An information system for assessing the likelihood of child labor in supplier locations leveraging Bayesian networks and text mining

Andreas Thöni, Alfred Taudes, AMin Tjoa

Publication: Scientific journalJournal articlepeer-review

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This paper presents an expert system to monitor social sustainability compliance in supply chains. The system allows to continuously rank suppliers based on their risk of breaching sustainability standards on child labor. It uses a Bayesian network to determine the breach likelihood for each supplier location based on the integration of statistical data, audit results and public reports of child labor incidents. Publicly available statistics on the frequency of child labor in different regions and industries are used as contextual prior. The impact of audit results on the breach likelihood is calibrated based on expert input. Child labor incident observations are included automatically from publicly available news sources using text mining algorithms. The impact of an observation on the breach likelihood is determined by its relevance, credibility and frequency. Extensive tests reveal that the expert system correctly replicates the decisions of domain experts in the fields supply chain management, sustainability management, and risk management.
Original languageEnglish
Pages (from-to)443 - 476
JournalInformation Systems and e-Business Management
Issue number2
Publication statusPublished - 2018

Austrian Classification of Fields of Science and Technology (ÖFOS)

  • 102 not use (legacy)
  • 101015 Operations research
  • 102001 Artificial intelligence
  • 502 not use (legacy)
  • 502028 Production management
  • 502012 Industrial management
  • 502044 Business management

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