数据驱动的客户接受主管家庭配送服务
Charlotte Köhler1, Ann Melissa Campbell2, Jan Fabian Ehmke3
1Department of Data Science and Decision Support, European University Viadrina, Frankfurt (Oder), Germany.
本研究引入了一种数据驱动的方法,以优化家庭交付安排. 通过分析历史订单数据,零售商可以提高交货效率和客户接受率,有效地管理稀缺的交货能力.
科学领域:
- 运营研究 运营研究
- 供应链管理 供应链管理
- 数据科学数据科学数据科学
背景情况:
- 家庭配送服务需要客户的存在,导致商定的交货时间窗口.
- 接受当前交货请求对未来客户可用性和路线效率的影响往往对零售商来说不清楚.
研究的目的:
- 开发和评估一种数据驱动的方法,用于管理家庭配送服务中稀缺的配送能力.
- 评估历史订单数据如何为客户接受决策提供信息,以优化路线效率和未来可用性.
主要方法:
- 一种基于采样的客户接受方法,利用历史订单数据.
- 一个数据科学过程,以确定决策的最佳数据最新性和采样量.
- 确定影响验收决策和零售商收入的关键特征.
主要成果:
- 拟议的方法有效地使用历史数据来管理交付能力.
- 识别的特征显著提高了交付验收决策的准确性.
- 通过优化日程安排,为零售商展示了更好的收入潜力.
结论:
- 历史订单数据对于有效管理交货能力和客户调度至关重要.
- 开发的数据科学方法增强了家庭配送服务的决策,平衡了当前和未来的客户需求.
- 零售商可以通过利用数据分析进行交付管理来提高运营效率和收入.
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