CONCURRENT MANUFACTURING SYSTEM OPTIMIZATION FOR TWO-PRODUCT OPERATION
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
Product/process design and optimization are typically aimed at a single product for a single customer. Such approach, however, often leads to underutilization of available production capacity. It is therefore reasonable for the manufacturer to make an effort to minimize available excess capacity to improve overall facility performance. Excess capacity can be allocated to the production of another product/process design, which can be also independently optimized. However, exploring possible synergies between the two products/processes may bring higher benefits. This paper presents a case where a manufacturing process (plastic blow moulding) was shared among two different products for two different customers, each with a different set of needs. These customer needs were mapped into core value-creating processes, recognizing both the differences in their requirements as well as the similarities in their expectations. Conflicting differences in complexity, production volumes and quality requirements were reconciled using QFD_based approach, and led to improved customer satisfaction and cost performance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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