Methodological Development of a Textual Big Data Analytics Approach for Country Logistics Performance

Research output: Contribution to conferencePaperResearchpeer-review

Abstract

Purpose: The purpose of this study is to develop a country logistics performance assessment methodology and tool based upon a textual data analytics approach.
Design/methodology/approach: The study uses a design science approach to develop and train the prototype performance classification tool from a text corpus of country logistics assessments. Data were collected from 21 text documents from 20 to 60 pages, describing the logistics systems of 20 countries from 2006 to 2014. The extracted texts were tokenised with Natural Language Toolkit (NLTK) and Python programming language, and techniques such as Keyword Analysis, Word Frequency Analysis, Collocation Analysis, and Text Classification are employed.
Findings: The methodology is able to generate results in the form of logistics performance assessments that can complement the existing performance assessment systems such as the Logistics Performance Index (LPI). The analysed text corpus, CSCMP Global Perspectives, is found to contain largely positive and descriptive information about the analysed countries. It is found that a deep involvement of both the logistics domain-expert and the text analyst is required in order to produce usable knowledge and a robust methodology.
Practical implications: When successfully implemented the developed methodology can be used by managers and government bodies such as the World Bank and its stakeholders to complement the existing assessment methods such as the Logistics Performance Index (LPI).
Originality/value: The methodology developed in this paper is new and unconventional for logistics system assessment and appraisal. The research can help enhance the operational and strategic business intelligence capabilities of organisations by creating a new, additional source of knowledge on country logistics performance for decision makers. The paper also demonstrates the potential of textual data analytics in supply chain management as an alternative big data application.
Original languageEnglish
Publication date2019
Number of pages16
Publication statusPublished - 2019
EventThe 31st NOFOMA Conference 2019: Annual Nordic Logistics Research Network Conference: Supply Chains and Sustainable Development of Societies - BI - campus Oslo, Oslo, Norway
Duration: 12 Jun 201914 Jun 2019
Conference number: 31
https://www.bi.edu/about-bi/calendar/2019/june/nofoma2019/

Conference

ConferenceThe 31st NOFOMA Conference 2019
Number31
LocationBI - campus Oslo
CountryNorway
CityOslo
Period12/06/201914/06/2019
Internet address

Bibliographical note

CBS Library does not have access to the material

Keywords

  • Logistics
  • Performance Assessment
  • Trade Facilitation
  • Logistics Performance Index (LPI)
  • Computer Aided Text Analysis (CATA)
  • Machine learning
  • Big Data

Cite this

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title = "Methodological Development of a Textual Big Data Analytics Approach for Country Logistics Performance",
abstract = "Purpose: The purpose of this study is to develop a country logistics performance assessment methodology and tool based upon a textual data analytics approach. Design/methodology/approach: The study uses a design science approach to develop and train the prototype performance classification tool from a text corpus of country logistics assessments. Data were collected from 21 text documents from 20 to 60 pages, describing the logistics systems of 20 countries from 2006 to 2014. The extracted texts were tokenised with Natural Language Toolkit (NLTK) and Python programming language, and techniques such as Keyword Analysis, Word Frequency Analysis, Collocation Analysis, and Text Classification are employed.Findings: The methodology is able to generate results in the form of logistics performance assessments that can complement the existing performance assessment systems such as the Logistics Performance Index (LPI). The analysed text corpus, CSCMP Global Perspectives, is found to contain largely positive and descriptive information about the analysed countries. It is found that a deep involvement of both the logistics domain-expert and the text analyst is required in order to produce usable knowledge and a robust methodology.Practical implications: When successfully implemented the developed methodology can be used by managers and government bodies such as the World Bank and its stakeholders to complement the existing assessment methods such as the Logistics Performance Index (LPI).Originality/value: The methodology developed in this paper is new and unconventional for logistics system assessment and appraisal. The research can help enhance the operational and strategic business intelligence capabilities of organisations by creating a new, additional source of knowledge on country logistics performance for decision makers. The paper also demonstrates the potential of textual data analytics in supply chain management as an alternative big data application.",
keywords = "Logistics, Performance Assessment, Trade Facilitation, Logistics Performance Index (LPI), Computer Aided Text Analysis (CATA), Machine learning, Big Data, Logistics, Performance Assessment, Trade Facilitation, Logistics Performance Index (LPI), Computer Aided Text Analysis (CATA), Machine learning, Big Data",
author = "Aseem Kinra and Hald, {Kim Sundtoft}",
note = "CBS Library does not have access to the material; null ; Conference date: 12-06-2019 Through 14-06-2019",
year = "2019",
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Methodological Development of a Textual Big Data Analytics Approach for Country Logistics Performance. / Kinra, Aseem; Hald, Kim Sundtoft.

