Advanced predictive-analysis-based decision support for collaborative logistics networks

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Abstract

Purpose – The purpose of this paper is to examine challenges and potential of big data in heterogeneous business networks and relate these to an implemented logistics solution.
Design/methodology/approach – The paper establishes an overview of challenges and opportunities of current significance in the area of big data, specifically in the context of transparency and processes in heterogeneous enterprise networks. Within this context, the paper presents how existing components and purpose-driven research were combined for a solution implemented in a nationwide network for less-than-truckload consignments.
Findings – Aside from providing an extended overview of today’s big data situation, the findings have shown that technical means and methods available today can comprise a feasible process transparency solution in a large heterogeneous network where legacy practices, reporting lags and incomplete data exist, yet processes are sensitive to inadequate policy changes.
Practical implications – The means introduced in the paper were found to be of utility value in improving process efficiency, transparency and planning in logistics networks. The particular system design choices in the presented solution allow an incremental introduction or evolution of resource handling practices, incorporating existing fragmentary, unstructured or tacit knowledge of experienced personnel into the theoretically founded overall concept.
Originality/value – The paper extends previous high-level view on the potential of big data, and presents new applied research and development results in a logistics application.

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  • Advanced predictive-analysis-based decision support for collaborative logistics networks

    Rights statement: © Elisabeth Ilie-Zudor, Anikó Ekárt, Zsolt Kemeny, Christopher Buckingham, Philip Welch, Laszlo Monostori. Published by Emerald Group Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 3.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial & non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/3.0/legalc

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Details

Original languageEnglish
Pages (from-to)369-388
Number of pages10
JournalSupply Chain Management
Volume20
Issue number4
DOIs
StatePublished - 2015

Bibliographic note

© Elisabeth Ilie-Zudor, Anikó Ekárt, Zsolt Kemeny, Christopher Buckingham, Philip Welch, Laszlo Monostori. Published by Emerald Group Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 3.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial & non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/3.0/legalcode Funding: EU FP7 (257398); and the National Innovation Office of Hungary - project “Cyber-physical systems in the production and in the related logistics” (ED_13-2-2013-0002)

    Keywords

  • decision-support systems, logistics, collaboration

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