Please use this identifier to cite or link to this item: http://hdl.handle.net/10125/63898

Shortening Delivery Times by Predicting Customers' Online Purchases: a Case Study in the Fashion Industry

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Title:Shortening Delivery Times by Predicting Customers' Online Purchases: a Case Study in the Fashion Industry
Authors:Weingarten, Jennifer
Spinler, Stefan
Keywords:Intelligent Decision Support and Big Data for Logistics and Supply Chain Management
anticipatory shipping
big data analytics
online purchases
prediction
show 1 moresupply chain management
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Date Issued:07 Jan 2020
Abstract:Online retailers still struggle with the disadvantage of delivery times compared to traditional brick and mortar stores. With the emergence of big data analytics, it has become possible to extract meaningful knowledge from the volume of data that online retailers collect on their website. Nevertheless, limited research exists that investigates how this data can be used to optimize delivery times for customers. The goal of this paper is to develop a prediction model for anticipatory shipping, which predicts customers' online purchases with the aim of shipping products in advance, and subsequently minimize delivery times. Different forecasting methods in combination with k-means clustering are applied to test if, and how early, it is possible to predict online purchases. Results indicate that customer purchases are, to a certain extent, predictable, but anticipatory shipping comes at a high cost due to wrongly sent products. The proposed prediction model can easily be implemented and used to predict purchases, which can also be leveraged for other areas of application besides anticipatory shipping.
Pages/Duration:10 pages
URI:http://hdl.handle.net/10125/63898
ISBN:978-0-9981331-3-3
DOI:10.24251/HICSS.2020.159
Rights:Attribution-NonCommercial-NoDerivatives 4.0 International
https://creativecommons.org/licenses/by-nc-nd/4.0/
Appears in Collections: Intelligent Decision Support and Big Data for Logistics and Supply Chain Management


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