Multi-indicator supply chain management framework for food convergent innovation in the dairy business
Bibliographic record
Abstract
A comprehensive integrated framework of indicators currently used in lean, agile, sustainable and resilient supply chain paradigms is developed under the umbrella of convergent innovation (CI) and applied to the management of the supply chain of a dairy company, including procurement, processing and distribution of their products to customers. CI is a meta-framework that opens new frontiers for commercial innovation, supply chains and market systems by making the convergence of economic, social and environmental outcomes the target of business and actor decisions throughout society to build supply and demand for commercially viable outcomes. This framework provides a company with a multi-indicator supply chain management tool designed to accommodate the supply chain paradigms of being lean, agile, sustainable and resilient, as well as providing milk-based essential nutrition within a process called convergent innovation. The proposed analytical framework can serve as a decision support tool to systematically evaluate and improve the dairy supply chain from plant production to retailers. In jurisdictions without a quota system for milk production at the farm level, the system constructed in this paper could be expanded to handle farm production and shipment to dairy processing plants.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".