For an Effective Management of the Functional Capacities of Companies: A Study of Pharmaceutical Companies
Bibliographic record
Abstract
The development of industry, continuous innovation and shortening production times and demands from customers to deliver products in the right quantities, at the right time, at the right price and with better quality have led to increased competitive pressures. These visions have created risks and disruptions in the supply chain. This could compromise the achievement of the targeted objectives as well as the continuity of the operating cycle, or even the sustainability of the company. Consequently, it is recommended to identify, upstream, any incident having an impact on the company's functional capacities. It is only by controlling these risks that manufacturers can guarantee the smooth running of their logistics activities, the objective of this work. This work is clearly based on the identification and evaluation of risks in an emerging economy through a qualitative approach based on interviews and a questionnaire with purchasing and distribution managers. The results displayed allow managers to refocus on the priorities to be solved in order to design viable and livable organizations, or even to act effectively to correct the errors that have been revealed or to continue and increase its development. As a case study we chose the pharmaceutical sector in the Constantine region. This is the first time this type of study has been done in Algeria.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".