Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/308114 
Erscheinungsjahr: 
2023
Quellenangabe: 
[Journal:] Annals of Operations Research [ISSN:] 1572-9338 [Publisher:] Springer US [Place:] New York, NY [Year:] 2023 [Pages:] 1-58
Verlag: 
Springer US, New York, NY
Zusammenfassung: 
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.
Schlagwörter: 
Data analytics
Predictive analytics
Data science
Big data
Data mining
Machine learning
Supply chain
Logistics
Data-driven optimisation
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