Sustainability and quality in the food supply chain. A case study of shipment of edible oils
Bibliographic record
Abstract
Purpose – Modern supply chains collect and deliver products worldwide and link vendors and consumers over thousands of miles. In the food industry, the quality of products is affected by manufacturing/processing and logistics activities, such as transportation and packaging. Specifically, transportation is likely the most critical step throughout the “food journey” from farm to fork because of the potential stresses that affect the products during shipment and storage activities. The purpose of this paper is to present and apply an original assessment of quality, safety and environmental effects due to the international distribution of food products via different container solutions. A case study that examines the shipment of edible oils from Italy to Canada demonstrates that the quality of a product at the place of consumption can be significantly affected by the use of different containers. Design/methodology/approach – A simulation-based quality assessment, combined with a life cycle and environmental analysis, supports the logistic manager in the decision-making process in order to guarantee the highest level of product quality at the place of consumption. Findings – The proposed approach and the illustrated case study demonstrate the importance of conducting safety and quality assessment combined with environmental analyses of sustainable food supply chains. Originality/value – This paper highlights the interdependency of implications and decisions on food quality and environmental sustainability of supply chain processes and activities.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".