An AI-based Collaborative Textile Management Platform for Custom Order Processing and Workforce Optimization
Bibliographic record
Abstract
The textile machine remains undefeated on how to synchronize custom order, effectively deploy skilled personnel, clear flow of communication between customers, textile personnel and the artisans. To deal with these issues, the present paper describes a mobile application that has been boosted with generative AI to simplify the process of processing orders and managing the workforce more effectively. The system with Google's Gemini AI API can read natural language order information and smartly pair tasks with workers according to the performance and availability data and the skills. The app was created using Kotlin and Jetpack Compose on the Android platform with Firebase that provides real-time database functionality, authentication, cloud storage, and immediate notifications. The solution realized 67 - 80 percentage decrease in processing time, 35 percentage increase in the utilization of workers, and customer satisfaction percentage improved by 68 to 91 percentage outcome. All in all, the Firebase supported structure facilitates scalable, coherent, and dependable textile manufacturing processes that operate on an intelligent basis through AI.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".