Bibliographic record
Abstract
This paper presents findings from an exploratory study that investigates the extent to which Continuous Collaborative Improvement (CCI) activities are implemented in the supply chains of Canadian industries. Several Canadian industries including the automotive, electronics and aerospace sectors were examined to determine: (i) what CCI activities were initiated, (ii) which supply chain nodes were the most proactive in establishing these endeavors, (iii) what are the most effective collaborative tools and processes, and (iv) the effect such tools would have on the supply chain performance of participating companies. The results indicate that Canadian companies are placing greater strategic importance on supply chain performance. Supply chain nodes are engaging in joint strategic planning to a greater extent than they did a decade ago. However, certain industries such as the automotive and aerospace sectors are decidedly more integrated, as cost control and on-time deliveries are strategic imperatives in these businesses. The efforts are being initiated more at the customer level than at the upstream supplier nodes. The most effective tools are quality standards such as ISO 9000, EDI usage, improvements process such as JIT and lean manufacturing, and the establishment of performance targets for suppliers. These efforts are resulting in improvements in variables such as quality, lead-time, on-time delivery and cost and operational efficiencies.
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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".