[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126386-en":3,"doc-seo-126386-105":31,"detail-sidebar-cat-0-en-105":92},{"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},126386,962085564549,"Genevieve","https://ap-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","A Machine Learning Approach to Inventory Stockout Prediction","The retail industry continues to face frequent stockouts intensified by ecommerce growth and disruptive shocks such as the COVID-19 pandemic, which undermine profitability and supply chain stability. Building accurate stockout prediction models is essential to improve operational efficiency and resilience. This study leverages a large retailer dataset covering over 1.6 million SKUs to train classical machine learning models using multiple features beyond sales. Results show strong performance on large, imbalanced data and improved predictive accuracy, with current inventory levels, three-month demand forecasts, and recent sales as key drivers, while near-term signals outperform longer-horizon 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","cbCaimwL9hiellZX","https://ap.wps.com/l/cbCaimwL9hiellZX","pdf",1402169,4,1,36,"English","en",105,"# Introduction\n## Inventory stockouts and disruption drivers\n## Motivation for machine learning prediction\n## Data and modeling approach","[{\"question\":\"Why are stockout prediction models increasingly important in retail?\",\"answer\":\"Stockouts are frequent due to ecommerce growth and disruptive events like the COVID-19 pandemic, which reduce profitability and weaken supply chain stability. Accurate prediction supports more efficient and resilient retail operations.\"},{\"question\":\"What dataset and modeling strategy are used in the study?\",\"answer\":\"The study uses a large retailer dataset covering over 1.6 million SKUs. It trains analytical models based on classical machine learning algorithms using multiple features alongside sales data.\"},{\"question\":\"Which factors most strongly influence stockout predictions?\",\"answer\":\"Feature importance analysis identifies current inventory levels, short-term demand forecasts for three months, and recent sales data as the most influential factors for predicting stockouts.\"}]","A Machine Learning Approach to Inventory Stockout Prediction | PDF",1785904788,91,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"a-machine-learning-approach-to-inventory-stockout-prediction","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/research-report/",3,{"item":53,"name":13,"@type":44,"position":20},"https://docshare.wps.com/document/a-machine-learning-approach-to-inventory-stockout-prediction/126386/",{"url":53,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-20","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Why are stockout prediction models increasingly important in retail?","Question",{"text":76,"@type":77},"Stockouts are frequent due to ecommerce growth and disruptive events like the COVID-19 pandemic, which reduce profitability and weaken supply chain stability. Accurate prediction supports more efficient and resilient retail operations.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What dataset and modeling strategy are used in the study?",{"text":81,"@type":77},"The study uses a large retailer dataset covering over 1.6 million SKUs. It trains analytical models based on classical machine learning algorithms using multiple features alongside sales data.",{"name":83,"@type":74,"acceptedAnswer":84},"Which factors most strongly influence stockout predictions?",{"text":85,"@type":77},"Feature importance analysis identifies current inventory levels, short-term demand forecasts for three months, and recent sales data as the most influential factors for predicting stockouts.","https://schema.org",{"og:url":53,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]