Bibliographic record
Abstract
Consumers are better educated and more demanding nowadays.They're no longer satisfied with standardized products that force them to compromise.It may be as small as a change in color or it could be a change in the functionality of the product.To meet customer expectations, clothing companies now have to manufacture or offer made-to-measure products.Brands that offer mass customization products are spreading through traditional businesses as well as the Web.In the clothing industry, some companies have successfully applied mass customization principles to their formerly standardized products.The mass customization, as a business strategy, is an evolving concept in apparel industry intended to provide customized products through agile and flexible processes in high volumes and at sensibly low costs.Companies need to realize the degree of customization valued by the customers and the extent of customization that can be offered competitively.It is a challenge to the apparel industry to maintain a profitable business and still satisfy the customer.However, mass customization is not generally well understood or implemented by companies, due to problems related to measurements, pattern adaptation, and inflexible manufacturing methods and lead times.The developments in new technologies, such as 3-dimensional body scanning and digital printing, have the potential of enabling manufacturers to utilize mass customization business strategies that would allow them to more effectively meet the needs of specific customers This research project offers many possibilities for innovation and could constitute a major opportunity for certain players in the clothing industry.If prime producers want to seize the moment, they will have to better understand what is feasible in clothing mass customization.This understanding will enable them to devise a measurement configurator strategy that will create opportunities for more effective market segmentation, and thus help develop a new competitive advantage.
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 | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".