Bibliographic record
Abstract
This paper examines the manufacturing experience of clothing configuration within the mass customization approach. It is within this context that ‘mass individualism’ is examined; a phenomenon which in a climate of globalization can provide novel and environmentally sustainable consumer opportunities for major fashion manufacturers. It has become increasingly difficult for companies to offer interesting products and respond to the specific needs and desires of clients who have become much more savvy and aware of traditional methods of marketing. Thus, the industry must add real value to previously standardized products, in the form of customer specific services to better respond to consumer demand for authenticity and individuality. We find there some problem is related to the manufacturing aspects with measurements, adaptation of patterns and flexibility in methods and experience on the part of the manufacturers to properly use the configuration systems. It is in this respect that mass customization is examined, and several key implementation strategies are developed for manufacturers. From the start, mass customization needs to directly involve customers in the designing and manufacturing phases. Furthermore, this approach must provide opportunities to generate savings by reducing stocks and allowing for better integration of all actors in the supply chain. Mass customization offers possibilities to reach, or even surpass, customers’ expectations. Therefore, it needs to provide a knowledge base of consumers’ needs and preferences and thus create opportunities for market segmentation and market targeting. Fashion Apparel Industry and smart mass customization approach with digitization makes 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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".