Please use this identifier to cite or link to this item: http://hdl.handle.net/11718/24403
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dc.contributor.authorKandula, Shanthan
dc.contributor.authorKrishnamoorthy, Srikumar
dc.contributor.authorRoy, Debjit
dc.date.accessioned2021-10-18T08:01:38Z
dc.date.available2021-10-18T08:01:38Z
dc.date.issued2021-04-30
dc.identifier.citationKandula, S., Krishnamoorthy, S., & Roy, D. (2021). A prescriptive analytics framework for efficient E-commerce order delivery. Decision Support Systems, 113584.en_US
dc.identifier.urihttps://doi.org/10.1016/j.dss.2021.113584
dc.identifier.urihttp://hdl.handle.net/11718/24403
dc.description.abstractAchieving 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.en_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofDecision Support Systemsen_US
dc.subjectAnalyticsen_US
dc.subjectData-driven deliveryen_US
dc.subjectMachine learningen_US
dc.subjectVehicle routingen_US
dc.subjectE-commerceen_US
dc.titleA prescriptive analytics framework for efficient E-commerce order deliveryen_US
dc.typeArticleen_US
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