MASS CUSTOMIZATION NEARSHORING PROGRAM FOR CLOTHING MANUFACTURERS
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
Mass customization offers considerable potential for increasing the notoriety of a brand, acquiring new markets and generating significant profits. But, before reaching or exceeding customer expectations, important steps must be taken. We discuss in this paper a nearshoring approach to more effectively implement a mass personalization program. Agility and flexibility remain essential to this concept because the demand is more and more volatile. Nearshoring is an interesting avenue for allowing manufacturers to adapt and transform their business practices and manufacturing strategies in a context of fast prototyping. To succeed, this approach must implement automated processes that also create new tradeoffs and challenges in terms of structure, operating model, sustainability and supply. The biggest challenge now is to procure raw materials in optimal quantities and on time. In addition to this challenge, available technologies are taking more and more space and allow a more intelligent mass personalization approach in terms of productivity and digitization via automation, thus making the supply chain more efficient, agile and customer-focused.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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