An enterprise default discriminant model based on optimal misjudgment loss ratio

Data science and big data analytics (DS &BDA) methodologies and tools are used extensively in supply chains and logistics (SC &L). However, the existing insights are scattered over different literature sources and there is a lack of a structured and unbiased review methodology to systematise DS &BDA application areas in the SC &L comprehensively covering efficiency, resilience and sustainability paradigms. In this study, we first propose an unique systematic review methodology for the field of DS &BDA in SC &L. Second, we use the methodology proposed for a systematic literature review on DS &BDA techniques in the SC &L fields aiming at classifying the existing DS &BDA models/techniques employed, structuring their practical application areas, identifying the research gaps and potential future research directions. We analyse 364 publications which use a variety of DS &BDA-driven modelling methods for SC &L processes across different decision-making levels. Our analysis is triangulated across efficiency, resilience, and sustainability perspectives. The developed review methodology and proposed novel classifications and categorisations can be used by researchers and practitioners alike for a structured analysis and applications of DS &BDA in SC &L.

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An enterprise default discriminant model based on optimal misjudgment loss ratio

Cited by 2 publications

References 79 publications

Intelligent Methods for Restructuring Digital Science-Intensive Solutions of Network Innovation Institutions

Intelligent Methods for Restructuring Digital Science-Intensive Solutions of Network Innovation Institutions

Data science and big data analytics: a systematic review of methodologies used in the supply chain and logistics research

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