Supply Chain Management and Development of Competencies: The Learning Logistics Concept and Applications
Bibliographic record
Abstract
Organizational competitiveness now lies in better mastery of a set of core competencies generated by the manipulation of resources stored in the organization's reservoir of knowledge. These resources may be internal or located in commercial partner organizations. It therefore seemed natural to combine resource-based theories with integrated logistics and supply chain management practices to produce a preliminary model. Our reflections led us to the conclusion that supply chain integration could result in a convergence of resources towards a limited number of beneficiaries, to the detriment of others. To reduce the risk of cannibalization and dilution of core competencies, managers must make full use of the knowledge available to them. This means 1) identifying and locating the organization’s own knowledge, 2) targeting the knowledge that can be shared without risk of losing strategic know-how, 3) making it available as required, while ensuring that it is not given free of charge, and 4) locating information held by external partners that is needed to strengthen a competitive position.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".