MCDM approach to investigate the effectiveness of SCRUM events in minimizing risk factors in project management
Bibliographic record
Abstract
Traditional project management methods have been used for many years in the implementation and management of projects. With the publication of the agile manifesto in 2001, the interest in agile methods increased and successful results were obtained in the execution and solution of complex projects with agile approaches. Agile approach consists of different structures that can be named as framework or method. Among these agile methods, which are selected and applied according to the suitability and purpose of the project, the most frequently preferred one is SCRUM. In this study, SCRUM, an agile project management technique that has been used frequently in recent years in order to develop a flexible project management process, has been examined, and the effectiveness of the events of the SCRUM technique in minimizing the risks that arise in project management has been evaluated. Determining the effectiveness of these SCRUM events, which each agile project team implements in turn, is very important for the correct and effective allocation of resources. For this purpose, a multi-criteria decision-making model has been proposed to evaluate the effectiveness of 4 SCRUM events within the scope of this study. While the importance weights of the 24 project management risk factors proposed in the solution phase of the created model were determined by the SWARA method, the SCRUM events were evaluated with the WASPAS, COPRAS and EDAS methods and the solution values found by three different methods were compared.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".