Bibliographic record
Abstract
Purpose Supply chain risk (SCR) has been extensively explored in various sectors, yet there is a notable scarcity of SCR studies in the dairy industry. This study aims to identify the primary and distinctive risks in the dairy supply chain (DSC), propose a typological model for SCR, highlight challenges specific to the DSC and offer mitigation strategies. Design/methodology/approach We employ a systematic literature review to collect and review relevant research articles published between 2010 and 2019 to identify the main risks and mitigation strategies associated with the DSC, enabling the construction of a typological model of DSC risks. Findings Results of the systematic review of the SCR literature show that the main DSC risks include on-farm risk (e.g. risks originating from the farming system), off-farm risk (e.g. supply risk, demand risk and manufacturing risk) and inherent SCR (e.g. logistics risk, information risk and financial risk). Notably, we find that the farming system plays a key role in today’s agricultural supply chain operations, indicating the importance of considering on-farm risk in the entire DSC. Additionally, mitigation strategies are located in response to the identified DSC risks by the typology of DSC risks. Originality/value This paper is the first attempt to develop a typological model of SCR for the dairy industry by a systematic literature review. The findings contribute to providing a comprehensive understanding of DSC risks by bridging the gap of ignoring the on-farm risks of the DSC in the existing literature. The typology may serve as a guide in practice to develop mitigation strategies in response to DSC risks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".