Bibliographic record
Abstract
Purpose To analyze how e‐collaboration tools affect different partners along the supply chain, and to categorize firms according to their level of collaboration planning within a supply chain environment. Design/methodology/approach First, a field study, which focuses on one large telecommunications equipment manufacturer and a few strategic first‐tier suppliers, provides the basis to fully understand the e‐collaboration methods and the various issues and concerns of the different members of the supply chain. It is followed by an electronic survey conducted with 53 firms worldwide acting in the same supply chain, which constitutes the second phase of the study. Findings Different roles may be attributed to collaboration tools such as facilitating access to information, which affects knowledge creation capabilities, and assisting in the design of flexible supply chains. Furthermore, three separate groups with different levels and types of collaboration planning were identified. These groups appropriately represent the telecommunications equipment supply chain, where firms are either deeply involved in supply chain collaboration or very minimally concerned by it. Research limitations/implications By focusing on the initial stage of CPFR, we might overlook some important links with the other two stages of CPFR. However, with a more focused approach, we were able to obtain detailed information on the collaborative planning stage. A second limitation is the selection of one specific supply chain, which makes the generalization to other supply chains difficult. Practical implications Understanding the role of CPFR in their supply chain and, more importantly, the role of collaboration planning in developing a network of partners. Originality/value This paper looks at how collaboration is planned, through CPFR actions, between members of a supply chain.
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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".