The Need for a Multidimensional Project Control Perspective
Bibliographic record
Abstract
A successful construction project relies heavily on planning and control. The prevalent practice is to adopt a single control method throughout a project, such as earned value management (EVM), which integrates costs and schedules to monitor project targets throughout execution. Although a single control method may be effective at a certain project phase, it might overlook important perspectives and be insufficient for other stages. Therefore, this study introduces a framework to evaluate the necessity for a multidimensional alternative perspective in project control. The study aims to diagnose some limitations of using a single method like EVM and demonstrate how adding another perspective, such as the last planner system (LPS) for project control, yields better insight into project performance. A design science research (DSR) methodology is adopted to address five key questions by conducting quantitative data analyses and Monte Carlo simulation for a large-scale project. The results show that although EVM performs well when dealing with controlled performance variability, it may yield undesirable results in uncontrolled performance variability, impacting its forecast accuracy. Moreover, the aspects covered by LPS in the project proved to be complementary to EVM. Furthermore, the results indicate an inconsistent divergence between planned and actual activities, resulting in disrupted flow and a purging effect, which can be attributed to the lack of a multidimensional approach in project control. The practical implication of these findings is that using a multidimensional perspective offers a more robust and adaptable project control strategy that improves forecast accuracy and project flow, especially under uncontrolled performance variability conditions, where single-method approaches like EVM alone may falter. Hence, adopting a multidimensional perspective can significantly enhance the management of construction projects, leading to more reliable outcomes and efficient resource utilization.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".