System Dynamics as an Assistive Tool to Delay Analysis in Identifying Productivity Losses
Bibliographic record
Abstract
It is well established in the construction literature that change orders have negative impacts on project productivity. Such impacts have been measured through statistical models and data gathered from a large number of projects, which enables drawing conclusions on a broad level. However, there is a gap when it comes to modeling the relationships between change orders and construction productivity on the project level. This paper proposes a novel framework using system dynamics (SD) modeling to complement current delay analysis techniques in quantifying the impacts of owners’ change orders on project productivity. SD was used as the modeling tool for its ability in capturing rippled impacts and model complex systems. The research methodology (1) identifies the exogenous and endogenous parameters, (2) develops a dynamic hypothesis that guides model formulation, (3) develops the SD model along with its mathematical formulation, (4) runs verification tests, and (5) calibrates the model and tests it on a real case study. The developed model captures the typical construction cycle and can quantify the impact of each change order separately. In addition, after calibrating the model to any project, multiple what-if scenarios are conducted to provide further insights on allocating delay responsibilities. This model can identify the delays caused due to change orders that can be helpful for both contractors and owners to resolve the corresponding claims prior to escalation to disputes.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".