Handling Data Problems in Machine Learning Applications in Supply Chain Management.

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Focusing on the intersection of data augmentation (DA) and machine learning (ML), this dissertation addresses challenges posed by poor data quality in ML applications. It explores various DA methods, aiming to clarify their benefits and obstacles in practical use. Through a multiple-case study, the research demonstrates how DA can enhance the performance and applicability of ML techniques, specifically within supply chain management, thereby contributing valuable insights to both fields.