How AI Helped a Retailer Prevent Stockouts
A national retailer combined optimization modeling with large language models to address the issue.
Supply chain leaders have long utilized optimization to make sense of complexity, from network design to replenishment, as mathematical models promise clarity in the face of uncertainty.
However, while these models excel at generating optimal solutions, they often fail to communicate them effectively. The optimization software outputs do little to reassure a planner, who must execute the plan, and when the plan cannot be explained, it will not be adopted.
This communication gap creates a paradox, where companies invest heavily in optimization engines, yet the resulting plans are reworked, delayed, or ignored, leading to the creation of "shadow" spreadsheets by planners and simplified summaries by executives that strip nuance and confidence.
In 2024, a national hardlines retailer confronted this problem directly, seeking a solution to the long-standing issue.
When the plan cannot be explained, it will not be adopted.
Author's summary: AI helps retailer optimize stock.
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