​Why Granular Retail Demand Forecasting Needs Multi-Tower Neural Networks

James Theuerkauf is an industry leader in machine-learning based demand forecasting & inventory optimization. He co-developed the LDSM.

Most retail demand is fairly predictable from far enough away. National weekly denim sales move with the calendar, promotions and the weather, and a competent statistical model will track them. But inventory decisions are made for one product, in one size, at one store, in one week.

At that level, history thins out fast. A planner deciding how many size eight boots to send to one store is often looking at mostly zeros. The M5 “Accuracy” competition, which used Walmart unit-sales data, confirmed that most item-store histories are intermittent: long runs of zero demand broken by the occasional sale.

After several years building forecasting systems for large apparel and footwear retailers, I treat this as a representation problem more than a time-series problem: describing products and stores well enough for a model to learn from all of them at once. The approach that has worked best for me is a multi-tower neural network built on learned embeddings.

Why Item-By-Item Forecasting Breaks Down

Take, for example, a running shoe that has sold in black for three seasons and is launching in green. The green version has no sales, but its style, its subclass and every store have years of history.

A traditional setup forecasts each item in each store from its own sales alone, discarding all of that, and leans on a planner’s hand-picked “like item” for new products.

Global models such as Amazon’s DeepAR moved the field forward by training one model across thousands of items. The open question is how it should describe products and stores.

What Products And Stores Have In Common

Entity embeddings, described by Cheng Guo and Felix Berkhahn, work differently. The model learns a short numerical profile for each style and store, like coordinates on a map. Products that sell alike end up close together, as do stores that behave alike.