Authors:
Hanen Neji
1
;
Mouna Rekik
2
;
Lotfi Souifi
1
and
Ismail Bouassida Rodriguez
1
Affiliations:
1
ReDCAD,ENIS,University of Sfax, Tunisia
;
2
MIRACL,ISIMS,University of Sfax, Tunisia
Keyword(s):
Sustainable Supplier Selection, Sentiment Analysis, Text Analytics, Multi-Criteria Decision-Making (MCDM).
Abstract:
Artificial intelligence (AI) algorithms have significantly advanced various fields, driving innovation in domains such as healthcare, finance, and sustainability. In the realm of sustainable development, selecting suppliers is crucial for promoting environmental responsibility and safeguarding the well-being of future generations. This complex decision-making process requires evaluating suppliers across numerous criteria. Multi-Criteria Decision-Making (MCDM) and AI techniques, including Natural Language Processing (NLP), Deep Learning (DL), and Machine Learning (ML), have emerged as powerful tools to address these challenges. However, these methods often face transparency issues and the risk of greenwashing, which can erode trust in sustainability assessments. To address this, we conducted a systematic literature review (SLR) of 44 papers published between 2019 and 2024, sourced from databases such as Springer (12 papers), IEEE Xplore Digital Library (11 papers), and Science Direct
(21 papers). This review offers an equitable analysis of MCDM and AI models (NLP, DL, ML) for evaluating both supplier sustainability and the risk of greenwashing. Additionally, sentiment analysis techniques are integrated to enhance transparency and provide insights into stakeholder perceptions.
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