An information system for assessing the likelihood of child labor in supplier locations leveraging Bayesian networks and text mining
Andreas Thöni (),
Alfred Taudes () and
A Min Tjoa ()
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Andreas Thöni: Vienna University of Technology
Alfred Taudes: Vienna University of Economics and Business
A Min Tjoa: Vienna University of Technology
Information Systems and e-Business Management, 2018, vol. 16, issue 2, No 9, 443-476
Abstract 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.
Keywords: Social sustainability; Supply chain risk management; Child labor; Bayesian network risk model; Text mining (search for similar items in EconPapers)
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