Supply chain risks: findings from Brazilian slaughterhouses
Bibliographic record
Abstract
Purpose The purpose of this paper is to identify the risks to which agrifood supply chains are exposed and to analyze how these risks impact the degree of coordination of the chain. Design/methodology/approach The present investigation was executed in two steps. Initially, a literature review and interviews with slaughterhouse managers were carried out to identify the main risks to which agrifood supply chains are exposed. The second step consisted of a survey involving 66 Brazilian slaughterhouses to identify how the perception of risks influences the degree of coordination in the examined chains. Findings The study revealed that risks, transaction costs and creation of collaborative advantages are determining factors in defining the degree of coordination in the analyzed agrifood supply chains. Practical implications The results allow slaughterhouse managers to more easily recognize the risks to which the supply chains are exposed and evaluate in more detail strategies for relationships with their suppliers. These strategies may be able to avoid conflict and create value for the supplier by stimulating longer relationships and facilitating animal purchase transactions for slaughter. This can lead to quality improvements, lower costs and reduced risk. Originality/value Studies of risks in agrifood supply chains are rare in comparison with those developed in other sectors. The present investigation is innovative in identifying the main risks specific to agrifood supply chains and associating those risks with a degree of coordination that minimizes them.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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; both teacher heads agree on what is shown here.
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".