By Jim Lewis, CEO Enhanced Retail Solutions LLC

Inventory planners are very busy these days. Recently we’ve had several clients request help with store to store transfers, lining up stores from one retailer to another and matching store locations to ship points from online orders. If you’ve ever tried to do this by hand, you know how much time it can take. Spreadsheets are a great tool, but with large datasets it can break down quickly.

Back in my buying days, there were many times where we wanted to move inventory from one store to another. Back then, it wasn’t so easy. And for many retailers, it still isn’t. But with the advent of the Omnichannel model, more retailers were forced to invest in logistics infrastructure that makes it easier.

The Benefits

There are many benefits of store matching including balancing sizes, colors or products based on community preferences. Wholesalers can also benefit from proximity analysis, especially in regard to optimizing assortments across their retailer partners. For example, if an item does well at retailer A and could also be a good fit for retailer B, they can match locations between A and B that are within 1 to 2 miles of each other. While some retailers may not like that idea, it is actually beneficial to them because it reduces the risk of putting a product in a store where it won’t sell, and feeding the locations with more inventory where it has the best change of selling.

Match one retailer’s store locations with another retailer’s locations

The Methodology

Several years ago we wrote an Excel macro that matched stores together given a specified radius. It’s based on a simple formula that calculates the difference between 2 points using latitude and longitude and takes into account the curvature of the Earth. But it was limited to copying and pasting 2 store lists into 2 different tabs. Using AI to help re-write our logic as a SQL query, we can now return all possible combinations between 50+ retailers. In seconds. Visualizing it and providing filters enables us to quickly get to the store combinations extremely quickly.

Adding Complexity

Matching stores based on distance is one thing, but adding additional business rules to the logic adds complexity. For example, maybe you want to match stores that have a glut of size small with stores that sold out of size small. And what if you only wanted to move inventory if it won’t break the size scale- more complexity. There are many other examples like Integrating demographic profiles and matching not just on distance, but on a shared demographic like income, age or ethnicity.

In a retail environment where every sale counts, optimizing where inventory goes is essential. We know there are costs associated with moving products between stores, so you have to do the match to ensure it is worthwhile. But if you are not thinking about this type of analysis, it could be a good time to start!