RFID and the EPC Network as Enablers of Mobile Business: A Case Study in the Retail Industry.
Bibliographic record
Abstract
This article provides some insights into Radio Frequency Identification (RFID) technology and the Electronic Product Code (EPC) Network and investigates their impacts as enablers of Mobile Business (M-Business)." M-business is the application infrastructure required to maintain business relationship and sell information, services, and commodities by means of the mobile devices" .Based on empirical data gathered from four tightly interrelated firms from three layers of a supply chain, several scenarios integrating RFID and the EPC Network have been tested in a pilot project and evaluated.In the context of warehousing activities in one specific retail supply chain, the results indicate that i) the business process approach seems quite appropriate to capture the real potential of RFID and the EPC Network; ii) they can improve the " shipping" , the " receiving" , the " put away" processes and allow " in transit visibility" , iii) they offer opportunities for process optimization through the automation or cancellation of non value added processes, and through the emergence of new " smart processes", iv) they foster a higher level of information synchronization, visibility and sharing between supply chain members; and v) they enable supply chain members to leverage their existing IS investments.The paper helps to improve our understanding of the real potential of RFID and the EPC Network for mobile business processes.
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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 | 0.001 | 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 | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".