A Process-Improvement Method for Implementing Earned Value–Based Project Management in Organizations
Bibliographic record
Abstract
Project monitoring has become crucial to the success of organizations in an increasingly competitive industrial environment. As such, earned value management (EVM) is an essential part of project cost control. Over the years, the scientific literature has mainly focused on developing mathematical models to improve EVM, whereas very few researchers have examined the implementation of EVM in organizations. Although some standards provide general guidelines for implementing the methodology, they rarely consider the maturity of the organization or the strategy required to implement it. This paper aims to develop an EVM implementation plan method for organizations. The method identifies the activities to be carried out, their sequencing, and the corresponding roles, techniques, and deliverables associated with each activity. The analysis of this phase of the existing situation allows for a consideration of the organization’s maturity level based on a process mapping analysis and its audit of the organization. Relevant recommendations are then identified before defining EVM implementation strategies according to the organization’s needs, constraints, and resources. Interviews with professionals allowed for the verification of the relevance of the recommendations before assigning them to the defined implementation strategies at every stage of implementation. The impact of each strategy on the organization’s processes is mapped and analyzed to determine the expected costs and benefits. The implementation method was tested successfully within a firm.
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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 | 0.013 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".