Customer-Centric Product Modeling for Rapid Product Identification in One-of-a-Kind Production
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
One-of-a-kind production (OKP) is a new manufacturing paradigm to produce customized products based on requirements of individual customers while maintaining the quality and efficiency of mass production. In this research, a customer-centric product modeling scheme is introduced to model OKP product families by incorporating the customer information. To develop this modeling scheme, data mining techniques, including fuzzy pattern clustering method, and hybrid attribute reduction method, are employed to achieve the knowledge from the historical data. Based on the achieved knowledge, the different patterns of OKP products are modeled by different sub-AND-OR trees trimmed from the original AND-OR tree. Since only partial product descriptions in a product family are used to identify the optimal custom product based on customer requirements, the efficiency of custom product identification process can be improved considerably. A case study to identify the optimal configuration and parameters of window products in an industrial company is used to demonstrate the effectiveness of the introduced approach.
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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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