Data-driven operations and supply chain management: established research clusters from 2000 to early 2020
Bibliographic record
Abstract
Despite the long-recognised importance of data-driven operations and supply chain management (OSCM) scholarship and practice, and the impressive development of big data analytics (BDA), research finds that firms struggle with BDA adoption, which suggests the existence of gaps in the literature. Therefore, we conduct this systematic literature review of journal articles on data-driven OSCM from 2000 to early 2020 to ascertain established research clusters and literature lacunae. Using co-citation analysis software and double-checking the results with factor analysis and multidimensional-scaling-based k-means clustering, we find six clusters of studies on data-driven OSCM, whose primary topics are identified by keyword co-occurrence analysis. Five of these clusters relate directly to manufacturing, which, in line with the existing literature, indicates the crucial role of production in OSCM. We highlight the evolution of these research clusters and propose how the literature on data-driven OSCM can support BDA in OSCM. We synthesise what has been studied in the literature as points of reference for practitioners and researchers and identify what necessitates further exploration. In addition to the insights contributed to the literature, our study is amongst the first efforts to deploy multiple clustering techniques to undertake a rigorous data-driven systematic literature review (SLR) of data-driven OSCM.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.023 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.034 | 0.061 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".