Understanding Transferable Supply Chain Lessons and Practices to a “High‐Tech” Industry Using Guidelines from a Primary Sector Industry: A Case Study in the Food Industry Supply Chain
Bibliographic record
Abstract
Flexibility and innovation at creating shapes, adapting processes, and modifying materials characterize composites materials, a "high-tech" industry. However, the absence of standard manufacturing processes and the selection of materials with defined properties hinder the configuration of the composites materials supply chain. An interesting alternative for a "high-tech" industry such as composite materials would be to review supply chain lessons and practices in "low-tech" industries such as food. The main motivation of this study is to identify lessons and practices that comprise innovations in the supply chain of a firm in a perceived "low-tech" industry that can be used to provide guidelines in the design of the supply chain of a "high-tech" industry, in this case composite materials. This work uses the case study/site visit with analogy methodology to collect data from a Spanish leading producer of fresh fruit juice which is sold in major European markets and makes use of a cold chain. The study highlights supply base management and visibility/traceability as two elements of the supply chain in a "low-tech" industry that can provide guidelines that can be used in the configuration of the supply chain of the composite materials industry.
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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 | 0.015 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.004 |
| 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; a candidate call from one teacher head, 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".