大规模商务场景的统计管理理论

Statistical Management Theory for Business Applications with Massive Scale

大规模商务场景是科学技术进步与商业实践发展的必然产物。大规模商务场景既覆盖了面向经济主战场的商务实践,也包括国家治理相关的重要领域,还关注数字孪生为核心的新一代数字管理技术。大规模商务场景的统计管理覆盖了管理学、经济学、计算机、环境治理、数学、统计学等多个交叉学科,为管理理论的创新提供了独特机遇。如何面向大规模商务场景,发展前沿统计方法,创新管理理论是政府部门、工业界和学术界共同关心的重要问题。基于国家自然科学基金委员会第344期“双清论坛”,本文从大规模商务场景出发,围绕复杂商务场景中的“数据分析方法”“统计计算与优化方法”以及“预测理论与管理决策”三方面进行了深入探讨。基于对相关概念的清晰界定和对国内外的重要文献进行系统梳理,总结了当前国内外研究现状与前沿,分析了发展趋势和方向,凝练了该领域未来5到10年的重大关键科学问题,探讨了前沿研究方向和科学基金资助战略。

The large-scale business scenario is an inevitable outcome of the progress in science and technology and the development of business practices.It not only encompasses business practices geared towards the economic forefront but also includes crucial areas related to national governance,with a focus on the new generation of digital management technology centered around digital twins.Spanning multiple interdisciplinary fields such as management,economics,computer science,environmental governance,mathematics,and statistics,the large-scale business scenario presents a unique opportunity for innovation in management theory.The development of cutting-edge statistical methods and the innovation of data-driven management theories tailored to the large-scale business scenario are important concerns shared by government agencies,industry,and the academic community.Based on the 344th issue of the“Shuang Qing Forum”,this paper initiates its exploration from the perspective of the large-scale business scenario,delving into three aspects within complex business scenarios:data analysis methods,statistical computation and optimization methods,and prediction theory and management decision-making.Through a clear definition of relevant concepts and a systematic review of important literature both domestically and internationally,the article summarizes the current research status and frontiers,analyzes development trends and directions,distills significant key scientific issues for the next 5 to 10 years in this field,and discusses cutting-edge research directions and strategies for scientific fund support.