Factors Influencing Cycle Times in Offsite Construction
Bibliographic record
Abstract
In offsite construction, various factors contribute to variability in cycle times at workstations in production facilities, leading to imbalanced production lines.Understanding these factors is vital for implementing Heijunka, a fundamental lean principle that consists of levelling out the work schedule.This study presents a qualitative approach for identifying and understanding factors that influence variable cycle times at the workstation level.The application of the approach is demonstrated in reference to a semi-automated framing workstation in a panelised construction facility.A list of 36 potential influencing factors categorised into eight classes is first compiled based on observation of the process, a cross-functional diagram, and a review of relevant studies, and then discussed based on feedback solicited from personnel at the case framing station through a semi-structured interview.The approach, its application, and the results demonstrate the effect of expending effort on the identification and understanding of cycle time-influencing factors in improving the accuracy of cycle time analysis, thereby facilitating the implementation of Heijunka.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".