Applying machine learning methods for supply chain risk management to predict supply chain disruptions
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URL
Journal Title
Journal ISSN
Volume Title
School of Business |
Master's thesis
Authors
Date
2022
Department
Major/Subject
Mcode
Degree programme
Information and Service Management (ISM)
Language
en
Pages
70
Series
Description
Thesis advisor
Basu, GautamKeywords
machine learning, supply chain risk management, disruptions, artificial intelligence