{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T05:52:06Z","timestamp":1791525126883,"version":"4.3.4"},"reference-count":46,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T00:00:00Z","timestamp":1791504000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Artif. Intell."],"abstract":"<jats:p>Demand forecasting has been shown to be one of the key facilitators of successful supply chain and inventory management. In this work, we introduce a benchmarking pipeline for demand forecasting in retail that includes data preprocessing, comparative benchmarking of different machine learning\/deep learning (ML\/DL) approaches, and decision-focused evaluation. Unlike other works that compare predictive performance of ML\/DL models and end there, our pipeline links reproducible ML\/DL benchmarking and decision-focused evaluation through cost of inventory management in order to derive costs from forecasting error\/bias. We also present a scalability study on another retail dataset. Several standard forecasting models, such as Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), CNN-LSTM, and XGBoost, are compared using a unified experimental pipeline on benchmark retail datasets. The experimental results reveal that while the deep learning models efficiently identify temporal dependencies, tree-based ensemble algorithms, especially XGBoost, consistently deliver better results in terms of forecasting accuracy on structured retail demand datasets. A scalability case study on the Rossmann Store Sales benchmark evaluates the top-performing model on a larger, richer dataset. Moreover, the study shows the connection between forecast accuracy and the subsequent inventory performance by performing a cost-based evaluation of inventory outcomes of forecasts made using various models.<\/jats:p>","DOI":"10.3389\/frai.2026.1936360","type":"journal-article","created":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T05:34:47Z","timestamp":1791524087000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["A systematic benchmarking pipeline for demand forecasting and inventory-aware supply chain decision support"],"prefix":"10.3389","volume":"9","author":[{"given":"Suleiman Ali","family":"Alsaif","sequence":"first","affiliation":[{"name":"Computer Department","place":["Deanship of Preparatory Year and Supporting Studies, Imam Abdulrahman Bin Faisal University, Saudi Arabia"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,10,9]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1803.01271","article-title":"An empirical evaluation of generic convolutional and recurrent networks for sequence modeling","author":"Bai","year":"2018","journal-title":"arXiv Preprint arXiv"},{"key":"B2","doi-asserted-by":"publisher","first-page":"1798","DOI":"10.1109\/TPAMI.2013.50","article-title":"Representation learning: a review and new perspectives","volume":"35","author":"Bengio","year":"2013","journal-title":"IEEE Trans. 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