How AI Helped a Retailer Prevent Stockouts
A national retailer combined optimization modeling with large language models to address supply chain complexity.
Supply chain leaders have long used optimization to make sense of complexity, from network design to replenishment, as mathematical models promise clarity in uncertain situations.
However, these models often fail to communicate their solutions effectively, leading to a communication gap between optimization software outputs and planners who must execute the plans.
When the plan cannot be explained, it will not be adopted.
This paradox results in companies investing heavily in optimization engines, only to have the resulting plans reworked, delayed, or ignored.
In 2024, a national hardlines retailer confronted this problem directly by fusing optimization modeling with large language models.
Author's summary: AI helps retailer prevent stockouts by improving supply chain optimization.