在零售部门确定机器学习的应用领域:一篇文献综述和采访研究
Clemens Brackmann1, Marek Hütsch1, Tobias Wulfert1
1Universitätsstraße 2, 45141 Essen, Germany.
概括
机器学习 (ML) 可以自动化许多零售任务. 本研究确定了在线和离线零售的21个ML应用,重点是决策和运营效率.
科学领域:
- 信息系统信息系统信息系统
- 零售管理 零售管理
- 人工智能的人工智能
背景情况:
- 零售业的手工任务越来越多地使用机器学习 (ML) 进行计算机化.
- 现有的ML实施模型缺乏针对零售业的具体指导.
- 确定零售业内的精确ML应用对于有效采用至关重要.
研究的目的:
- 在实体零售和电子商务零售中识别和分类机器学习应用领域.
- 制定一个框架,指导从业者和研究人员选择适当的ML用例.
- 在示例零售场景中,在工艺层面分析ML应用.
主要方法:
- 对225篇研究论文进行结构化文献审查,以确定潜在的ML应用和信息系统架构.
- 文学发现的三角化与八个专家采访的见解.
- 在两个特定的零售过程中探索ML应用.
主要成果:
- 在线和线下零售中确定了ML的21个不同的应用领域.
- 这些应用程序的分类主要是以决策为导向和经济运营任务.
- 焦点区分:线下零售中心的ML集中在商品上,而电子商务的ML优先考虑客户.
结论:
- 机器学习为优化各种零售业务提供了巨大的潜力.
- 一个结构化的框架有助于识别和实施合适的零售ML应用.
- 零售业的ML采用需要量身定制的策略,考虑到在不同的零售道中对商品与客户的独特关注.
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