2019. Paper presented at The 31st NOFOMA Conference 2019, Oslo, Norway.

Research output: Contribution to conferencePaperResearchpeer-review

TY - CONF

T1 - Methodological Development of a Textual Big Data Analytics Approach for Country Logistics Performance

AU - Kinra, Aseem

AU - Hald, Kim Sundtoft

N1 - CBS Library does not have access to the material

PY - 2019

Y1 - 2019

N2 - Purpose: The purpose of this study is to develop a country logistics performance assessment methodology and tool based upon a textual data analytics approach. Design/methodology/approach: The study uses a design science approach to develop and train the prototype performance classification tool from a text corpus of country logistics assessments. Data were collected from 21 text documents from 20 to 60 pages, describing the logistics systems of 20 countries from 2006 to 2014. The extracted texts were tokenised with Natural Language Toolkit (NLTK) and Python programming language, and techniques such as Keyword Analysis, Word Frequency Analysis, Collocation Analysis, and Text Classification are employed.Findings: The methodology is able to generate results in the form of logistics performance assessments that can complement the existing performance assessment systems such as the Logistics Performance Index (LPI). The analysed text corpus, CSCMP Global Perspectives, is found to contain largely positive and descriptive information about the analysed countries. It is found that a deep involvement of both the logistics domain-expert and the text analyst is required in order to produce usable knowledge and a robust methodology.Practical implications: When successfully implemented the developed methodology can be used by managers and government bodies such as the World Bank and its stakeholders to complement the existing assessment methods such as the Logistics Performance Index (LPI).Originality/value: The methodology developed in this paper is new and unconventional for logistics system assessment and appraisal. The research can help enhance the operational and strategic business intelligence capabilities of organisations by creating a new, additional source of knowledge on country logistics performance for decision makers. The paper also demonstrates the potential of textual data analytics in supply chain management as an alternative big data application.

AB - Purpose: The purpose of this study is to develop a country logistics performance assessment methodology and tool based upon a textual data analytics approach. Design/methodology/approach: The study uses a design science approach to develop and train the prototype performance classification tool from a text corpus of country logistics assessments. Data were collected from 21 text documents from 20 to 60 pages, describing the logistics systems of 20 countries from 2006 to 2014. The extracted texts were tokenised with Natural Language Toolkit (NLTK) and Python programming language, and techniques such as Keyword Analysis, Word Frequency Analysis, Collocation Analysis, and Text Classification are employed.Findings: The methodology is able to generate results in the form of logistics performance assessments that can complement the existing performance assessment systems such as the Logistics Performance Index (LPI). The analysed text corpus, CSCMP Global Perspectives, is found to contain largely positive and descriptive information about the analysed countries. It is found that a deep involvement of both the logistics domain-expert and the text analyst is required in order to produce usable knowledge and a robust methodology.Practical implications: When successfully implemented the developed methodology can be used by managers and government bodies such as the World Bank and its stakeholders to complement the existing assessment methods such as the Logistics Performance Index (LPI).Originality/value: The methodology developed in this paper is new and unconventional for logistics system assessment and appraisal. The research can help enhance the operational and strategic business intelligence capabilities of organisations by creating a new, additional source of knowledge on country logistics performance for decision makers. The paper also demonstrates the potential of textual data analytics in supply chain management as an alternative big data application.

KW - Logistics

KW - Performance Assessment

KW - Trade Facilitation

KW - Logistics Performance Index (LPI)

KW - Computer Aided Text Analysis (CATA)

KW - Machine learning

KW - Big Data

KW - Logistics

KW - Performance Assessment

KW - Trade Facilitation

KW - Logistics Performance Index (LPI)

KW - Computer Aided Text Analysis (CATA)

KW - Machine learning

KW - Big Data

M3 - Paper

ER -