How AI Helped a Retailer Prevent Stockouts
A national retailer combined optimization modeling with large language models to address supply chain complexities.
Supply chain leaders have long relied on optimization to make sense of complexity, from network design to replenishment, using mathematical models to provide clarity in uncertain situations.
However, these models often struggle 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, with planners creating "shadow" spreadsheets and executives requesting simplified summaries that lack nuance and confidence.
In 2024, a national hardlines retailer confronted this problem directly, seeking a solution to prevent stockouts and improve supply chain efficiency.
Author's summary: AI helps retailer optimize supply chain.
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