Assortment Planning and Optimization with Reinforcement Learning
Executive Summary Retailers face complex decisions in assortment planning – determining the optimal mix of products to stock across stores and channels. Traditional methods struggle to account for changing customer preferences, demand uncertainty, and the myriad factors influencing product performance. Reinforcement Learning (RL) offers a data-driven, dynamic approach to optimize assortments in real time. By […]
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