基于需求不确定性的数据驱动库存管理研究综述OACHSSCDCSTPCD

In recent years,with the increasing abundance of high-quality data,continuous development of machine learning techniques and significant improvements of computational capabilities,data-driven inventory management is experiencing unprecedented development opportunities.However,comprehensive and systematic reviews of research advances in this emerging field are currently lacking.In this study,an in-depth analysis of 183 academic papers is conducted using bibliometrics,and the state of the art in this field is visualized through scientific knowledge graphs.Then,the research results of data-driven inventory management from the perspectives of big data and operation management are summarized and synthesized in three aspects:demand information,basic models and basic methods.Essentially,this paper introduces four inventory management models from the perspectives of demand uncertainty and feature data:univariate data-driven newsvendor model,univariate data-driven dynamic inventory model,multi-feature data-driven newsvendor model and multi-feature data-driven dynamic inventory model.On this basis,six main data-driven decision-making methods are summarized:Bayesian analysis,robust optimization,sample average approximation,quantile regression,operation statistics and machine learning.Finally,future research directions and suggestions are discussed from the perspectives of methodologies,tools,challenges,and application hotspots in data-driven inventory management,aiming to provide valuable references and insights for researchers and practitioners in the relevant fields,and to foster the continuous development of data-driven inventory management.