An Unstructured Big Data Approach for Country Logistics Performance Assessment in Global Supply Chains

Aseem Kinra*, Kim Sundtoft Hald, Raghava Rao Mukkamala, Ravi Vatrapu

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review


Purpose – The purpose of this study is to explore the potential for the development of a country logistics performance assessment approach based upon textual big data analytics. Design/methodology/approach – The study employs design science principles. Data were collected using the Global Perspectives text corpus that describes the logistics systems of 20 countries from 2006–2014. The extracted texts were processed and analysed using text analytic techniques, and domain experts were employed for training and developing the approach. Findings – The developed approach is able to generate results in the form of logistics performance assessments. It contributes towards the development of more informed weights of the different country logistics performance categories. That said, a larger text corpus and iterative classifier training is required to produce a more robust approach for benchmarking and ranking. Practical implications – When successfully developed and implemented, the developed approach can be used by managers and government bodies, such as the World Bank and its stakeholders, to complement the Logistics Performance Index (LPI). Originality/value – A new and unconventional approach for logistics system performance assessment is explored. A new potential for textual big data analytic applications in supply chain management is demonstrated. A contribution to performance management in operations and supply chain management is made by demonstrating how domain-specific text corpora can be transformed into an important source of performance information
Original languageEnglish
JournalInternational Journal of Operations and Production Management
Issue number4
Pages (from-to)439-458
Number of pages20
Publication statusPublished - 2020

Bibliographical note

Published online: 5. March


  • Digital science
  • Global supply chains
  • Big data and machine learning
  • CSCMP global perspectives
  • Logistics Performance Index (LPI)
  • Trade Facilitation
  • Neo-institutional economics
  • Public policy

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