Impact of Product Platform and Market Demand on Manufacturing System Performance and Production Cost
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
Due to the rapid change in customer demands and needs, manufacturers are increasingly shifting from mass productions to mass customizations. Product platform strategy, which is one of the enablers of mass customizations, has been implemented by many companies in order to offer a wide range of products that belong to a family. Recently, a new platform approach was developed where an optimal platform is formed for a product family and is customized for different variants by adding, removing, and/ or substituting platform components to form product variants as orders are received. In this paper, the effect of product platform design and customers' demand on the production cost is investigated using Discrete-Event Simulation (FlexSim). The product platform and the product platform scalability concepts are examined and compared. The findings of this research demonstrate that effective platform implementation has a direct effect on the overall production costs as well as improving customer satisfactions by offering the desired level of customized products.
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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 | 0.001 |
| 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