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Accuracy Is Not Service: A Decision-Aware Benchmark for Intermittent-Demand Forecasting — arXiv:2609.13840

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.13840
License (as archived): CC0-1.0
Archived: 2026-10-16T00:10:29.883Z
SHA-256 of the archived text: 16240606aaac3236f15ffede54717d1d6db481d59b80e01492da92ad7998a643
Stable URL of this page: https://metraipolis.com/documento/dc_2bd554d682ff

How to cite

Accuracy Is Not Service: A Decision-Aware Benchmark for Intermittent-Demand Forecasting — arXiv:2609.13840 — arXiv (en), https://arxiv.org/abs/2609.13840 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_2bd554d682ff (Metraipolis archive dc_2bd554d682ff, sha256 16240606aaac3236f15ffede54717d1d6db481d59b80e01492da92ad7998a643)

BibTeX

@misc{dc2bd554d682ff,
  title = {Accuracy Is Not Service: A Decision-Aware Benchmark for Intermittent-Demand Forecasting — arXiv:2609.13840},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.13840},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_2bd554d682ff; sha256 16240606aaac3236f15ffede54717d1d6db481d59b80e01492da92ad7998a643},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.13840. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

A contract-logistics spare-parts operator is paid on order-level service: an order counts only if every requested line is fulfilled, yet forecasters are selected based on line-level forecast accuracy. This disconnect matters when demand is intermittent and lumpy, histories are short, and lead times span months. We benchmarked 38 forecasting methods spanning classical, intermittent-demand, machine-learning, deep-learning, and pretrained foundation models. A common decision-aware protocol evaluates them on an industrial panel drawn from a live contract and two public datasets. Forecast-accuracy rank and order-service rank are negatively correlated on the industrial panel, at -0.555, across methods evaluated on 20,330 real multi-item orders. Service is more closely associated with the direction of cumulative forecast bias, including over-prediction during zero-demand periods, than with point accuracy. Examining bias in Chronos-2's instance normalization yields a training-free correction that lifts the per-material fill proxy from 77.5% to 92.0% (14.5 percentage points) at the 90% policy target and raises the complete-order fill rate from 54% to 63%. For reproducibility, we release RUF (Regenerate-Until-Fidelity), a method for generating fidelity-certified synthetic panels on which the findings reproduce. For intermittent demand, the lowest-error forecast need not deliver the highest service. Bias direction helps explain this gap, which can be reduced without retraining.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-15 in cs.LG).
Categorie arXiv: Machine Learning (cs.LG).
Pagina del lavoro: https://arxiv.org/abs/2609.13840 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-15T23:54:14.642Z dalla pagina https://arxiv.org/catchup/cs.LG/2026-09-15?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).