Data mining and operations research techniques in Supply Chain Risk Management: A bibliometric study.

• Autor(a) principal: da Silva, Juliana Bonfim Neves
• Data de Publicação: 2020
• Outros Autores: Senna, Pedro, Chousa, Amanda, Coelho, Ormeu
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Brazilian Journal of Operations & Production Management (Online)
• Texto Completo: https://bjopm.org.br/bjopm/article/view/966
• topic: Bibliometry; Supply Chain Risk Management; Data Science; Operations Research

GOAL: This paper aims to carry a bibliometric study to map how data mining and operations research techniques are being applied to Supply Chain Risk Management. DESIGN/METHODOLOGY/APPROACH: We conducted a bibliometric analysis implemented in R language (bibliometrix package) using Systematic Literature Review approach to conduct the search. RESULTS: As the main results we highlight the gap we found in the literature considering Data Mining techniques in Supply Chain Risk Management and we set a full panorama of this stream of research. LIMITATIONS OF THE INVESTIGATION: We used Scopus database which allows recovering peer-reviewed texts from dozens of strong databases, nevertheless, we can not guarantee that all relevant documents were recovered. In addition, we considered only full published papers published in English language. PRACTICAL IMPLICATIONS: Managers and companies that are related in a supply chain must gradually redesign processes to include Data Mining techniques to support SCRM processes and activities along the SC. ORIGINALITY / VALUE: The paper showed the updated panorama of Data Mining implementation regarding SCRM. We did not find any similar studies, which shows our unique contribution.