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A prescriptive analytics framework for efficient E-commerce order delivery
Decision Support Systems ( IF 7.5 ) Pub Date : 2021-04-30 , DOI: 10.1016/j.dss.2021.113584
Shanthan Kandula , Srikumar Krishnamoorthy , Debjit Roy

Achieving timely last-mile order delivery is often the most challenging part of an e-commerce order fulfillment. Effective management of last-mile operations can result in significant cost savings and lead to increased customer satisfaction. Currently, due to the lack of customer availability information, the schedules followed by delivery agents are optimized for the shortest tour distance. Therefore, orders are not delivered in customer-preferred time periods resulting in missed deliveries. Missed deliveries are undesirable since they incur additional costs. In this paper, we propose a decision support framework that is intended to improve delivery success rates while reducing delivery costs. Our framework generates delivery schedules by predicting the appropriate delivery time periods for order delivery. In particular, the proposed framework works in two stages. In the first stage, order delivery success for every order throughout the delivery shift is predicted using machine learning models. The predictions are used as an input for the optimization scheme, which generates delivery schedules in the second stage. The proposed framework is evaluated on two real-world datasets collected from a large e-commerce platform. The results indicate the effectiveness of the decision support framework in enabling savings of up to 10.2% in delivery costs when compared to the current industry practice.



中文翻译:

用于高效电子商务订单交付的规范分析框架

实现及时的最后一英里订单交付通常是电子商务订单履行中最具挑战性的部分。有效管理最后一英里运营可以显着节省成本并提高客户满意度。目前,由于缺乏客户可用性信息,交付代理遵循的时间表针对最短的旅行距离进行了优化。因此,订单未在客户首选的时间段内交付,从而导致错过交付。错过交货是不可取的,因为它们会产生额外的成本。在本文中,我们提出了一个决策支持框架,旨在提高交付成功率,同时降低交付成本。我们的框架通过预测订单交付的适当交付时间段来生成交付时间表。特别是,拟议的框架分两个阶段运作。在第一阶段,使用机器学习模型预测整个交付班次中每个订单的订单交付成功。预测用作优化方案的输入,该方案在第二阶段生成交付计划。所提出的框架是在从大型电子商务平台收集的两个真实世界数据集上进行评估的。结果表明,与当前的行业实践相比,决策支持框架可有效节省高达 10.2% 的交付成本。所提出的框架是在从大型电子商务平台收集的两个真实世界数据集上进行评估的。结果表明,与当前的行业实践相比,决策支持框架可有效节省高达 10.2% 的交付成本。所提出的框架是在从大型电子商务平台收集的两个真实世界数据集上进行评估的。结果表明,与当前的行业实践相比,决策支持框架可有效节省高达 10.2% 的交付成本。

更新日期:2021-06-14
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