Enhancing information flow in a retail supply chain using RFID and the EPC network
Bibliographic record
Abstract
RFID technology and the Electronic Product Code (EPC) network have attracted considerable interest from businesses and academics in recent years. The interest is even stronger in the retail industry where firms such as Best Buy, Wal-Mart, Tesco, Target and Metro AG are capitalizing on the potential of these technologies. Based on a field study conducted in a three-layer retail supply chain, this paper tests several scenarios integrating Radio Frequency Identification (RFID) technology and the EPC network and evaluates, in a laboratory setting, their potential as enablers of information flow within a retail supply chain. Using an open-loop adoption strategy, our preliminary results indicate that RFID technology and the EPC network (i) hold some potential that can be grasped through Business Process Management (BPM), (ii) enable the synchronization of information flow with product flow in a given supply chain, and thus, (iii) provide a better level of information integration between supply chain members. The results suggest that these new waves of information technology (IT) could in fact provide end-to-end information flow between supply chain members.
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 | 0.003 | 0.008 |
| 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 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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".