[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126217-en":3,"doc-seo-126217-105":31,"detail-sidebar-cat-0-en-105":93},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126217,549768702563,"Sage","https://ap-avatar.wpscdn.com/avatar/8000c4aa63b76e948b?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786536092046926083",8,"Research & Report","A Machine Learning Approach to Inventory Stockout Prediction","The retail industry continues to suffer frequent stockouts, intensified by e-commerce growth and disruptive events such as the COVID-19 pandemic, affecting both profitability and supply chain stability. Effective stockout prediction is therefore essential to strengthen operational efficiency and resilience. This study uses a large retailer dataset with 1.6 million+ SKUs to build a model using classical machine learning, addressing data imbalance and uncertainty while improving predictive accuracy. Feature importance highlights current inventory levels, three-month demand forecasts, and recent sales data as key drivers, with near-term indicators outperforming six- and nine-month projections.","Journal Pre-proof  \nA Machine Learning Approach to Inventory Stockout Prediction  \nDr Yang Liu, Dr Dimitra Kalaitzi, Dr Michael Wang, Dr Christos Papanagnou  \nPII: S2773-0670(25)00020-2  \nDOI: [https://doi.org/10.1016/j.jdec.2025.06.002](https://doi.org/10.1016/j.jdec.2025.06.002)  \nReference: JDE 68  \nTo appear in: Journal of Digital Economy  \nReceived Date: 22 May 2025  \nRevised Date: 12 June 2025  \nAccepted Date: 15 June 2025  \nPlease cite this article as: Liu, Y. , Kalaitzi, D. , Wang, M. , Papanagnou, C. , A Machine Learning Approach to Inventory Stockout Prediction, Journal of Digital Economy, [https://doi.org/10.1016/](https://doi.org/10.1016/)[ ](https://doi.org/10.1016/)j.jdec.2025.06.002.  \nThis is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.  \n© 2025 The Author(s) . Published by KeAi Communications Co. , Ltd.  \nA Machine Learning Approach to Inventory Stockout Prediction  \nAbstract  \nThe retail industry continues to experience frequent stockouts, driven by the rise of ecommerce and disruptive events such as the COVID-19 pandemic, which have significantly impacted both profitability and supply chain stability. As a result, developing effective models for stockout prediction has become increasingly critical for enhancing the efficiency and resilience of retail operations. The growing availability of data, challenges posed by data imbalance, and high demand uncertainty underscore the need to transition from traditional forecasting models to more intelligent, data-driven approaches that integrate multiple relevant features alongside sales data. In this study, we utilise a large dataset from a retailer comprising over 1.6 million SKUs to develop an analytical model based on classical machine learning algorithms aimed at improving stockout prediction accuracy. Our results demonstrate that the proposed model performs well in handling large-scale, imbalanced data and significantly enhances predictive performance. Feature importance analysis reveals that current inventory levels, short-term demand forecasts (three months), and recent sales data are the most influential factors in predicting stockouts. Furthermore, the findings suggest that recent demand forecasts and sales data have greater predictive power than longer-term projections (six and nine months), highlighting the importance of near-term indicators in forecasting accuracy. To the best of our knowledge, these insights provide valuable contributions to understanding stockout dynamics and improving inventory management strategies within theretail sector.  \nCorresponding author: Dr Yang Liu, [kurt.liu@henley.ac.uk](kurt.liu@henley.ac.uk)  \nDr Yang Liu [kurt.liu@henley.ac.uk](kurt.liu@henley.ac.uk)[ ](kurt.liu@henley.ac.uk)Henley Business School University of Reading United Kingdom  \nDr Dimitra Kalaitzi  \n[dimitra.kalaitzi@cut.ac.cy](dimitra.kalaitzi@cut.ac.cy)  \nSchool of Management and Economics Cyprus University of Technology Cyprus  \nDr Michael Wang  \n[m.wang@kingston.ac.uk](m.wang@kingston.ac.uk)  \nDepartment of Management, Kingston University  \nGlobal Business School for Health, University College London United Kingdom  \nDr Christos Papanagnou[c.papanagnou@aston.ac.uk](c.papanagnou@aston.ac.uk)  \nCollege of Engineering and Physical Sciences Aston University  \nUnited Kingdom  \nA Machine Learning Approach to Inventory Stockout Prediction  \nAbstract  \nThe retail industry continues to experience frequent stockouts, driven by the rise of ecommerce and disr","cbCaitlDjpbWZO26","https://ap.wps.com/l/cbCaitlDjpbWZO26","pdf",1455585,9,1,36,"English","en",105,"# Introduction\n## Inventory stockouts and drivers\n## Data-driven stockout prediction with machine learning\n## Model development and evaluation\n## Feature importance and demand-horizon insights\n## Implications for inventory management","[{\"question\":\"Why is stockout prediction important in retail operations?\",\"answer\":\"Stockouts reduce profitability and disrupt supply chain stability, so accurate prediction supports more efficient and resilient retail inventory decisions.\"},{\"question\":\"What dataset and modeling approach does the study use?\",\"answer\":\"The study trains a classical machine learning-based model using a large retailer dataset containing over 1.6 million SKUs to improve stockout prediction accuracy.\"},{\"question\":\"Which factors are most influential for predicting stockouts?\",\"answer\":\"Feature importance shows that current inventory levels, three-month demand forecasts, and recent sales data are the strongest predictors.\"}]","A Machine Learning Approach to Inventory Stockout Prediction | 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is stockout prediction important in retail operations?","Question",{"text":77,"@type":78},"Stockouts reduce profitability and disrupt supply chain stability, so accurate prediction supports more efficient and resilient retail inventory decisions.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"What dataset and modeling approach does the study use?",{"text":82,"@type":78},"The study trains a classical machine learning-based model using a large retailer dataset containing over 1.6 million SKUs to improve stockout prediction accuracy.",{"name":84,"@type":75,"acceptedAnswer":85},"Which factors are most influential for predicting stockouts?",{"text":86,"@type":78},"Feature importance shows that current inventory levels, three-month demand forecasts, and recent sales data are the strongest 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