New method estimates demand transfer coefficients for large item universes

Researchers have developed a new method for estimating demand transfer coefficients at scale, which is crucial for optimizing store assortments and improving demand forecasting. This approach allows for the computation of these coefficients even with millions of items, addressing inefficiencies in existing literature that require separate forecasts for each possible assortment. The proposed procedure accurately estimates underlying coefficients and enhances demand forecasting when certain assumptions about substitution behavior are met. AI

IMPACT This research could lead to more efficient and accurate demand forecasting in retail, potentially impacting inventory management and sales optimization.

RANK_REASON The cluster contains a research paper published on arXiv detailing a new methodology. [lever_c_demoted from research: ic=1 ai=0.4]

Read on arXiv cs.AI →

• arXiv
• DagsHub
• Deep Narayan Mishra
• Demand Transfer
• Hugging Face
• IArxiv
• Logit Modeling

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