Data-Driven Simulation Model for Quality-Induced Rework Cost Estimation and Control Using Absorbing Markov Chains
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
This paper aims to develop a novel, data-driven simulation model to quantitatively assist decision support systems in quality-induced rework cost estimation and control for construction product fabrication. At the core of the model is a specialized absorbing Markov chain, which stochastically models the construction product fabrication process while considering quality-induced rework uncertainty. The model parameters are dynamically updated using real-time quality management and cost management information to achieve more accurate and reliable simulation outputs. Furthermore, two types of decision-support metrics are developed to support rework cost management processes, namely (1) rework cost estimation during the project planning phase, and (2) rework cost control during the project execution phase. An illustrative example is provided to demonstrate the functionalities of the model and the implementation of the decision-support metrics. Finally, the proposed approach is integrated into the previously developed simulation-based analytics framework and implemented by an industrial pipe fabrication company in Edmonton, Canada. The presented case study demonstrates the applicability and feasibility of the proposed approach to industrial pipe welding processes.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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