Efficiency Optimization in Supply Chain Using RFID Technology
Bibliographic record
Abstract
Radio-frequency identification (RFID) has been a very crucial element when it comes to the Internet of Things (IoT). Its low cost, small form factor, and multiple items tracking make it suitable to use in Smart Supply Chain Management (SSCM). There are many limitations of traditional Supply Chain Management (SCM) such as decentralized control, slow process, a lot of manual bookkeeping, unpredictable supplies which can lead to uncertain prices and deliveries. This paper discusses how SSCM can help overcome these drawbacks, by investigating four different scenarios of SSCM where RFID plays a major role. The frameworks discussed here try to solve some parts of the SSCM and give a brief idea about how they can be incorporated into the whole process. In addition to examining different approaches, a combined framework is proposed at the end. This proposal reflects the best characteristics that can demonstrate the versatility of RFID for tracking products, inventory management, and security aspects in SSCM.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".