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, resulting in 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 leads companies to invest heavily in optimization engines, yet the resulting plans are often reworked, delayed, or ignored, with planners creating "shadow" spreadsheets and executives requesting simplified summaries that lack nuance and confidence.
In 2024, a national hardlines retailer addressed this problem directly by fusing optimization modeling with large language models.
Author's summary: AI helps retailer prevent stockouts by improving optimization modeling.
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