The factors influencing modeling of collaborative performance supply chain: A review on fresh produce
Bibliographic record
Abstract
The aim of this study is to identify and explore the success factors that influence the fresh product supply chain collaborative performance system (CPS) towards the flow of information among partners along the chain, and the supply chain relationships of all partners in it by identifying the role played by information structures at the planning level of supply chain collaboration, as well as providing policy insights to stakeholders in different countries to analyze applicable implementation. This research method uses a research approach by reviewing the previous literature that was selected deliberately during the last 10 years; journal papers, conferences, working papers, and Ph.D. thesis. Using three steps, the first step found 189 articles. The second step was to get 96 articles that match the topics raised. Finally, the third step, determined 39 articles selected as important topics focusing on fresh production areas and they were categorized and analyzed. This study is considered to be our best knowledge to examine the success factors influencing CPS in FPSC, such as; knowledge of the benefits of collaborative performance systems, reluctance to change, collaborative culture, trust, technology and information, social relations, environmental friendliness, and sustainability security and safety. The theoretical framework, was also developed incorporating the principles of supply chain network collaboration, taking into account the importance of business strategy and inter-organizational network theory, to strengthen the evidence for the relationship between the collaborative planning levels in usable information flow, at both the strategic, operational and tactical levels in the supply chain collaboration. The implication of this research is intended to examine the success factors that influence it, so that it can be developed and become the basis for improvement models that are still rarely applied in this field, from the influencing factors that exist in the collaboration structure.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; 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".