Scholars and practitioners have long recognised the importance of data-driven operations and supply chain management (OSCM), which typically centres on production and logistics. Given the impressive development of big data analytics (BDA), there are many papers on BDA in OSCM, possibly indicating a shift of focus in OSCM studies. Nevertheless, research finds that firms struggle with BDA adoption (Boldosova 2019, Caesarius and Hohenthal 2018), which suggests the existence of gaps in the literature. Therefore, we conduct this systematic literature review (SLR) of research on data-driven OSCM from 2000 to early 2020 to identify established research clusters and literature lacunae. Using co-citation analysis software and double-checking the results with factor analysis and multidimensional scaling as methodological triangulation, we find six research clusters on data-driven OSCM, whose primary topics are identified by keyword co-occurrence analysis. Overall, manufacturing is commonly-studied in data-driven OSCM scholarship, and quantitative modelling dominates research on data-driven inventory management, demand forecasting and transportation. However, case study, survey, and conceptual modelling are more frequently deployed in research on data-driven SCM and manufacturing system integration. In addition to the insights contributed to the literature, our study is amongst the first efforts to undertake a data-driven methodologically-triangulated SLR of data-driven OSCM.