Demand forecasting with language models in the loop
Demand and sales forecasting appear on 66 recent papers in the operations research and AI journals, but only 9 of them carry a language-model, foundation-model or text keyword. Retailers now feed product descriptions, reviews, weather and news into forecasting systems. Does text improve forecasts for new products, where history is missing, and does it help where history is long?
What prompted this#
Since October 2025, 66 papers carry a demand-forecasting, sales-forecasting or demand-prediction keyword (31 in operations research journals, 22 in AI): "demand forecasting" on 42, "sales forecasting" on 7, "retail demand forecasting" on 3. Only 9 of them also carry a language-model, foundation-model, transformer, deep-learning or text keyword.
Questions#
- For new products with no sales history, do embeddings of descriptions and images predict first-season demand better than analogue-based methods, and by how much?
- Does adding text (reviews, news, promotions) improve forecasts for established products, or add noise?
- Do the forecast gains translate into lower inventory and fewer stockouts once ordering rules use them?
Data that could help#
Retail sales data with product descriptions and images (public datasets such as the M5 and fashion retail datasets), and inventory records from a retailer.
Next step#
Compare new-product forecast errors from an embedding-based model and an analogue method on a fashion retail dataset with product images and text.