We talk about multi-indexing when one product appears in the information system under multiple different SKU numbers. This most commonly applies to products available in the basic collection (baseline), which remain in the fashion network’s merchandise assortment for many seasons. Examples include a white t-shirt or a beige camisole, which are considered “must-haves” in every woman’s wardrobe.
Another index number may result from differences in fabric composition, production year, or manufacturer. Often, these differences arise independently of retailers; for example, a manufacturer might use a different type of cotton due to new ISO standards, or production might be moved to a different factory. In other cases, the retailer itself decides to change suppliers, perhaps opting for an alternative manufacturer due to more favorable pricing or shorter lead times. Regardless of the reason, it may mean that the same product, from the perspective of the end customer who regularly visits a brand’s store, is labeled with different SKU numbers.
Consequences of multi-indexing
As a result, situations arise where “the same” products with different indexes circulate within the retail network – both in stores and in warehouses. On the one hand, there are older SKUs that have not yet sold, and on the other hand, new SKUs because the inventory has been replenished in the warehouse and then delivered to selected stores during replenishment. Even at the level of a single store, theoretically the same products may appear, but with different indexes, for example, because there was a need to replenish sizes.
Treating all indexes separately would significantly impact sales forecasts and store stocking. For example, it could turn out that additional units of a product wouldn’t be delivered to a store because they are no longer in stock for that SKU, even though, in reality, there is an identical product in the warehouse under a different SKU.
How we handle multi-indexing in Occubee?
At Occubee, we account for multi-indexing during sales forecasting, store demand generation, and picking orders, fully automatically.
For the newest product, we define reference products in the system (previous SKUs), thereby associating the same products from the customer’s perspective, appearing in the information system under different SKUs. As a result, sales forecasts for individual stores will be generated based on the combined historical sales of reference products, store demands will consider the store stock of such products combined, even if they appear under multiple SKUs, and picking instructions, following the FIFO (first in, first out) principle, will enforce sending the oldest products from the warehouse to stores first.