Comparative Study of Methodologies for Schedule Management in an Environment of Multiple Simultaneous Projects
Bibliographic record
Abstract
A project is a unique event that has an established deadline and with a purpose to meet a specific need of the team interested in the project. The objective of this work was to identify which method would be the most adequate for the reality of the studied environment and to show the benefits and losses in the adoption of each one of these methods. To achieve this objective, an analysis of 25 projects was carried out between the years of July 2019 and June 2019 to obtain a sufficient database and with these data to carry out a comparative study between three different methods of estimating deadlines in relation to what was actually practiced. The projects were divided into six main stages, the opening of the project, approval of the purchase order, delivery, confirmation of the start of operations, capitalization of assets and closing of the project. The first stage of data collection was to capture the number of days required to complete each stage in each of the 25 projects analyzed and thereby calculate minimum, maximum and average points of execution. With the data obtained from these projects, a simulation was made for the case of using the adapted media, Pert and Pert methodology. The studied environment has as a singularity the occurrence of multiple simultaneous projects and taking place in different stages. After comparative analyzes, it was Pert for presenting a greater balance between the metrics "projects within the deadline" and "variation of project X actual," however, the study also showed a lot of instability in the processes studied, so future studies to understand the discrepancy for the amount of days needed to perform a similar activity on different projects.
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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 | 0.017 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".