Project Management for a Plant Implementation: Success or failure?
Bibliographic record
Abstract
Observation: there are many projects launched daily. However, few of these projects achieve the expected results. Various reasons create gaps between the final result and the initial objectives, among others, poor definition of the problem or project and, at times, an incompetent manager. Our first goal is to evaluate the management of a company using a project management theoretical model. The strengths and weaknesses of project management are identified during this comparative analysis. The second goal is to compare these strengths and weaknesses in order to verify the short and medium-term impact on this project. The following weaknesses were identified from the comparative analysis: poor definition of the problem to be solved and the project itself, as well as the lack of audit and project closure. These weaknesses, in the case studied, led to the company's closing after only three years in business. To ensure that the company's other projects do not suffer the same fate, it would be important for the company to further study its project management system and implement a project management process based on a recognized model. [ABSTRACT FROM AUTHOR] \nCopyright of Journal of Modern Project Management is the property of Editora Mundo and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.
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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 | 0.033 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".