Complexity analysis of an operation in demand-based manufacturing
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
Abstract In demand-based production systems with stochastic demand arrival times, operations often take place in random and long-time intervals. Therefore, traditional learning curve models may not be a good fit for estimating the operation time (OT) in such production environments. Moreover, the complexity of an operation is another influential factor in OT that is not quantified. In this article, human cognitive and complexity factors in demand-based production systems with stochastic demand arrival time are studied. Performing statistical analysis, a double segment learning curve is developed that is a best fit for OT with breakpoint feature. The breakpoint indicates the required number of orders received to reach the mastery level of performing a certain operation. A comparative analysis among existing and the double segment learning curve models is performed and the operation complexity measure is derived from the model. Keywords: human cognitivecomplexitylearning curvedemand-based production systemsstochastic demand times
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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