What question did this study set out to answer?

Evaluate the effectiveness of foundation models for retail demand forecasting compared to traditional methods.

July 15, 2026Open Access

When Do Foundation Models Pay Off for Retail Demand Forecasting? A Systematic Review and Cross-Benchmark Meta-Analysis

Puntos clave

Evaluate the effectiveness of foundation models for retail demand forecasting compared to traditional methods.
Conducted a systematic review of approximately 150 papers from 2020 to 2026.
Performed gap-filling experiments and cross-benchmark extraction from fev-bench and GIFT-Eval.
Executed a model-level meta-regression on 543 comparisons between foundation and conventional models.
Foundation models show a significant advantage over conventional methods on distributional metrics.
Point-forecast metrics reveal a smaller benefit of foundation models compared to traditional baselines.
Findings come with considerations for sensitivity and practical cost-accuracy trade-offs.

Resumen

We present a PRISMA-informed systematic review of forecasting methods for short-term retail demand, combined with original gap-filling experiments and cross-benchmark extraction from fev-bench and GIFT-Eval. We screen approximately 150 papers from 2020 to 2026 and extract quantitative results into more than 800 rows spanning nine retail datasets. A model-level meta-regression on 543 paired comparisons across foundation models and conventional baselines finds a robust benchmark-level foundation-model advantage, strongest on distributional metrics and smaller on point-forecast metrics. The report includes sensitivity checks, benchmark-level caveats, and a practitioner-oriented cost-accuracy Pareto analysis.

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