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Similarity-matching in decision-making processes of Supply Chain Analytics: a systematic literature review

ABSTRACT Supply Chain Analytics (SCA) has recently shown positive impact on Supply Chain Management (SCM) and the simultaneously trending concept of Data Science could become a valuable addition to SCA. In this article, structural frameworks for the analytical approaches in SCA and types of tasks in Data Science are identified. As a starting point for integrating SCA and Data Science, the type of Data Science task ‘similarity matching’ is chosen and investigated under consideration of SCA. A systematic literature review is conducted and nine different decision-making processes in SCM are identified and the different approaches to these tasks have been investigated for further Data Science aspects. The results reveal promising approaches for advanced SCA supported by Data Science aspects.