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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.
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No potential conflict of interest was reported by the author.
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Notes on contributors
Tino T. Herden
Tino T. Herden is Research Associate at Institute of Technology Berlin (Technische Universität Berlin), in the Logistics department. He is investigating the use of Business Analytics in a logistics and supply chain context, an evolving field called Supply Chain Analytics. His research focus is on the strategic use of Supply Chain Analytics while he teaches strategic and operational use.