Technological advancements and challenges in food supply chain management: a scientometric analysis
Bibliographic record
Abstract
Purpose This study aims to synthesise existing research to provide a comprehensive understanding of the current state of food supply chains by evaluating technological advancements that can address the challenges in food supply chain management (SCM). Design/methodology/approach The scientometric analysis, a quantitative study using statistical methods, selected English publications from after 2014, excluding non-peer-reviewed articles. This study analysed 479 research papers, filtered from 621 articles indexed in Scopus. Findings The review highlights key challenges in food SCM: perishability, increased intermediaries, safety compliance and sustainability. Technological solutions such as blockchain, Internet of Things (IoT), artificial intelligence (AI) and big data (BD) analytics are promising tools to address these issues and attention to food wastage, security and catering services is also essential. Research limitations/implications The study relies on existing literature, possibly missing emerging technologies like multi-enterprise platforms for food certification. Practical implications This review offers valuable insights for food supply chain practitioners by highlighting current trends and contributions from different regions. It underscores the importance of technologies and considers adopting innovative technologies to enhance operations and efficiency. Social implications Accurate food SCM through technological solutions can reduce waste and lower the environmental impact of food production and distribution, emphasising sustainable SCM. Originality/value This study contributes to the existing body of knowledge by systematically reviewing the literature on food SCM and identifying the role of technology in overcoming its challenges. It also directs research on future trends of digitalisation of food SCM.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.039 | 0.166 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.141 | 0.204 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".