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Big data analytics in e-commerce logistics: Findings from a systematic review and a case study

Authors: Eleni Zampou; Christina Milioti; Aggelos Liapis; Vega Rodrigalvarez; Florian Flocke; George Dimitrakopoulos; George Bravos;

Big data analytics in e-commerce logistics: Findings from a systematic review and a case study

Abstract

Digital evolution has significantly changed consumer shopping habits and expectations resulting in a major growth of e-commerce. The immediate outcome of this growth was the creation of a dynamic and turbulent environment with increasing density of the distribution network. This environment consists of many delivery points, multiple delivery channels and last-mile delivery requirements. Due to its complexity and the proliferation of data challenges, e-commerce logistics is an area where the application of Big data analytics can be proven to be extremely fertile. Despite the growing interest, there are limited studies that investigate the role of Big data. By using a design science approach, we clarified the current e-commerce logistics practices as well as the envisioned ones that can, to a large extent, be supported by appropriate big data technologies. We concluded to a set of business requirements that express the needs towards the e-commerce logistics. Then, the requirements were translated into a set of use case scenarios to demonstrate how they could be supported by big data analytics. We conclude by proposing a conceptual architecture of a big data analytics artefact that could cover the e-commerce logistics requirements.

Keywords

big data, business analytics, e-commerce logistics, collaboration, requirements

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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