Production control in precast fabrication: considering demand variability in production schedules
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
On-time delivery is a key factor in the business success of precast fabricators. The greatest obstacle to achieving this goal is demand variability. The objective of this research is to develop a plan that continuously improves production control systems for precast fabrication. This plan involves a lead time estimation model (LTEM) and schedule adjustment principles. The LTEM is established to estimate the impact of demand variability. In the model, previous jobs are analyzed as indicators of customer behavior. Using the captured behavior, fabrication lead time can be estimated for forthcoming projects. Two principles are proposed to adjust the production schedule according to the estimated lead times. Two adjustment principles are designed to reduce the impact of demand variability: (1) start fabrication later relative to the required delivery dates and (2) shift production milestones backward to the end of the production process. The effectiveness of the developed improvement plan including LTEM and the adjustment principles were validated using a real precast fabricator. The proposed approach is one of the first studies to use historical data to estimate the impacts of demand variability based on customer behavior.
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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 | 0.001 | 0.001 |
| 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 it