes:
- You won’t know which pricer’s items are selling and which pricer’s are not.
- You won’t know which donation center items sell better than others.
- You won’t know which items have been sitting on the floor the longest.