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


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2018-05

AUTHORS

Andreas Thöni, Alfred Taudes, A Min Tjoa

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. More... »

PAGES

443-476

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s10257-018-0368-0

DOI

http://dx.doi.org/10.1007/s10257-018-0368-0

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1100856712


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