Bibliographic record
Abstract
The logistics outsourcing concept is to address the feasibility of using its own capabilities and sources of supply to perform certain logistics functions that the company can entrust to an external partner. However, in the current context of rapid change, it is important to make a quick and efficient decision on sources of supply: regardless of the sourcing model of the company. That is, what is the usual supply strategy for the company. Security (reliability and stability) of supplies is at the first place. Therefore, a quick decision on the optimal source of supply is the optimal solution. More precisely is the optimal combination of the use of internal resources of the company and the resources of external suppliers. Multisourcing is a type of outsourcing used by many companies in conditions of frequent changes. Unlike traditional outsourcing, the multisourcing model involves the use of several different vendors for the same product at different times. The decision depends on the level of security. For example, with multiple sources, a company can choose the best supplier for a particular task. By outsourcing certain operations, a company can perform critical tasks on its own. These actions can achieve optimization of operating costs. However, when deciding on multisourcing, it is important to assess the risks. It is important to estimate the cost of supply according to different options. You should compare the results of calculations and compare with the risks. These actions can ensure security of supply. So, the proposed economic and mathematical model is able to help to make the right decision of the rational choice of supply channel from several alternatives and, as a consequence, to achieve the following main goals: improving the quality of supply management; reduction of the logistics cycle; reduction of supply costs; increase the reliability of supply
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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, 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".