Evaluation of automation levels for construction change management
Bibliographic record
Abstract
Purpose Current processes to manage changes are subject to failure since they are heavily dependent on human discipline. The purpose of this paper is to evaluate and quantify the difference between levels of automation of change management processes and to provide input for determining the use of automation systems for change management. Design/methodology/approach Three generations of change management processes are defined to represent progressive practices used in major capital projects over the past few decades. Discrete event simulation was used to model these processes to capture their behavior and compare their performance according to time and compliance metrics. An oil and gas megaproject served to validate the findings of this modeling and analysis. Findings The results showed that automated processes can bring more compliance and real-time traceability, but not a significant time reduction in the change process. This contributes to the understanding of the impact of workflow-based automation on construction process performance. The validity of the conclusions are limited by the breadth of sectors studied and the inability to capture off-line time allocations of the personnel involved. Future research may build on the work presented here by studying additional processes such as requests for information, project change notices, requests for scaffolding, and interface management in various industry sectors. Originality/value A new approach for modeling and evaluating construction management process automation is contributed and the specific results of the paper indicate that automated workflow-based change management processes should be implemented in megaprojects.
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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 | 0.012 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| 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".