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Optimising Supply Chain Logistics System Using Data Analytics Techniques

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Book cover Intelligent Transport Systems. From Research and Development to the Market Uptake (INTSYS 2019)

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Abstract

The transport sector’s share of global energy-related carbon emissions is about 23%. Transportation and logistics can improve the economic growth of nations and profitability in businesses, and if efficiently designed and managed their carbon footprints will be reduced. Important progresses have been made to enhance the efficiency of logistics supply chain using mathematical optimisation techniques. However, recent needs in collaborative supply chain on one hand, and advancements in data science have heightened the need for optimisation techniques based on big data analytics. This paper studies and evaluates models for European freight transport logistics actions utilising advanced data analytics solutions. Three new supply chain algorithms of horizontal collaboration, pooling, and physical internet have been developed using historical data of European road freight transport. Then, two indicators of sustainability and efficiency were used to evaluate each developed strategy. The results have shown that there is substantial potential in pursuing these strategies and encourages future research into logistic supply chain and data analytic methods for designing sustainable transport systems.

Supported by University College Dublin and University of Leeds.

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Acknowledgements

This publication has emanated from research conducted between School of Computer Science (UCD) and Business School (University of Leeds) as part of the collaborative/network project “Big Data Analytics for Sustainable Global Supply Chains” and data provided by the Commission (Eurostat) in the framework of the above mentioned collaborative/network project (Research entity identification number:2014/219/UK).

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Authors and Affiliations

  1. School of Computer Science, University College Dublin, Dublin, Ireland

    Eleni Mangina & Pranav Kashyap Narasimhan

  2. UCD Energy Institute, University College Dublin, Dublin, Ireland

    Eleni Mangina & Mohammad Saffari

  3. School of Mechanical and Materials Engineering, University College Dublin, Dublin, Ireland

    Mohammad Saffari

  4. La Rochelle Business School, Excelia Group, La Rochelle, France

    Ilias Vlachos

Authors
  1. Eleni Mangina
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  2. Pranav Kashyap Narasimhan
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  3. Mohammad Saffari
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  4. Ilias Vlachos
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Corresponding author

Correspondence to Eleni Mangina .

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Editors and Affiliations

  1. ISCTE-IUL, Lisbon, Portugal

    Ana Lúcia Martins

  2. ISCTE-IUL, Lisbon, Portugal

    Joao Carlos Ferreira

  3. University of Pisa, Pisa, Italy

    Alexander Kocian

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© 2020 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Mangina, E., Narasimhan, P.K., Saffari, M., Vlachos, I. (2020). Optimising Supply Chain Logistics System Using Data Analytics Techniques. In: Martins, A., Ferreira, J., Kocian, A. (eds) Intelligent Transport Systems. From Research and Development to the Market Uptake. INTSYS 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 310. Springer, Cham. https://doi.org/10.1007/978-3-030-38822-5_6

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  • DOI: https://doi.org/10.1007/978-3-030-38822-5_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-38821-8

  • Online ISBN: 978-3-030-38822-5

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