HP Brazil: Journey Towards Industrial Internet of Things Within Industry 4.0 Context
Bibliographic record
Abstract
This paper features a case study of Hewlett Packard Brazil’s deployment of radio frequency identification-enabled Exceler8 platform to support its product assembly using Flextronics in Sorocaba, Sao Paulo and distribution by DHL. The study also identifies the stage HP Brazil belongs to in its journey to being a full smart factory using the framework of Odwazny et al. (2018). The case study and content analysis methods are used in analyzing the concepts prescribed by the Industry 4.0, smart factory, and Industrial Internet of Things (IIOT) frameworks to HP Brazil’s RFID system. The Odwazny et al. (2018) framework identifies HP Brazil as being in the maturity stage, with selected attributes of the “smart factory” stage since its Exceler8 platform supports vertical integration in its assembly, distribution, and recycling sites. Hopefully, empirical work will be pursued with vigor in the future to gain an understanding of the actual conditions that support the successful deployment of both Industry 4.0 and IIOT initiatives. Firms interested in applying Industry 4.0 and IIOT concepts within their production environments would be guided by this study. Applying the German Industry 4.0 model, their Industry 4.0 initiative would seek to (1) enable collaboration between humans and machines; (2) produce customized products in small batches; (3) optimize high automation; and (4) deploy devices in flexible and eco-friendly production processes to meet customization requirements.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| 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".