RFID technology usage effect on enhancing warehouse internal processes in the 3pls providers: An empirical investigation in Jordanian manufacturing firms
Bibliographic record
Abstract
In the last two decades due to fast enhancing technology tools such as Radio frequency identification (RFID), which plays a crucial role in enhancing the ability of many firms to obtain a wide array of information about the site and features of any entity that can be physically tagged and wirelessly scanned within certain technical limitations. Furthermore, RFID technology can be adopted and implemented in a range of diverse functions, duties, and business-to-business systems (B2B) along the value chain, including services, intra-business logistics activities, as well as marketing and after-sales service applications. In total, 26 semi-structured interviews were involved in this study; several interviews were applied with senior, production, operations, facilitation, distribution, logistics, and warehouses managers, senior 3PLs provider managers, and supervisors and warehouses men from Alkasih and five 3PLs providers in Jordan country. Our forecast results for the third-party logistic providers' sample have shown an enormous growth that would reach 38.5% by 2030. Therefore, we have proposed RFID as a business process reengineering technique to mitigate the coming growth instead of expansion that would cost a huge amount of money due to land high costs. Moreover, we have noticed through our visits that the space utilization and warehouse capacity are very high and most of the warehouses are seeking expansion or employing additional operators to handle the current growth.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".